The new wave of quality assurance and software testing is here, and it’s the adoption of artificial intelligence. DevOps and the evolution of CICD has matured the software development and testing space to be ripe for advancement. End-user demand for quality products and faster delivery is driving the increase of artificial intelligence in software testing. The more mundane tasks we can automate with AI, the more we can speed up pipelines while allowing experts to innovate.
Many define Artificial Intelligence (AI) as the simulation of human intelligence or human-like decision making by computer systems. If you want to get even more official, John McCarthy, who many call the “godfather of Artificial Intelligence” defined AI as “the science and engineering of making intelligent machines.”
Key elements of AI include computer systems exhibiting intelligent behavior, solving complex problems, or taking appropriate action, based on current circumstances. Meanwhile, machine learning (ML), often used interchangeably with the term Artificial Intelligence, is when a computer system performs tasks solo, sans human intervention, after being trained to recognize certain patterns.
“AI for 2023 will have many more positive stories, and partly because these technologies—whether it’s generative AI, or it’s the intersection between AI and science—are actually more useful in the short- to medium-term. So they’re not making the same promises that autonomous vehicles or voice assistants were making.” - Azeem Azhar, author of The Exponential Age: How Accelerating Technology Is Transforming Business, Politics and Society.
With software development lifecycle processes becoming more complex, teams are looking to AI to help develop data-driven pipelines to fully take hold of the next level of quality assurance.
The Intersection of Software Testing and AI
Testing is one of the largest bottlenecks for development teams. Within DevOps and the CI/CD pipeline, testing continues to be the least prioritized phase for advancement, yet it can be the source for the most friction in release cycles.
Thanks to massive test suites, flaky tests, and an overall reliance on manual testing, software testing all too often becomes the hurdle standing between developers and faster, more reliable release times.
That’s where AI comes in. AI is starting to be used to automate software testing, and make the testing process less tedious and time-consuming. With AI, the direct involvement of developer testing can be drastically reduced, a surefire path to improving developer experience and workplace efficiency.
AI and machine learning are quickly becoming an integral part of the software development and testing life cycle. Two common scenarios teams have been leaning on AI and ML for improving software testing processes is when they face having too few or too many tests.
Having enough tests is crucial to catching errors quickly and effectively. Without enough tests, developers may be flying blind and unable to catch bugs before they get passed downstream. This can lead to compounding errors and a greater hassle when trying to fix them. This is why AI test-generating tools such as Mabl and Mesmer have become so popular. These tools can help teams build more quality tests for your testing program.
On the flipside of this issue is the alternative conundrum: organizations often face bloated testing pipelines from too many tests. Performing unnecessary tests during each commit can take a lot of time and increase the likelihood of false positives and negatives. To address this, some teams are using machine learning (ML) to support testing at scale. This can involve collecting data to identify the necessary tests to run, which can help optimize the testing process.
Beyond these two general AI use case buckets, artificial intelligence in software testing is branching out into more specific areas, with six groups trending.
Emerging Categories of Artificial Intelligence Software Testing Tools
Artificial intelligence enables machines to mimic human cognitive functions such as learning, problem-solving, and decision-making. This allows machines to perform tasks quickly and accurately, just like humans.
By using AI to perform more mundane tasks, we free up our experts to perform more complex testing and innovation. Artificial Intelligence is making headway in software testing in six categories.
Automated Test Creation and Smart Crawling AI
Thanks to AI, automated test creation tools can streamline the process of writing tests. Meanwhile, smart crawling AI uses NLP (Natural Language Processing) on documented requirements to analyze changes and identify testing gaps. By automating test creation and analysis, AI greatly speeds up test development and enables team members to focus on other, more sensitive issues.
Self-Healing and Visual Inspection AI
In a similar vein, self-healing AI is also a growing tool category, focused on improving application quality and workflow efficiency. Self-healing AI can best be thought of as automation for automation. Self-healing AI testing tools detect changes and automatically make updates, to keep the test cases functional.
We are also seeing the advent of AI-based visual inspection tools. Visual inspection tools visually examine the features of an app, in order to find and then fix issues that would mar product experience after release. Automated visual inspection is so important to modern DevOps teams because it can help to create a better end-user experience.
Coverage Detection and Automated Anomaly Detection AI
Coverage detection tools speed up software testing by automatically detecting all of the avenues an end user could take in an app and then automatically reporting code coverage gaps. The big benefit of coverage detection tools is the ability to improve testing efficiency and to drive higher, better quality performance.
Automated anomaly detection in software testing helps highlight any incongruence between actual test results and what was expected from testing. Anomaly detection can uncover flaws like improper test use cases or software requirement specifications, as well as virtually any other features that need improvement or correction. Automated anomaly detection tools in software testing are one of the best ways to speed up the testing cycle and find bugs faster. Finding problems faster = faster fixing.
Predictive Test Selection for Intelligent Test Impact Analysis
For teams struggling with slow tests and bloated test suites, Test Impact Analysis is a way to accelerate test cycles by running only the tests that matter for source code changes. Traditional Test Impact Analysis approaches use static code analysis to determine the tests that are most likely to be affected by a code change. This requires using tools that are specific to particular programming languages or frameworks making it cumbersome and more expensive to apply in environments with many different languages and environments.
While there are many approaches to analyzing code changes to assess the impact on tests, Launchable uses machine learning for Predictive Test Selection. This approach to Test Impact Analysis determines which tests to run for a given code change using a machine learning model that has been trained on historical test results. With Predictive Test Selection, teams can scale test impact analysis to every project, allowing them to have faster testing cycles while also increasing confidence in their code.
The growing adoption of AI in software testing allows developers to focus more on innovation. Prioritize finding tools that advance your team’s capabilities and tackle your biggest testing hurdles.