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.
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.
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.