Regardless of your testing experience or the effectiveness of your automation infrastructure, you should use a robust test design to initiate automation. The lack of strong test design can force testing teams to face a wide range of issues which generate incomplete, inefficient, and difficult-to-maintain tests. In order to make sure the cost efficiency, quality, and delivery are not affected, it is necessary to familiarize with those indicators which represent the performance of tests. To begin with, consider the following automation anti-patterns to improve testing.

Longer Length of Sequence

Often, tests are created with small steps for long sequences, thus their management and maintenance is hard. For instance, while an application which is currently being tested undergoes changes, it is considerably complex to work around these modifications with other tests.

Instead of using a bottom-up approach first, generate a high-level design. In accordance with the respective method, such a design can consist of features like scopes and definitions of test products which are included in the main objective and test objective for different tests. For instance, a product could consist of test cases which test the calculation process of home loan mortgage premiums.

Business-Tests

When testers focus too much on interaction tests, it is possible that they may design weak tests which do not factor major business-level concerns like the interaction of application responses for unusual circumstances.

Testers should emphasize on the use of businesses tests which represent rules, objects, and processes of business alongside the interaction tests. For instance, with a business test, a user can log in, type a few orders, and view the financial information with the help of high-level activities which mask the details of the interaction.

In interaction tests, a user can type name/password combination and assess whether or not the button of submission (submit) is enabled or not—such a test can occur in any business environment type.

Blurred Lines

While it is important to run interaction and business tests, however, keep in mind that they should be run separately. For instance, the rules and lifecycles of business objects along with their processes and calculations must not be combined with interaction test details like confirming the presence of submit button or determining whether or not a login message is displayed after the login process. Such a scenario can make maintenance hard due to the mix. For instance, if the welcome message is generated in a new application version, all the associated tests have to be checked and maintained.

A modular and high-quality test can help testers with the correct vision about how these blurred lines should be avoided to make sure the maintainability and manageability is strong. Test modules are known to carry a well-defined scope with test modules. They prevent checks which are not suitable for their scope and mask comprehensive steps for the UI interactions.

Life Cycle Tests

Most of the global applications work with business objects like products, invoices, orders, and customers. The application lifecycles of such objects are updated, retrieve, create, and delete—known as CRUD. The major issue is that the tests for these lifecycles are hard to find, incomplete, and scattered. Hence, there can be real vulnerabilities in the tests’ scope especially in the case of business objects, particularly when the business objects are ever-changing. For a car rental, while one can try several vans and cars, however, the coverage is lesser for both the buses and motorcycles.

It is easy to design life cycle tests. You can initiate by choosing business objects along with their operations. Such a process can also consist of variations like updating or canceling an order. It is important to remember that life cycle tests are similar to business tests, instead of the interaction tests.

Poorly Developed Tests

Factors like pressure, time, and others can culminate in the creation of shallow test cases which are not good enough to properly test the application. As a result, quality suffers, like missed situations which are not responded. Doing this can also affect the test maintenance, making it more expensive.

More importantly, it is necessary to sync both the testing and test automation for the entire Scrum sprint. These tests and their automation require a greater degree of cooperation which is tougher in case they are still running after the completion of the sprint.

When improved automation architecture and test design are not good enough in keeping up with the velocity, then you can think about the outsourcing of a number of tasks so the testers are able to match the speed.

You have to think in terms of a professional tester—as an individual who has a knack of breaking things. For instance, if different testing methods like error guessing decision tables are used then they can assist to pinpoint those situations which require immediate assistance with the test cases. On a similar note, equivalence partitioning and state-transition diagrams can help to think about different design to test the cases.

Scope

Lack of scope in tests is quite a common problem like when an entry dialog has to be tested by a test or with a group of financial transactions. It is easy to find the tests and also update in case of changes in the application while duplication is also possible.

Duplicate Checks

Testers usually use to assess steps by using an unexpected output for all the steps; this is also encouraged by different management tools. What this indicates is that separate tests can be used for the same check. For instance, the previous test determined whether or not the welcome screen is generated after the login.

Begin with a test design on which the testers focused heavily. You have to ensure that all the modules in the test have well-differentiated and clear scopes. Hence, during the development of such tests, you have to avoid checking after each step. Add checks according to the scope.

Amazon S3 is used by many companies for storage purposes. Due to its use as object storage, it offers flexibility with a slew of data types including small objects to massive datasets. Thus, S3 has carved out its niche as a great service which can store a broad scope of data types via a resilient and available environment. As your objects of S3 have to be accessed and read by other AWS services, applications, and end users, do you believe that they are optimized to offer the best possible performance? Here is how you can optimize your S3. Follow these tips to improve your performance with Amazon S3.

Perform TCP Window Scaling

TCP window scaling facilitates developers to improve the network throughput performance via the modification of the header in the TCP packet that uses a window scale. This helps in sending data with a single segment—more than the traditional 64 KB. It is important to note that such practice is exclusive to S3; it functions along with the protocol level. As a result, by using this protocol you can execute window scaling for your client while establishing a connection with a server.

When a connection is established between a destination and source by the TCP, then the next thing is a 3-way handshake that starts up from the source. This means that with the S3 view, it is possible that the client might be required to upload an object to the S3. However, prior to this, you must create a connection with the S3 servers.

A TCP packet will be sent by the client along with a defined window scale of TCP in the header—such a request is also referred to as SYN request—the first part of the 3-way handshake. When the S3 gets this request, it uses an SYN/ACK message to send a response to the client while using the same window scale factor—this forms the second part of the 3-way handshake—maintaining the relevant window scale factor. The third and final part consists of an ACK message which is sent to the S3 server—it serves as the response’s acknowledgment. A connection is generated after this 3-way handshake ends where the S3 and client can finally exchange data.

In order to send more data, you can widen the window size via a scale factor. This helps in sending voluminous amount s of data via a single segment whereas your speed is also quickened.

Use Selective Acknowledgment

At times while using TCP, it is not uncommon for packets to get lost. To figure out which of the packets went missing is hard within a TCP window. Consequently, at times it is possible to resend all of these packets. However, then the receiver may have received some of the packets so this is an ineffective strategy.

Instead, you can use TCP SACK (selective acknowledgment) for improving performance where the sender receives notifications about which were the failed packets for a window. As a result, the sender can then easily only resend the failed packets.

However, it is necessary that the source client or the sender initiates the SACK when a connection is being established amidst the handshake’s SYN phase. Such an option is also called as SACK-permitted. For using and implementing SACK, you can visit this link.

Setting Up S3 Request Rates

Alongside TCP SACK and TCP Scaling, S3 is quite nicely optimized to address a high request throughput. A year back, in 2018, AWS introduced a new change for these request rates. Before the announcement, it was recommended that the prefixes can be randomized within the bucket to help with performance optimization—there is no more need for it. Now exponential growth of request rate performance can be achieved when more than one prefixes within the bucket are used.

Now, developers are getting a 3,500 PUT/POST/DELETE request for each second while they are also achieving 5,500 GET requests. A single prefix is a reason behind such limitations. However, keep in mind that there is no limit for prefixes which are to be used in an S3 bucket. What this means is that if you use 2 prefixes then you can get 110,000 GET requests and 70,000 PUT/POST/DELETE for each passing second for the same bucket.

There is no hierarchical-based structure in the folders of S3; it follows a flat structure for storage. This means that all you need is a bucket while all the objects are saved in a flat space of the bucket. You can generate folders and store objects in it—without depending on a hierarchical system. The prefixes of the object are responsible to make them unique. For instance, in case you have the following objects in a bucket:

• Design/Meeting.ppt
• Objective/Plan.pdf
• jpg

Here, the ‘Design’ folder serves as a prefix for identifying the object—such a pathname is also referred to as the object key. The ‘Objective’ folder is also an object’s prefix while the ‘Will.jpg’ is without any prefix.

Amazon Cloud Front

One more strategy for optimization is to integrate Amazon CloudFront with Amazon S3. This is a wise strategy when the request of the S3 data is a GET request. CloudFront is a content delivery network which increases the pace of the distribution of dynamic and static content across a global network comprising of edge locations.

