In this special guest feature, Erik Fogg, Chief Operating Officer at ProdPerfect, covers some of the main benefits of adding AI to the software testing process, and why you should consider adding it to yours if you haven’t already. ProdPerfect is an autonomous E2E regression testing solution which leverages data from live user behavior data.
Software testing can represent anywhere from 25-40% of a project’s budget. It is an expensive process, but a necessity for ensuring software works correctly. Larger companies will have entire teams dedicated to testing, but for many smaller development teams, a large testing department is beyond the available budget. Meanwhile, market demands encourage rapid development phases, which leaves little time for adequate testing.
Companies large and small are looking for ways to reduce costs and improve the scope and reliability of testing, all while delivering software that meets client expectations and deadlines. It is under these pressures that testers are increasingly turning to artificial intelligence and machine learning to augment their testing processes.
They’re not the only industry to do so, either. Governments, insurers, and the medical industry are all pumping money into the world of artificial intelligence for many different purposes. The application of artificial intelligence to almost every area of business has brought about profound change, and this is no less applicable to software testing.
AI and quality assurance are a match made in robot-heaven. Why are they such a great pair, and how can they enhance the software testing process? This article will cover some of the main benefits of adding AI to the software testing process, and why you should consider adding it to yours if you haven’t already.
Shortening software development lifecycles
New software is continuously being released and updated by engineering teams on the frontier of maturity and talent. The life-cycle of software is increasingly shortening and becoming more complicated. The boom in the popularity and use of microservices, third-party APIs and other software packages leaves many developers building software with hundreds of different dependencies, all of which need to be tested.
Where software releases used to happen once a month, leading engineering teams release dozens of times per day. For every new feature that is added or altered, rigorous testing must be carried out on it to make sure it’s ready for consumers. Popular development methodologies now favor pushing small, frequent updates, which puts additional strain on the testing process.
Finding a balance with AI and ML
There is a struggle in software development between the competing pressures of developing software and meeting deadlines. Developers need to meet milestones and deadlines set by clients and executives, all while still delivering quality software. Automation is a very familiar topic to developers, so it’s no surprise that the advances in artificial intelligence (AI) and machine learning (ML) are now being applied to software testing to improve the pace, accuracy, and cost of testing these complex releases. Listed below are some of the major benefits that AI and ML bring to software testing.
Improved code coverage
Like many subjects in software development, there is a debate over exactly how much code coverage is adequate for a testing suite. Some developers advocate 100% code coverage, and some call it a pipe dream. Creating adequate tests takes time, especially if one is trying to attain 100% code coverage. Software products very often grow organically, which means that development can be a haphazard process where tech and testing debt grows over time.
Embedding testing as an integral step in the development process can be a difficult sell, so if it is not sufficiently prioritized within company culture, then software products can find themselves with holes in the testing process. This only adds to the pressure on testers, who have to choose between staying current with testing new features or turning their attention to patching up the holes and creating a backlog of new features to test.
With AI supporting the development and maintenance of automated testing, 100% code coverage is no longer a pipe dream or a massive time sink. ML can be trained to understand a codebase and generate tests based on the units of code it finds. It can be trained to learn context, understand expected outputs, prioritize what matters most to users, and generate tests that can be run using automatically generated test data. Entire test suites can be automated, making 100% code coverage a far more realistic proposition for many projects.
Avoid delays with regression testing
Regression testing is a particularly time-consuming part of the testing process. This is because tests should be run against old code and new code every time a change is made. Regression tests are necessary to ensure that new features, updates, patches, and other modifications do not introduce bugs into existing code. These regressions are common, especially if code is poorly documented or developer turnover on a project is high. Clients can create demands for alterations to existing features that seem simple on the surface, but require major restructuring within the code, increasing risk further.
Developers need to be sure that they do not introduce breaking changes anywhere in the codebase, but when this is combined with the need to deliver small updates rapidly, regression testing can quickly become a bottleneck in the testing process. Testing entire codebases for small commits may seem infeasible, but small mistakes can have large consequences.
AI-powered automated testing makes conducting full test suites in a timely manner on every change, however minor, a possibility. AI can prioritize tests better than humans, and soon will likely be able to retarget regression tests on the fly to test high-risk areas with short run-times. This can be combined with other advancements, such as parallel testing, to reduce the time testing takes even further.
A strong E2E regression test involves not just testing the code itself, but very often it includes testing it on a wide range of devices and browsers; this is particularly true of web applications. Sequential testing involves running a test suite on each device in a collection one after the other. Even when using virtualized devices, this can take hours or even days: far too long for a rapid deployment pace.
Parallel testing aims to significantly cut down device testing time by running these tests at the same time–that is, in parallel. As parallel processing advances, threads can be spun off to test multiple parts of the application or multiple devices and browsers in parallel, rather than sequentially. AI can support this by identifying through experience which tests can and cannot be run concurrently, thus optimizing testing threads and infrastructure resources to maximize parallelization and minimize test cycle time. Further advances here may allow thorough regression tests suites to run in a few minutes, supporting continuous deployment with near-zero risk of introducing regressions into production.
The future of software testing
Advancements in AI and ML look set to increase the variety of tasks that can be automated and reduce the time it takes to complete them, by prioritizing them, dynamically resourcing them, and running them in parallel. There are many programming languages and some of the newer languages are even integrating parallel processing support into the core of the language itself. This could revolutionize software testing, which can traditionally be a costly and time-consuming process. By allowing ML to learn a codebase and automatically generate and run tests, testers are free to focus their efforts elsewhere, while also helping developers to deliver more reliable software with fewer bugs.
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