In this special guest article, Erik Fogg, COO at ProdPerfect, covers some of the key benefits of adding AI to the software testing process, and why you should consider adding it to yours if you don’t. haven’t already. ProdPerfect is a stand-alone E2E regression testing solution that leverages live user behavior data.
Software testing can represent between 25% and 40% of a project’s budget. It is an expensive process, but a necessity to make sure the software is working properly. Large companies will have entire teams dedicated to testing, but for many small development teams, a large testing service exceeds the available budget. Meanwhile, market demands encourage rapid development phases, leaving little time for adequate testing.
Businesses large and small are looking for ways to reduce costs and improve test reach and reliability, while delivering software that meets customer expectations and deadlines. It is under these pressures that testers are increasingly turning to artificial intelligence and machine learning to augment their testing processes.
It is also not the only industry to do so. Governments, insurers, and the medical industry are all pumping money into the artificial intelligence world for many different purposes. The application of artificial intelligence to almost every field of activity has brought about profound changes, and this is no less applicable to software testing.
AI and quality assurance are an alliance made in robot heaven. Why are they such a good pair and how can they improve 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 your own if you haven’t already.
Shorten software development cycles
New software is continuously released and updated by engineering teams at the border of maturity and talent. The software lifecycle is getting shorter and more complicated. The rise in popularity and use of microservices, third-party APIs, and other software packages has left many developers building software with hundreds of different dependencies, all of which need to be tested.
While software releases were once a month, major engineering teams release dozens of times a month. daytime. For every new feature added or changed, rigorous testing must be done to ensure that it is ready for consumers. Popular development methodologies now favor the release of frequent small 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 software development and meeting deadlines. Developers must meet milestones and deadlines set by customers and leaders, while delivering quality software. Automation is a topic very familiar to developers, so it’s no surprise that 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 versions. Below are some of the main benefits AI and ML bring to software testing.
Improved code coverage
Like many topics in software development, there is debate over exactly how much code coverage is adequate for a test suite. Some developers advocate 100% code coverage, and some call it a pipe dream. Creating good tests takes time, especially if you’re trying to achieve 100% code coverage. Software products very often develop organically, which means development can be a haphazard process where technology and testing debt increases over time.
Embedding testing as an integral part of the development process can be a hard sell, so if it is not prioritized enough in the company culture, software products can end up with loopholes in the testing process. . This only adds to the pressure on testers, who must choose between staying on top of new feature testing or focusing their attention on fixing shortcomings and creating a backlog of new features to test.
With AI supporting the development and maintenance of automated tests, 100% code coverage is no longer a pipe dream or a huge waste of time. The ML can be trained to understand a code base and generate tests based on the units of code it finds. It can be trained to learn context, understand expected results, 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 much 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 must be run on old code and new code each time a change is made. Regression testing is necessary to ensure that new features, updates, fixes, and other changes do not introduce bugs into existing code. These regressions are common, especially if the code is poorly documented or if developer turnover on a project is high. Customers can create existing feature change requests that seem straightforward at first glance, but require major restructuring within code, further increasing risk.
Developers need to make sure that they don’t introduce significant changes to the codebase, but when this is combined with the need to deliver small updates quickly, regression testing can quickly become a bottleneck. throttling in the testing process. Testing entire code bases for small commits may seem impractical, but small mistakes can have big consequences.
AI-powered automated testing makes the completion of comprehensive test suites in a timely manner for every change, no matter how small, a possibility. AI can prioritize testing better than humans and will soon be able to retarget on-the-fly regression testing to test high-risk areas with short run times. This can be combined with other advancements, such as parallel testing, to further reduce the time required for testing.
A solid E2E regression test involves not only testing the code itself, but very often testing it on a wide variety of devices and browsers; this is especially true for web applications. Sequential testing involves running a suite of tests on each device in a collection one at a time. Even when using virtualized devices, it can take hours or even days – far too long for a rapid pace of deployment.
Parallel testing aims to dramatically reduce device testing time by running these tests at the same time, i.e. in parallel. As parallel processing progresses, threads can be separated 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, thereby optimizing test threads and infrastructure resources to maximize parallelization and minimize test cycle time. Further advancements here can allow deep regression test suites to run in minutes, supporting continuous deployment with near zero risk of introducing regressions into production.
The future of software testing
Advances in AI and ML are expected to increase the variety of tasks that can be automated and reduce the time required to accomplish them, by prioritizing them, resourcing them dynamically, and performing them in parallel. There are many programming languages ââout there, and some of the newer languages ââeven have parallel processing support built into the core of the language itself. It could revolutionize software testing, which can traditionally be a long and expensive process. By allowing ML to learn a code base and automatically generate and run tests, testers are free to focus their efforts elsewhere, while helping developers deliver more reliable software with fewer bugs.
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