Typically, after a user sends a request from S3 in the form of GET request then the S3 service is used to route it while the relevant servers return the content. In case you use CloudFront with S3 then it can also perform caching for those objects which are requested commonly. Hence, the user’s GET request is directed to the nearest edge location that offers low latency for returning the cached object and providing the best performance. It also decreases the AWS costs when the number of GET requests in the buckets is shortened.

How do you run code reviews? Code reviews are vital and enhance the quality of code. They are responsible for the stability and reliability of the code. Moreover, they build and foster relationship among team members. Following are some of the tips for code reviews.

1.   Have a Basic Understanding of What to Search in the Code

To begin with, you should have a basic idea about what you are looking for. Ideally, you should look for major aspects like the following.

• What structure has the programmer followed?
• How is the logic building so far?
• What style has been used?
• How is the code performing?
• How are the test results?
• How readable is the code?
• Does it look maintainable?
• Is it ticking up all the boxes for the functionality?

You can also perform static analysis or other automated checks to evaluate the logic and structure of your code. However, some things are best reviewed from a pure manual check like functionality and design.

Moreover, you also have to consider the following questions for the code.

• Are you able to understand how the code works and what does it do?
• Is the code following the client requirement?
• Are all modules and functions running as expected?

2.   Take 60-90 Minutes for a Review

You should avoid spending too much time for reviewing a codebase in a single sitting. This is because after a 60-minute time interval, a code reviewer naturally begins sensing tiredness and does not have the same physical and mental strength to pick out defects from the code. Such state is supported by proofs from other studies as well. It is a common fact that whenever human beings commit themselves to an activity which needs special attention, their performance begins to experience a dip after 60 minutes. By following this time period, a reviewer can at best review around 300 to 600 lines of code.

3.   Assess 400 Lines of Code at Max

According to a code review by Cisco, for best results, developers should conduct a code review which extends from 200 to 400 LOC (lines of code) at a time. After this time period, the capability to identify bugs begins to wither way. Considering an average review stretches up to 1.5 hours, you can get a yield in between 70-90%. This means that if there were 10 faults in the code, then you maybe successful to find at least 9 of them.

4.   Make Sure Authors Annotate Source Code

It is possible that authors of the code can remove almost all of the flaws from the code before a review is required. This can be done if it is mandatory for the developers to re-check their code, thus making reviews end faster while the code quality remains unaffected as well.

Before the review, authors can use annotation in their code. With annotations the reviewer can them go through all the modifications, see what to look first and assess the methods and reasons for all the changes in the code. Thus, these notes are not merely code comments but they serve as a guide to the reviewers.

Since authors have to re-assess and explain their modifications while annotating the code, therefore it can help to show different flaws in the code prior to the beginning of the review. As a result, the review achieves a greater level of efficiency.

5.   Setup Quantifiable Metrics

In the beginning, you have to come up with the goals for the code review and brainstorm how to assess the effectiveness of the code. After certain goals are defined, it can help to reflect better whether or not the peer review is providing the required results.

You can use external metrics like “cut upon the defects from development by 50%” or “decrease support calls by 15%”. Therefore, you can get a better picture of how well your code is performing from an external outlook. Moreover, a quantifiable measurement is wiser rather than having an unclear objective to “resolve more bugs”.

It must be noted that the results of the external metrics are not realized too early. For instance, there will be no changes to the support calls till the release of the new version and users can use the software. Therefore, you should also judge internal process metrics for getting the number of defects, study the points which cause issues, and get an idea about how much time is being spent by your developer on reviewing their code. Some of the internal code review metrics are the following.

• Inspection rate: It is measured in kLOC (thousands of lines of code) per work hour and represents the time required for reviewing the code.
• Defect rate: It is measured in number of defects discovered for each hour. It represents the process to discover defects
• Defect density: It is measured in number of defects for each kLOC. It represents the number of defects which are discovered in a code.

6.   Create Checklists

Checklists are vital for the reviews due to the fact that help the reviewer to keep tasks in mind. They are a good option to evaluate those components which might be forgotten by you. They are not only effective for reviewers but they can also aid the developers.

One of the tough defects to highlight is omission as it is obviously tough to identify a piece of code which was never added. A checklist is one of the best ways to address this issue. With checklist both the reviewers and author can verify that the errors have been resolved, the arguments in the function are tested to run with invalid values, and the required unit tests are generated.

Another good idea is to use a personal checklist. Each developer often repeated the same errors in their code. If the creation of personal checklist is enforced then it can help the reviewers.

With each evolution, garbage collectors get new improvements and advancements. However, garbage collections in Java face a common issue: unpredictable and redundant object allocations. Consider the following tips to improve your garbage collection in Java.

1.   Estimate Capacity of the Collections

All types of Java collections including extended and custom implementations take advantage of underlying object-based or primitive arrays. Due to the size immutability of arrays after allocation, when items are added to a collection, a new array can force the old array to get dropped.

Many implementations of collections attempt optimization of the re-allocation process and try to limit it an amortized restriction, whether or not the expected collection size is not given. To get the best results, give the collection its expected size during creation.

For instance, consider the following code.

public static List reverse(List < ? extends T > list) {

List answer = new ArrayList();

for (int x = list.size() – 1; x > = 0; x–) {

answer.add(list.get(x));

}

return answer;

}

In this method, a new array is allocated after which another list’s items fill it via a reverse order. In this code, optimization can be applied to the specific line which performs addition of items in the new list. Whenever an item is added, the list has to ensure that there are enough available slots in the underlying array so it can store the incoming item. If there is a free slot, then it can easily store it. Or else, it has to perform allocation of a new underlying array, move the content of the old array to the newer one, and then perform the addition of the new item. As a result, multiple arrays are allocated which are ultimately collected by the garbage collection.

In order to avoid these redundant and inefficient allocations, you can “inform” the array about the number of items it can store while creating it.

public static List reverse(List < ? extends T > list) {

List answer = new ArrayList(list.size());

for (int x = list.size() – 1; x > = 0; x–) {

answer.add(list.get(x));

}

return answer;

}

As a result, the first allocation from the constructor of the ArrayList is big enough to store the items of the list.size(), thus there is no need for reallocation of memory in the middle of an iteration.

2.   Compute Streams with a Direct Approach

While data is being processed like it is downloading from the network or read from the file, then it not uncommon to view the following instances.

byte[] fData = readFileToByteArray(new File(“abc.txt”));

As a result, the byte array a JSON object, XML document, or Protocol Buffer message can then be used for parsing. While working with bigger files or files having uncertain size, there are possibilities of exposure to OutOfMemoryErrors if buffer allocation of the complete file is not performed by the Java Virtual Machine.

In case, we assume that the data size can be managed, the above-mentioned pattern can lead on to generate considerable overhead for garbage collection because a huge blob is allocated on the heap for holding the data of the file.

There are better solutions to tackle this. One of them is the use of the relevant InputStream and use the parser directly before converting it into a byte array. Usually, all crucial libraries are known to have API exposure for direct streams parsing. For instance, consider the following code.

FileInputStream fstream =new FileInputStream(fileName);

MyProtoBufMessage message = MyProtoBufMessage.parseFrom(fstream);

3.    Make Use of Immutable Objects

Immutability brings a lot of benefits to the table. Perhaps, its biggest one is on the garbage collection. An object in which you cannot alter the fields after the construction of the project is known as an immutable object. For instance,

public class twoObjects {

private final Object a;

private final Object b;

public twoObjects(Object a, Object b) {

this.a = a;

this.b = b;

}

public Object getA() {

return a;

}

public Object getB() {

return b;

}

}

The instantiation of the above-mentioned class provides an immutable object in which all the fields are set as ‘final’ and cannot be altered.

Immutability means that objects which are referenced via an immutable container are generated prior to the container’s construction. In terms of garbage collection, the container and the reference have the same age.

Therefore, while working with cycles of garbage collections for young generations, the garbage collection can ignore the older generations’ immutable objects as they are unable to reference.

When there are lesser objects for scanning, it requires less memory and also saves up on garbage collection cycles, resulting in improved throughput.

4.   Leverage Specialized Primitive Collections

The standard collection library of Java is generic and convenient. It assists in using semi-static type binding in collections. This can be advantageous if, for instance, you have to use a string set or work with a map for strings lists.

The actual issue emerges when developers have to store a list contains “ints” or a map containing type double values. As it is not possible to use primitives with generic types, another option is the use of boxed type.

