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.