Such an approach consumes a lot of space because an Integer is an object having a 12-byte header and a 4-byte int field. In total each Integer item amounts to 16 bytes—four times the size of the similar primitive int data type. There is another issue that the object instances of these integers have to be assessed for garbage collection.

To resolve this issue, you can use the Trove collection library. It provides a few generics over specialized primitive collections that are memory efficient. For instance, rather than using the Map<Integer, Double>, you can use the TIntDoubleMap.

TIntDoubleMap mp = new TIntDoubleHashMap();

mp.put(6, 8.0);

mp.put(-2, 8.555);

The underlying implementation from Trove works with primitive arrays, hence no boxing occurs during the manipulation of collections and objects are not hold in the primitives’ place.

Are you using a serverless architecture?

As it was seen in 2018, more and more businesses are coming to serverless computing, especially to Kubernetes. Many of them have even started reaping the benefits of their efforts. Still, the serverless era has just started. In 2019, the following trends are going to change how the organizations create and deploy software.

Adoption in the Enterprise Software

In 2018, serverless computing and FaaS (function as a service) began to become popular among organizations. By the end of 2019, these technologies will go onto the next level and are poised to get adopted on a wider scale, especially for the enterprise application sector. As container-based applications—using the cloud-native architecture—are spreading with a rapid pace, it has served as a catalyst for the burgeoning adoption of serverless computing.

Today, software delivery and deployment has evolved to a great extent. The robustness and range of containers increased the cloud-native applications to unprecedented heights for legacy applications and Greenfield. As a result, business scenarios in which earlier there was not much progress for cloud-native modernization—like data in transit, edge devices, and stateful applications—can be converted into cloud-native. While container-based and cloud-native systems are beginning to experience a rise, software developers are using serverless functions for performing a wide range of tasks across various types of applications. Teams will now deliver microservices transition on a large scale—some may use FaaS to decrease the application’s complexity.

Workflows and similar high-end functionalities in FaaS are expected to provide convenience in creating complicated serverless systems via a composable and modular approach.

Kubernetes as the Defacto Standard

There are fewer better infrastructures than Kubernetes for working with serverless computing. By 2018, Kubernetes was widely used with container orchestration with different cloud providers. As a result, it is the leading cloud-native systems’ enabler and is on its way to becoming the defacto operating system. Ubiquity assists Kubernetes to be transformed into the default standard which can be used for powering serverless systems.

Kubernetes helps to easily create and run those applications in serverless architecture which can utilize cluster management, scaling, scheduler, networking, service discovery, and other powerful built-in features of Kubernetes. Serverless runtime needs these features with interoperability and portability for any type of environment.

Due to Kubernetes position as the standard for the serverless infrastructure, organizations can make use of their own multi-cloud environments and data centers for running serverless applications, rather than being restricted with a single cloud service and face excessive cloud expenses. When organizations will take advantage from cost savings, speed, and enhanced serverless functionality with their own data centers while at the same t time in different environments, they can port serverless applications, then the end result is impactful—the serverless adoption in the enterprise gets a massive boost. It not only becomes a strong architecture for the acceleration of new applications’ development, but it is also a worthy pattern which can help to modernize legacy applications and brownfield.

In cloud-native architecture, the increased refinement of Kubernetes deployments means that you can foresee the Kubernetes-based FaaS frameworks to be integrated with chaos engineering and service meshes concepts. To put it simply, if we consider the next Kubernetes as the next Linux, then serverless can be considered as the modern Java Virtual Machine.

Serverless with the Stateful Applications

Stateless applications with short life spans are used mainly by serverless applications. However, you can now also expect a more rapid serverless adoption having stateful scenarios—which are powered by improvements in advancements in both Kubernetes-based storage systems and serverless systems.

These workloads can consist of validation and test of machine learning applications and models which execute high-end credit checks. Workflows are going to be a major serverless consideration which can make sure that all the use cases only are not only executed properly but can also scale according to requirements.

Serverless Tooling Enters a New Age

Lack of tooling has been an issue for FaaS and serverless computing for a long time. This encompasses the operational and developer team ecosystem support and tooling. In 2019, the major FaaS projects are expected to take a more assembly line tooling view while using the enhanced experience of the developer, and smooth pipelining and live-reload.

In 2019, GitOps will achieve newfound recognition as a FaaS development paradigm. Hence, now all the artifacts can use Git for versioning and roll forwards or rollbacks can resolve the common versioning issues.

Cost Is Going to Raise Eyebrows

As the graph of last years suggests more and more enterprises are going to use serverless architectures to power mission-critical and large-scale applications; thus this also stretches the expenses and costs of serverless computing for public clouds. Consequently, cloud-lock is also expected to become a significant concern.

By the end of 2019, organizations will manage cloud expenses and provide portability and interoperability via standardization on those serverless systems that are open source—similar to Kubernetes. They can also make use of the strategies for utilizing the most efficient cloud provider while avoiding reliance on re-coding while at the same time serverless is being run via private clouds. This can have a major effect and help to enhance resource utilization and improve the current infrastructure. The impact will also touch the investment which was put in on-premise data centers for delivering the identical experience of developers and cloud operations—like how it works in the public cloud.

Before the start of 2020, it can be expected that these 2019 predictions will serve as the foundation of adoption of serverless architecture. All the single applications will be modeled in terms of services, triggers will be used for execution, and they run till the service request is satisfied. As a result, the model can simply how to code software along with ensuring that quick speed is also possible while keeping in mind both the expenses and security of the software.

An application programming interface (API) allows developers to establish a connection with services. These services can be cloud services and assist with updating a DB, storing data, pushing data in a queue, moving data, and managing other tasks.

APIs play an important role in cloud computing. Different cloud providers depend on various API types. Consumers debate on portability and vendor lock-in issues. AWS leads the market, holding its position as the de facto standard.

A cloud API is a special category of API which facilitates developers to develop services and applications which are needed for provisioning software, hardware, and platforms. It acts as an interface or gateway which may be indirectly or directly responsible for the cloud infrastructure.

Risk of Insecure APIs

Cloud APIs help to simplify many types of cloud computing processes and automate various complex business functionalities like configuration of a wide range of cloud instances.

However, these APIs need a thorough inspection of cloud customers and cloud providers. If a cloud API has a security loophole then it can create a number of risks pertaining to availability, accountability, integrity, and confidentiality. While the providers of cloud services have to be vigilant around securing their APIs, their mileage can differ. Hence, it is necessary to learn how you can assess the cloud API security. In order to evaluate cloud APIs, this report discusses several areas of concerns like what cyber threats do they exactly represent? How do the risks operate? How is it possible for companies to evaluate and protect these cloud APIs? Some of these areas where the customers have to be vigilant are as follows.

Transport Security

Generally, APIs are provided through a wide range of different channels. Those APIs which hold and store private and sensitive data require a greater level of protection via a secure channel like IPSec or SSL/TLS. Designing the tunnels of IPSec for a customer and CSP (cloud service provider) is often complex and resource-intensive; therefore many eventually select SSL/TLS. As a consequence, a can of worms is opened in the form of multiple potential issues. These issues include the management and production of legitimate certificates from an external or internal CA (certificate authority), problems with end-to-end protection, platform service, and their configuration conundrums, and the integration of software.

Authorization and Authentication

Most of the clouds APIs emphasize on authorization and authentication—hence they are major considerations for a lot of clients. One can ask the cloud service provider questions like the following.

• How easily can they handle the attributes of two-factor authentication?
• Are the APIs capable enough to encrypt the combination of usernames and passwords?
• Is it possible to create and maintain policies for fine-grained authorization?
• What is the connectivity for internal IMS (identity management systems) and their attributes along with those which are offered by the cloud service providers’ APIs?

Code practices

If your cloud API processes XML or JSON messages or receive user and application input, then it is integral to test them properly so they can be evaluated for routine injection flows, schema validation, input and output encoding, and CSRF (cross-site request forgery) attacks.

Protection of Messages

Apart from making sure that the standard coding practices are used, other cloud APIs factors can include encryption, encoding, integrity validation, and the message structure.

Securing the Cloud APIs

After a company analyzes the concerns which the insecure cloud APIs can cause, they have to think about what practices and solutions they can implement in order to safeguard them. Firstly, you have to assess the cloud service provider’s API security; request them to provide the APIs documentation which can include reports and assessment results of the existing applications that can help to highlight the best audit results and best practices. For instance, take the examples of Dasein Cloud API which offers a comprehensive case study pertaining to the cloud APIs with extensive and open documentation.

Additionally, other than documentation, clients can request their cloud service providers so they can operate vulnerability assignments and penetration tests for the cloud APIs. Sometimes, CSPs seek the service of other third-party providers to proceed with these tests. The final outcome is then shown to the clients with an NDA; this helps the client to assess the current cybersecurity of the APIs.

The APIs of the web services must be secured for the OWASP’s—Open Web Application Security Project—10 common considerations for security loopholes via the application and network controls. Additionally, they should be also protected for QA testing and development practices.

Several cloud service providers provide an authentication and access mechanism via encryptions keys for their customers to benefit from the APIs. It is necessary to safeguard these keys—for both the CSP and customers. There should be clear-cut policies to manage the production, storage, dissemination, and disposal of these encryption keys—they must be stored with the help of a hardware security component or a protected and encrypted file store can be used for them. Do not use the configuration files or similar scripts for key embedding. On a similar note, if keys are embedded directly into the code, then developers will have a tough time with updates.

Search for cloud security providers like Microsoft Azure and Amazon. They offer symmetric keys and authentication codes that run on a hash-based mechanism in order to provide integrity and refrain from spreading shared information between untrusted networks. If a third party intends to work with a cloud service provider’s API then they must abide by these considerations and view API security and protection keys as a major priority.

Internet of Things still remains a relatively new technology for companies around the world. It is providing businesses with a lucrative opportunity to thrive and prosper in the future of “Things”. However, implementing the internet of things is easier said than done. Their deployments are complex. This means that you not only require the IT team but also need the business units and operations team to implement your IoT solutions. Some of the IoT challenges and solutions are listed below.

Cost

The expenses and costs incurred in migrating from traditional architecture to an IoT are significantly high. Companies should refrain from proceeding with this leap initially via a one-off stream. While there is nothing wrong with the overall vision to adopt IoT, it is difficult for the management to ignore costs.

To handle and mitigate these expenses, there are a number of projects with IoT implementations. They are quite cost-friendly and have defined goals, also called as ‘bite-sized’. Start your adoption slowly by utilizing pilot technologies and spend money via a series of phases. To manage additional costs, give a thought to SaaS (software as a service) to get on-premise and robust installations.

Moreover, evaluate those IoT projects which provide good value for money and go through the business cases documentation.

Security

Sending and receiving data on the Web is always one of the riskiest activities faced by the IT team. A major reason behind this is the latest onslaught of hacking which has engulfed the modern-day world as cybercriminals are attacking governments and businesses left and right. However, in IoT, the issue is more complex; not only have you to facilitate online data communication but also connect a lot of devices— creating more endpoints for the cybercriminals to attack. While assessing the security of your IoT application, consider the following.

Data at Rest

When databases and software store data through the cloud or on-premises architecture, such data is commonly known as “at rest”. To protect this data, companies rely on perimeter-based defense solutions such as firewalls and anti-virus. However, cybercriminals are hard to deter—for them this data offers lucrative opportunities in the form of several crimes like identity theft. Cybersecurity experts recommend that this issue can be resolved with the use of encryption strategies for both the hardware and software in order to ensure that the data is saved from any illegal access.

Data in Use

When an IoT application or a gateway uses data, then its access is often available for different users and devices and thus is referred to as data in use. Security analysts claim that data in use is the toughest to safeguard. When a security solution is designed for this type of data, mainly the security considerations assess the authentication mechanisms and focus on how to secure them so only the authorized user can access them.

Data in Flight

The data which is currently being moved is primarily referred to as data in flight. To protect this data, communication protocols are planned and designed by using the latest and most effective algorithms of cryptography; they allow blocking the cybercriminals from decoding the data in flight. You can use a wide range of internet of things equipment which provides an extensive list of security protocols—many of them are enabled by default. At the minimum, you should ensure that your IoT devices which are linked with the mobile apps or remote gateways utilize HTTPS, TLS, SFTP, DNS security extensions, and similar protocols for encryption.

Technology Infrastructure

Often, clients have IoT equipment which is directly linked to the SCADA (supervisory control and data acquisition). This means that they are ultimately responsible for the production of data that can help with analytics and insights. In case there is a lack of power monitoring equipment, the SCADA network can provide the relevant system to connect the newly-added instrumentation.

Secure networks often rely on one-way outbound-only communication. SCADA can facilitate the management of the equipment’s control signals.

You can use two methods for the protection of data for the APM data transmission. First things first, you can link the APM and the SCADA’s historian. The historian is a component which is required for the storage of the instruments’ readings and control actions and it resides in a demilitarized zone where it is required for the access of applications through the Internet. These applications only have access to look into the historian’s stored data.

You should know that SCADA only permits the writes to the DB. To do this, the historian sends the SCADA an outbound signal which is based on an interval. Many EAM (enterprise asset management) systems use the SCADA’s historian data to populate dashboards.

Another handy solution is to adopt a cellular service or any other independent infrastructure. This can help you to power your data communication without any dependence on a SCADA connection. The idea for uploading data—which is cellular in nature—is a wise one in facilities that have issues with the networking infrastructure. In such a setup, users can connect a cellular gateway device with a minimum of at least five devices while a 120-V outlet powers it. Today, pre-configured cellular equipment is offered by different companies, helping businesses to easily connect and deploy their IoT solutions within a few days.

Communication Infrastructure

The idea to use a cellular gateway for connecting internet of things equipment is smart. However, users who are close remote areas can struggle with reception. For such cases, you need to invest an enormous amount of money to develop the required infrastructure. LTE-M and LTE-NB may use the existing cellular towers but even then you can get a broader coverage from them.

This means that even while using the 4G-LTE data, if a user is unable to work with a good signal for voice calling, then they have a formidable option in the form on the LTE-M.

Nowadays companies are aggressively incorporating internet of things into their existing IT infrastructures. However, planning to shift to IoT is one thing; an effective implementation is an entirely different ball game. Several considerations are required to develop IoT application. For starters, you need a robust and dependable architecture which can help with the implementation. Organizations have adopted a wide range of architectures along the years but the four layers and seven layer architecture are especially notable. Here is how they work.

Four Layer Architecture

The four-layer architecture is composed of the following four layers.

1.    Actuators and Sensors

Sensors and actuators are the components of IoT devices. Sensors collect and gather all the necessary data from an environment or object. It is then processed and becomes meaningful for the IoT ecosystem. Actuators are those components of IoT devices which can modify an object’s physical state. In standard four-layer architecture, data is computed at all the layers including in sensors and actuators. This processing depends upon the processing limits of the IoT equipment.

2.    The Internet Gateway

All the data which is collected and stored by sensors fall into the analog data category. Thus, a mechanism is required which can alter it and translate it into the relevant digital format. For this purpose, there are data acquisition systems in the second layer. They connect with the sensor’s network, perform aggregation on the output, and complete the analog-to-digital conversion. Then this result goes to the gateway which is responsible for routing it around Wi-Fi, wired LANs, and the Internet.

For example, suppose there is a pump which is integrated with many sensors and actuators; they send data to an IoT component so it can aggregate it and convert it into digital format. Subsequently, a gateway can process it and perform the routing for the next layers.

Preprocessing plays an important role in this stage. The sensor data produce voluminous amounts of data within no time. This data includes vibration, motion, voltage, and similar real-world physical values to create datasets with ever-changing data.

3.    Edge IoT

After the conversion of data into a digital format, it cannot be passed onto data centers before you can perform additional computation on it. At this phase, edge IT systems are used for the execution of the additional analysis. Usually, remote locations are selected to install them—mostly those areas which are the closest to the sensors.

Data in IoT systems require heavy consumption of resources such as the network bandwidth in the data centers. Hence, edge systems are utilized for analytics which decreases the reliance on computing resources. Visualization tools are often used in this phase to monitor data with graphs, maps, and dashboards.

4.    Cloud

If there is no need for immediate feedback and data must go under more strenuous processing, then a physical data center or cloud-based system routes the data. They have powerful systems that analyze, supervise, and store data while ensuring maximum security.

It should be noted that the output comes after a considerable period of time; however, you do get a highly comprehensive analysis of your IoT data. Moreover, you can integrate other data sources with actuators and sensors’ data for getting useful insights. Whether on-premises, cloud, or a hybrid system is used, there is no change in the processing basics.

Seven Layer Architecture

Following are the layers of the seven-layer architecture.

1.    Physical Devices

Physical equipment like controllers falls into the first layer of the seven layer architecture. The “things” in “internet of things” is referred to these physical devices as they are responsible for sending and receiving data. For example, the sensor data or the device status description is associated with this type of data. A local controller can compute this data and use NFC for transmission.

2.    Connectivity

Following tasks are associated with the second layer.

• It connects with the devices of the first layer.
• It implements the protocols according to the compatibility of various devices.
• It helps in the translation of protocols.
• It provides assistance in analytics related to the network.

3.    Edge Computing

Edge computing is used for data formatting which makes sure that the succeeding layer can make sense of the data sets. To do this, it performs data filtering, cleaning, and aggregation. Other tasks include the following.

• It is used for the evaluation so data can be validated and computed by the next layer.
• It assists in the data reformat to ease up high-level and complex processing.
• It provides assistance in decoding and expanding.
• It provides assistance in the data compression, thereby decreasing the traffic workload on the network.
• It creates event alerts.

4.    Data Accumulation

Sensor data is ever changing. Hence, it is the fourth layer which is responsible for the required conversion. The layer ensures that data is maintained in such a state that other components and module of IoT can easily access it. When data filtering is applied in this phase, a significant part of data is eliminated.

5.    Data Abstraction

In this layer, the relevant data is processed for adhering to specific properties pertaining to the stored data. Afterward, data is provided to the application layer for further processing. The primary purpose of the data abstraction layer is data rendering keeping in mind its storage and using an approach through which IoT developers are easily able to code applications.

6.    Application

The purpose of the application layer is data processing so all the IoT modules can access data. Software and hardware layer are linked with this later. Data interpretation is carried out for generating reports, hence business intelligence comprises a major part of this layer.

7.    Collaboration

In this layer, response or action are offered to provide assistance for the given data. For instance, an action may be the actuator of an electromechanical device following a controller’s trigger.

Development with traditional software architectures and IoT architectures is quite different, particularly when it comes to the design patterns. To put it clearly, the detail and abstraction levels in IoT design patterns vary. Therefore, for creating high-quality IoT systems, it is important to assess various layers of design patterns in the internet of things.

Design patterns are used to add robustness and allow abstraction to create reusable systems. Keep in mind that design involves working with technical information and creativity along with scientific principles for a machine, structure, or system to execute pre-defined functions, enabling maximum efficiency and economy.

IoT helps to integrate the design patterns of both the software and hardware. A properly-designed application in internet o things can comprise of microservices, edge devices, and cloud gateway for establishing a connection between the Internet and the edge network so the users are able to communicate via IoT devices.

Development for configuring systems and linking IoT devices come with an increased level of complexity. Design patterns in IoT tackle issues to manage edge applications starting from initialization and going towards deployment. In developing IoT applications, edge deployment pattern is one of the most commonly used design patterns.

Edge Code Deployment Pattern

Problem

How to make sure the developers are able to deploy code bases for several IoT devices and ensure that they achieve the required speed. Similarly, there are also concerns regarding the security loopholes of the application. Additionally, it includes how to configure IOT devices while not worrying about the time-consuming phases of build, deployment, test, and release.

Considerations

One of the primary factors for deploying a portion code is maintainability for deploying IoT devices which are remotely based. When developers resolve bugs and enhance the code, they want to add to ensure that this updated code is deployed to its corresponding IoT devices as soon as possible. This assists with the distribution of functionality across IoT devices. After some time, the developers may be required to perform reconfiguration of the application environment.

Let’s consider that you are working on an IoT system which uses billboards to show advertisements in a specific area. Throughout the day, your requirements include modifying the graphical and textual elements on the screen. In such a case, adaptability and maintainability emerge as two of the toughest challenges where the developers are required to update and deploy the code to all the corresponding IoT devices simultaneously.

Often networking connectivity slows down the internet of things ecosystem. To combat this dilemma, you can incorporate the relevant changes rather than proceeding the complete application upload with a network that is already struggling to maintain performance.

Developers are required to consider programming as their primary priority and search for the right tools which can assist with their development. It is important to ensure that the IoT devices’ code deployment tools remain transparent so it can help the developers. This can assist in achieving a deployment environment that is fully automated.

Subsequently, the safety of operations is also improved. As discussed before, your development pipeline should be aimed at building, deploying, testing, releasing and distributing the application for the devices in the internet of things ecosystem.

For testing, you can use the generated image with an environment that resembles the production environment and initiate your testing. After the completion of the tests, the IoT devices take out the relevant image. This image is derived out from the configuration files, the specification of the container, and the entire code. One more aspect is that there is a need for a mechanism which can help coders to rollback their deployment to a prior version so they do not have to deal with outage—something which is quite important for the IoT devices.

Additionally, for the new code requirements, the deployment stage has to contain and consider configurations and dependencies of the software. For this purpose, they need reconfiguration for the environment of the application and the entire tech stack via safely and remotely to maintain consistency.

Solution

Since in recent times developers are extensively using version control systems, it can help with deployments as well. Today, Git is heavily used by developers for maintaining versioning and sharing code. Git can serve as the initial point for triggering the building system and proceed with the deployment phase.

Developers can use Git for pushing a certain code branch at the server’s Git repository which is remotely based so it gets alerts about the software’s new version. Afterward, they can use hooks for triggering the build system initiating the following phase where they can deploy the code in the devices. A new Docker image is built by the build server which adds the image’s newly-created layers. They are received by the central hub of Docker so the devices can pull. With this strategy, a developer can use any version control system like Git to deploy code when the IoT devices are distributed geographically.

After regular intervals, these devices can inquire the central hub or registry to see if there are any new versions. Similarly, the server itself can alert the devices of a new image version release. Afterward, the newly-created image layers are pulled by the IoT devices where they generate a container and make use of the new code.

To summarize, after there are changes in the code, Git is used for commits and pushes. Subsequently, a freshly-built Docker image is created and moves to the IoT devices which then makes use of the image for generating a container and utilizing the code that has been delayed. Each commit starts the deployment pipeline and modifies the source code—thereby it is published for all the devices.

The DevOps survey explains how certain practices help in creating well-performing teams. However, it also sheds light on the gap which is created between two teams—one which works with a quality culture and the other without one.  In order to bridge this gap, machine learning has been cited as one of the solutions. So, how can machine learning change the game?

Save Time in Maintenance of Tests

Many development teams suffer from false positives, mysterious test failures, false negatives, and flaky tests. Teams have to create a robust infrastructure for analytics, monitoring, and continuous delivery. They then use automated tests and use test-driven development for both their user interface and APIs. As a consequence, a lot of their time is spent on maintaining their tests.

This is where machine learning can be useful and automate such tests. For example, the auto-healing functionalities of mabl can be used for such purpose. Algorithms of mabl are optimized in order to pick the target element for which interactions are required for a journey.

Many factors and considerations are needed to create maintainable automated tests, however, the capability of user interface test in assessing the change and execute accordingly saves a considerable amount of time. It may be possible to apply these benefits for the other types of tests too. The automation of tests for the service or API level is significantly less taxing in comparison to the user interface; however, they also need maintenance along with the modifications of the application. For instance, machine learning can be required to choose new API endpoints parameters and place some other automated tests for covering them.

Machine learning is great at consuming large amounts of data and learn from it. The idea to assess and determine failures in tests for the detection of patterns is one of the best advantages of machine learning.  You may even get to find an issue prior to the failure of any test assertions.

More on Testing

You can work around your test and production code in such a way that along with information about errors, it can also log the information pertaining to an event’s failure. While this sort of information can be too big size for a human to make sense out of it, machine learning is not restricted by such limitations. Therefore, you can use it to create meaningful output.

Machine learning can assist in the design of reliable tests which saves up on time. For instance, it can detect anti-patterns in the code of test. Similarly, it can identify those tests which can be marked with a lower priority or identify those system modules which can be mocked so a test runs quicker.

Like mabl, concurrent cloud testing increases the speed of continuous integration pipelines. However, when end-to-end tests are run in a website browser, then their speed is slow in comparison to those tests which run in the browser’s place.

Testers use machine learning to get recommendations like which tests should be automated with regards to their importance or when to automatically create such tests. To do this, a production code’s analysis can assist to pinpoint problematic areas. One application of machine learning is a production usage analysis to discover user scenarios and automatically generate or recommend the creation of automated test cases for covering them. The optimization of time which is needed to automate tests can be replaced by an intelligent automation mechanism of the most necessary ones.

You can use machine learning to assess production use and see how to get the application’s user flows and collect information about the accurate emulation of production use for security, accessibility, performance, and another testing.

Test Data

The creation and maintenance of data which is similar to production consist of different automated tests. Often, serious problems fail to get detected in production as the problematic edge needs a data combination which is not replicable in standard test environments. Here, machine learning can locate a detailed, representative, and comprehensive production data sample, eliminate any possible privacy concerns and produce canonical test data sets which are required by manual exploratory testing and automated test suites.

You can equip your production code, log detailed events data, and configure production alerts and monitoring—this can assist in a quicker recovery. Decreasing MTTR (mean time to recovery) is a nice objective whereas low MTTR is linked with high performance. For domains where the level of risks is particularly higher like in real life critical applications, you may have to use exploratory testing to decrease the possibilities of failure.

Human Factor

While context differs, however, most of the time, it is not advised to experiment with all types of automated testing in types. Thus, human eyes and similar senses are required along with the thinking skills to assess and be informed about the types of risks which can hit the software in the future.

The automation of boring stuff is also necessary so it can help testers for the interesting testing. Among the machine learning applications, one of the initial for test automation is visual checking. For instance, screenshots of all the visited pages are used by mabl to create the visual models of the automated functional journey. It identifies those parts which must change like ad banners, date/time or carousels. Similarly, it rejects new modifications in these areas. It is used for the shrewd provision of alerts whenever visual differences are detected for the areas which must look the same.

In case you have executed visual checking all by yourself through viewing a user interface website page in the production along with testing the same element, then you can understand how heartbreaking it can be. Thus, machine learning can help here to complete all the repetitive and tedious tasks.

DevOps refers to a group of modern-day IT approaches and practices which are designed to join operations and IT staff members (generally software developers) on a project with unforeseen levels of collaboration. The idea is to eliminate the traditional hurdles and barriers which have historically plagued cooperation between these departments, resulting in losses to an organization. Therefore, DevOps quickens the deployment of IT solutions. As a result, development cycles are optimized and shortened, saving money, time, staff, and other resources.

In the last few years, DevOps has commanded a strong presence in the IT circles. Despite its popularity as a useful approach, the domain lacks visibility and suffers from improper implementations. As a result, the operation and development departments are unable to leverage its maximum advantage. The integration of DevOps is needed in organizations where the IT leadership has its own strategy for its implementation.

As DevOps gains greater recognition, its community has managed to discuss the best practices which can help organizations improve collaboration and develop higher quality IT solutions. Following are some of the DevOps tips that can help you propel your IT department in the right direction through clean coding and optimized operations.

1.     Participation from the Concerned Parties

One of the founding principles of DevOps is to ensure that the operations personnel, the development professionals, and the support staff are facilitated to work together at regular intervals. It is important that all of these parties recognize the importance of each other and are willing to collaborate.

One of the well-known practices in the agile industry is the use of onsite customer where the agile development team operates the business by offering their support. Experienced professionals are known to adopt stakeholder participation practice. This practice recommends developers to expand their horizons and work closely with all the concerned parties other than the customer.

2.     Automated Testing

An agile software engineer is always expected to spend a considerable amount of time in quality programming along with focusing on testing. This is why automated regression testing is a recurring tool in the toolbox of agile developers. Sometimes, it is modeled as behavior-driven development or test-driven development.

One of the secret ingredients for the success of agile development is the fact that these environments are continuously run daily tests to identify issues quickly. Subsequently, they also receive rapid fixing. As a result, such environments are able to attain a greater degree of software quality in contrast to the traditional software approaches.

3.     Addition of “I” in Configuration Management

Configuration management is a set of standard practices which is required to monitor and manage changes in the software lifecycle. Previously, configuration management was ineffective because it was too limited. DevOps has allowed adding “I” in the CM equation, which has produced integration configuration management. This means that along with the implementation of CM, developers are also analyzing problems related to production configuration which originates between the infrastructure of the company and the proposed solution. This is a key change because developers were not too concerned about such variables in the past and only focused on CM in terms of their solutions.

DevOps promotes a fresh brand of thinking which targets enterprise-level awareness and offers visibility to a more holistic picture. The ultimate goal of integrated configuration management is to ensure that developer teams are equipped with the knowledge about all the dependencies of their solutions.

4.     Continuous Integration

When a project’s development and validation undergoes automated regression testing along with optional code analysis (after the code is updated via version control system), the approach is labeled as continuous integration. CI evokes positive responses from agile developers working in a DevOps environment. With support from CI, developers have been able to design a working product with gradual and regular coding while any code defect is promptly addressed through immediate feedback.

5.     Addition of I in DP

Traditionally, deployment planning was done with cooperation from the operation department of organizations whilst at times the release engineers were also consulted. Those who have been in the industry for a long period of time are compliant with this planning from the assistance received from active stakeholder participation.

In DevOps, one is quick to come to the understanding that a cross-team is necessary in deployment planning so the staff from the operations department is easily able to work with each of the development teams. Such practice may not be anything new for the operations staff, though development teams would find it the first instance due to their restrained nature of duties.

6.     Deployment

The methodology of CI is further enhanced through continuous development. In CD, whenever integration is met with success in a single sandbox, the modifications are sent to the upcoming sandbox as a sort of promotion. The promotion can only be stopped when the modifications require verification from people who serve the operations and development.

With CD, developers are able to decrease the time duration elapsed between the identification of a new feature and its deployment. Companies are becoming more and more response thanks to CD. Though, CD has raised some eyebrows for operational risks where errors maybe originated during the production due to the negligence of the developers.

7.     Production

In high-scale software environments, often software development teams are engaged in updating a new release of a product which is already established .i.e. it is in production. Hence, they are not only required to spend time on the newer release but also handle any incoming issues in the production front. In such cases, the development team is effectively the third level of application’s support as they are expected to be the third and last set of professionals to respond to any critical production issues.

Final Thoughts

Do you work with enterprise-level development?

In this day and age, DevOps is one of the most useful tools for large organizations. Follow the above-mentioned tips and improve the efficiency of your IT-related solutions and products within a short period of time.

As the world entered into a new millennium in the year 2000, few people would have predicted how the next 19 years would completely change the face of several industries. All this transformation was made possible through the rising IT juggernaut. One such industry was the call center industry. Traditionally, the industry focused on dealing with as many customers as it could within a certain time period. Times have changed since then, and now the objective is to get the approval of customers by fixing their issues and generate quality CX (customer experience).

This paradigm shift was supported by speech analytics, which refers to a practice in which the voice of customers undergoes analysis and processing. This voice is converted into the text where analytics identify patterns in the recorded data. In this way, customer feedback like complaints, recommendations, suggestions, and other useful factors are collected easily, which are then applied to enhance the services and products of organizations.

Therefore, unsurprisingly, speech analytics has become one of the major reasons behind the resurgence of customer satisfaction and retention for many organizations. According to MarketsandMarket, by 2020, the speech analytics industry is estimated to balloon up to $1.60 billion.

Today, organizations harness speech analytics for raising the standards of their conversations, insights, and outcomes. If we examine through a historical outlook, then it was always the norm for organizations to comprehend and recognize the requirements of customers and adjust their offers accordingly.

For instance, Henry Ford got a breakthrough in the automotive industry when he divided the major issues at hand and went through their individual analysis. Then, he began implementing procedures that would supplement the efficiency of his organization. Ford’s approach was not too dissimilar to the modern-day analytics.

The Change in Mindset

Previously, call center agents were involved in repetitive outbound calls to their potential and actual clients. They received queries from inbound calls and the actual emphasis was on the length of the call. However, in this age, the emphasis has shifted towards customer engagement and overall experience. Organizations now understand that offering solutions to identified issues can help in fostering loyalty with customers and maximizing their own revenues.

Consider your surroundings to analyze accepted and normal behaviors. For instance, have you noticed how invested people have become lost in their smartphone screens? It is hard to imagine how days went by before the invention of the smartphone. Interpret the usage of IT and its impact on daily human interactions. Likewise, get a grip on the social media frenzy which has become the primary medium for communication.

Today, industries are actively pursuing speech analytics to boost their operations. The older voice solutions like IVR (Interactive Voice Recognition) have been shunned a long time ago. This is the speech analytics age where the technology is powerful enough to grasp the message of a customer and facilitate the management to respond with a workable and effective solution.

What Did Speech Analytics Do?

Speech analytics has triggered a revolution for call center agents through the following benefits.

Locating Customer Issues

The operational nature of call centers vary. However, standard practices are applied by all. It is important to understand that these practices may not be the best replacements. On the other hand, speech analytics ensures that a call center is able to mark the exact points from where customer grievances arise. Likewise, it can then guide agents to respond to those issues.

For instance, a customer purchases a winter jacket from an e-commerce website. After receiving the jacket, he might find its quality subpar or that the color does not match his order description. In response, the agitated customer calls for the cancellation of the order and demands a refund. Speech analytics can be used here to identify the common issues in these orders which can not only improve the service but also assist in adding a new feature in the product as a result, the churn cycle could be minimized to an extent.

Marketing

There is a strong relationship between the sales and marketing departments of organizations, and therefore they are interdependent. With speech analytics, marketing experts can take notes from the calls of sales agents which can assist them in “connecting” to their customers.

For instance, speech analytics pinpoints to the high pricing of a product as a major issue for a wide majority of customers who are primarily students. The marketing department can use this piece of information to promote an advertisement based on discounts for those students. Consequently, orders pour in and the marketing department scores an important victory.

The marketing department views speech analytics as a formidable tool which can provide it with the demographics of customers like their occupation, gender, age, location, etc. Hence, their marketing strategies are adjusted accordingly.

Training

One of the most productive uses of speech analytics is to allow supervisors and managers to train their calling agents. The performance of those agents is duly assessed by going through these calls. Proper analytics can also recognize if a particular agent requires further training or not. Likewise, it can serve as the basis of promotion for an agent whose output was not noticed before.

Minimizing the Churn Rate

The loss of clients is referred to as the customer churn. Customer churn is an extremely important factor for organizations, especially the SMEs. Presently, consumer habits are ever-changing which means that same product which generated positive results in 2017 may not generate the same results today. This is where speech analytics can be leveraged to identify the changing trends so businesses can comprehend and tailor their processes accordingly for better results.

Final Thoughts

The inception of speech analytics has disrupted the conventional call center industry. Businesses are using it to increase their revenues. If you do not use it in your operations, then do give a thought or two about integrating it in your business processes.

Search engines are used on a daily basis by people all over the world. Individuals living in both first world, as well as third world countries, frequently rely on search engines to answer all of their questions. Whether you’re looking for the nearest restaurant or a new product to buy, you’re likely to use Google, Bing, or another search engine for this purpose. Have you ever wondered how do search engines skim through tons of data to offer you the best possible results? Well, here’s a quick look at how search engines work.

Index

When a user types a search query, the search engine picks up the most relevant web page and sends it back to the user as a response. This web page has to be stored somewhere. To do this, search engines maintain a large index. The index assesses and arranges each website and its web pages so they can be linked with the phrases and words which are searched by users.

Additionally, the index has to put a specific rating for pages that are associated with a specific topic. To do this, they have to maintain a standard for quality so users can get the most relevant and useful content from search engines. For example, suppose a user searches “How to learn Java?” Such a topic is already discussed by different websites so how does the search engine know which pages to show to a user? To do this, search engines have designed their own criteria.

Crawlers

Website crawlers are programs that serve as the primary tool for search engines. They are responsible for recognizing the pages in the “Web.” Whenever a page is identified by a crawler, it is “crawled.” Crawl refers to the analysis of the page during which the crawler gathers and accumulates different types of data from the web page. It then adds the page in the index after which the hyperlinks of the page are utilized to go to other websites and the process is repeated. This process is similar to the life of a spider which weaves a web and crawls from one place to another easily. Hence, crawlers are also called as spiders. Web administrators can make their web pages more readily available to crawlers through two techniques.

• Sitemap – A sitemap contains a hierarchical view of the complete website with topics and links which makes it easy for crawlers to navigate all the pages of a website.
• Internal Links – Website administrators place hyperlinks in the web content which direct to the internal web pages across the entire website. When crawlers come across such links, they are able to crawl through them, thereby improving their efficiency.

Algorithms

One of the most important considerations for an index is to assign a relative rating to all the stored web pages. When a crawler finds 20,000 relevant web pages for a user query, then how is the search engine suppose to choose the ranking results?

In order to address these concerns, search engines like Google use an extensive list of algorithms. Examples are RankBrain and Hummingbird. Each search engine has a think-tank which initially decided on how to rank a web page. This understanding takes some months before a large algorithm is designed to rank websites.

How to Rank?

Search engines rank a website after considering multiple factors. Some of the common factors are the following.

Age

If there are two websites A and B with the same attributes other than the fact that A was designed earlier than B, then A holds greater importance for the search engines. Search engines favor older websites and see them as reliable and authentic.

Keywords

Perhaps the most important metric for search engines is to assess a keyword. When search engines look for the best results in terms of relevancy, they go through a list of words and phrases. These words are popularly known as keywords. The importance of keywords has to lead to the creation of a separate study known as keyword analysis.

Mobile Optimization

Search engines like Google have made it clear that if a website loads and works well on desktop PC then it is not enough. Nowadays mobiles and smart devices have swarmed the world. Wherever you go, everyone has a mobile in hand. Therefore, mobile optimization is necessary for search engines.

Links

External links remain as one of the most crucial metrics for search engines to rank a page. If your web page is linked by a credible and established website, then the worth of your page increases in the eyes of the search engines. It conveys them a message that your web page has informative and high-quality content that made others reference your website. The more external links a web page receives, the more quickly the website gains a boost in its online ranking.

Speed

How often have you opened a website, only to be annoyed by the slow loading speed? Factors like these also weigh on ranking. When crawlers visit a link and find slow loading, they record it and inform the search engine. Consequently, the search engines cut off your mark in ranking and your page will lose its online ranking. Likewise, search engines monitor the number of instances where a user enters your web page and immediately leaves it, thereby signaling the possibility of slow loading.

The Birth of a New Field

Brick-and-mortar businesses are a thing of the past. Today, every brand has gone digital to market and advertise their services. This has given rise to the creation of an in-demand field known as SEO (Search Engine Optimization). Since every website competes to rank well on search engines, mainly Google, website owners have to understand how search engines work. To do this, there are several SEO strategies which are formed on the basis of search engine metrics and allow businesses to rank high on search engines.

If you have used RabbitMQ for managing the messaging part of your applications, then you are certainly aware of the configuration issues which are recurrent with it. Software engineering experts who have used RabbitMQ for an extended period of time know that it can face certain configuration errors.

Therefore, it is necessary to configure it correctly. In this way, you can achieve the best possible performance along with getting the cluster which is the most stable of them all. This article focuses on a number of tips and techniques which can improve the availability and uptime of your application.

Limit Queue Size

The arrangement of queues matters in the RabbitMQ environment. If you wish to attain maximum performance for your application then ensure that these queues remain shorter in size. When a queue is considerably large in size, then the accompanied processing overhead slows down the performance of the application. In order to achieve optimal performance for your application, queues should remain close to 0.

Turn On Lazy Queues

Since RabbitMQ 3.6, there is an option in the messaging platform to use lazy queues. In lazy queues, messages are automatically redirected to the secondary memory of your disk and are then stored there. When the need arises for a message, then the main memory calls out the corresponding message from the disk. Since all the messages are stored on the disk, therefore it helps to manage and optimize the usage of RAM. On the other hand, it also promises additional time for the throughput.

The use of lazy queues ensures you can produce improved stable clusters. These cluster features are known to infuse a certain degree of predictability in the performance. As a result, the server’s availability gets a much-needed boost. If your application involves too much processing of batch jobs and deals with a high number of messages, then you might be concerned that the speed of publisher may not always be kept up by the consumers. In this case, you must give a thought to turn off the lazy queues.

Manage Cluster Setup

The node collection in RabbitMQ is also known as a cluster. In order to improve availability, make sure that the clients are facilitated with data replication in the event of any possible or expected failure. This means that even if a cluster is disconnected, clients could access it to fulfill their requirements.

In Cloud AMQP, you can create 3 clusters. You have to locate all nodes for various availability zones (AWS) while queues should be replicated (HA) amongst the availability zones.

If a node goes down, then there should be a mechanism which can transfer processing to the remaining cluster nodes for keeping up the availability of the application. To do this, you can use a load balancer with the instances of the RabbitMQ. The load balancer can help with the message publishers for transparent brokers distribution.

Keep in mind that in Cloud AMPQ, 60 seconds is the max failover time. The DNS TTL runs every 30 seconds while the endpoint health is processed after every 30 seconds.

By default, message queues are positioned on a single node. However, they can be reached and seen by all the other nodes. For replication of queues in the node cluster, you can visit this link to achieve high availability. If you need more information on the configuration of nodes in your cluster, then you can visit this link.

Using Durable Queues and Persistent Messages

If it is absolutely critical that your application cannot lose a message, then you can configure your queues as “durable”. In this way, your sent messages are now delivered with a “persistent” mode. If you wish to prevent the loss of messages with the broker, then you have to consider broker crashes, broker hardware failure, and broker restart issues. In order to make sure that the broker definitions and messages are not hampered by restarts, you can store them on the disk. Queues, exchanges, and messages which are not declared as persistent and durable are missed if the broker restarts.

Using the Federation for the Cloud

According to some experts, clustering between regions or cloud is not recommended. This means you do not have to think about the distribution of nodes across data centers or regions. In case, an entire cloud region is disconnected, the impact can force the CloudAMQP to falter as well, though it not necessary that it will be affected. The nodes in the cluster are distributed among availability zones in the same region.

If you wish to safeguard the setup when a complete region goes down, then you can configure two clusters for separate regions and apply “federation” between them. Federation is a plugin in RabbitMQ. The use of the federation plugin allows your application to leverage more than one broker which are spread across separate systems.

Turn Off HiPE

If you plan to use HiPE, then avoid it. When HiPE is used, the server throughput is noticeably increased but it also increases the startup time. If HiPE is turned on, then the compilation of RabbitMQ is triggered at the startup. This also means that the startup time can increase to as much as 3 minutes. As a consequence, you might face issues during a server restart.

Disable Detailed Mode

If you have set the RabbitMQ Management statistics rate mode as “detailed, disable it immediately. When this mode is enabled, it can affect the performance and availability of the application. As such, it should not be utilized in production.

Limit Priority Queues

All the priority levels work on the Erlang VM with an internal queue. This queue consumes the considerable resource. For general use, you may not need more than 5 priority levels.

One of the most popular machine learning algorithms is the random forest algorithm. In real life nature, a forest is measured according to the number of its trees. A bigger number points towards a healthy forest. The random forest algorithm functions on the same principle of nature. As its name suggests, it creates a forest with trees. However, these trees we are talking about are the decision trees which we have covered in one of our earlier posts. These decision trees allow a random forest to make accurate forecasting decisions.

The forest is referred to as random because of the randomness of its components. Each of the trees in this forest receives training through a procedure known as the bagging method. This method enhances the final result of the algorithm.

The classifier class in a random forest is convenient. While the trees in the forest grow, the algorithm applies to achieve a greater degree of randomness.

In other algorithms, the best feature is determined during the split of a node. In this one, when the algorithm is in the working stage, the best random feature out of a collection is searched. This is done in order to enhance the model’s diversity.

Bear in mind that out of all the features, only a select few are assessed by the random forest during a node’s split. For further randomness in its trees, another technique known as the random threshold is used where they are equipped with all the individual features.

To illustrate this point, take the example of a man named Bob who wants to dine at a new restaurant. Bob asks his friend James for a few suggestions. James asks Bob questions related to his likes and dislikes in food, budget, area, and other relevant questions. In the end, James uses Bob’s answers to suggest a suitable restaurant. This whole process mirrors a decision tree.

Moving forward, Bob is not happy with the recommendation of James and wants to explore other dining options. Thus, he begins asking other friends for their recommendations. He goes to 5 more people who act similar to James; they ask him relevant questions to provide a recommendation. In the end, Bob goes through all the answers and picks the most common answer. Here, each of Bob’s friend acts as a decision tree and their combined answers generate a random forest.

Determining the relative importance of the feature of a prediction is easy in random forest, especially if it is compared with others. If you are looking for a tool which can aid you to calculate such values, then do consider the scikit-learn—a machine learning library in Python.

In the post-training period, a score is assigned to all of the features so the results can be scaled. This makes sure that a zero value is placed for each of the importance sum.

The assessment of feature importance is crucial in order to drop a feature. Usually, a feature is dropped when it struggles to add anything of value to the prediction. This is done because too many features pose the issue of over-fitting.

Hyper-parameters

One of the factors that make random forest unique is the precise output which does not require tuning of hyper-parameters. Like a decision tree, a random forest carries its own hyper-parameters.

In random forest, hyper-parameters are used for increasing the speed of the model. Following are the scikit-learn’s hyper-parameters.

• n_jobs

It provides the engine with details about the limit of processor for computational usage. If it has a “1” value, then this indicates that only a single processor can be run. On the contrary a “-1” value indicates that there is no restriction.

• N_estimators

It is the overall number of trees to be generated in the time period before the determination of max voting and averages for predictions. A larger number of figures increases reliability but it also affects the performance speed.

• random_state

It is used to convert the model’s output to create a replicable result. If similar piece of training data, a definite value for random_state, and hyper-parameters are inserted in the model, then the output would also be identical.

• min_sample_leaf

It examines the lowest limit of a leaf for the split of internet nodes.

• Max_features

It takes the figure of maximum digit of features that are required to be used in each tree.

Now that you have understood how a random forest algorithm works, you should begin implementing through coding. For more information about machine learning, check other blogs on our website.

Machine learning is divided into types: supervised and unsupervised machine learning. In supervised machine learning, inputs and outputs are offered. To aid judgment in future, it offers several algorithms of fixed quantities. Supervised machine learning algorithms have come up with applications like chatbots, facial expression system, etc.

Both classification and regression fall into the category of supervised learning. So what are they and why is it necessary to understand them?

Classification

If your dataset requires you to work with discrete values, then you should use classification. When the solution to a problem demands a definite or predetermined range of output, then you most probably have to deal with classification. The following scenarios are one of the few examples where classification is used.

• To determine consumer demographics.
• To predict the likelihood of a loan.
• To check who wins or lose a coin toss.

When a problem can have only two answers (yes or no), then such a classification falls under the category binary classification.

On the other hand, multi-label classification processes several variables. This type of classification comes handy in the above-mentioned consumer segmentation, grouping images, and text and audio analysis. For instance, a sports blog can have posted about multiple sports like basketball, baseball, tennis, football, and others, at the same time.

There is also the multi-class classification in which a target defines a sample. For example, it is possible for a fruit to be apple or banana but it cannot become both at the same time.

Classification computes only those values which are “observed”. It relies on the total of its input to compute forecasting which offers more than a single result. The algorithm which maps a provided input into a specific category is referred to as the classifier. The feature is a measurable variable.

Before the creation of a classification model, firstly you have to pick a classifier and have it initialized. Subsequently, you have to provide some training to that classifier. In the end, you can check the output for the observed x values to predict the label y.

Regression

Regression works opposite to classification; it is used for the prediction of results where continuous values are at play. In regression, the variables are flexible and can be modified, unlike classification hence; there is no need to restrict to a fixed set of labels.

Linear regression is one of the leading algorithms. Sometimes, linear regression is underestimated as some perceive its working to be too easy. However, in actuality, linear regression can be used in multiple cases, as it is quite simple in comparison to others. You can use logistic regression to estimate the prices of property, assess the churn rate of customers, and even manage the collection of money from that person.

For more details, keep following this blog series. If you have any questions, then you contact us to clear up your confusion.