The power that AI possesses in terms of reasoning, problem-solving, and learning through experience has resulted in its widespread adoption across business units. It is estimated that the AI market will grow to $22.6 billion in 2020. In the world of software testing, one of the most basic capabilities AI offers is converting manual tests into a set of automated test scenarios. Using AI, developers can understand the fundamental patterns and relationships in different test scenarios, create comprehensive data models of observed parameters, and build automated tests that learn and update themselves. With time, AI models can apply rules intelligently to avoid repetitive or ineffective tests and suggest tests that help fill gaps in business risk coverage.
By automating mundane and tedious tasks, test automation services reduce defect rates, improve automation testing efficiency, and optimize software development outcomes. Since it reduces the direct involvement of developers and testers in mundane tasks, it helps them focus on activities that require human intuition and judgment. Although the technology has mostly been used to automate test design, here’s the very latest on the
Role of AI in software testing and test automation:
Optimizing test strategies
In addition to automating tests, AI can also optimize test strategies. The technology can keep track of what features to prioritize, what tests to run and when, and even what features to build. It can keep track of how frequently certain features are used and examine the business risk associated with every feature. Such information can also help quantify the potential business impact a feature could have on customer churn, damage to reputation and financial loss.
Unlike manual or automated tests where any error detected needs to be manually fixed, AI algorithms can detect abnormalities in existing code and, potentially, fix them – without human intervention. Instead of posting a series of error messages to test logs – especially if many tests are being run – AI can automatically fix changes made to source code, ensure the software is completely verified before going into production, and prevent tests from failing.
When test cases in enterprise portfolios are redundant, they add no value in terms of business risk coverage. AI can help eliminate or prevent redundancies in existing test case portfolios, allowing organizations to achieve the required test coverage – with reduced effort. By identifying tests that are redundant, AI can not only reduce testing overhead but also quicken the process of testing – thus accelerating time-to-market.
Optimizing risk coverage
Since AI models have the ability to learn with experience, they can identify tests that maximize risk coverage and defect detection. Such identification can help in reducing the resources required for software performance testing thus minimizing test execution costs – while optimizing risk coverage in a pre-defined timeframe. Using AI, software testers can effectively distribute test cases to multiple resources to optimize objectives – including which tests to automate and which to conduct manually – to achieve better results.
AI can also help in inspecting test portfolios and increasing their quality in the long run. Using AI, testers can evaluate test case inconsistency and take actions to stabilize automated test cases that break due to bad test design. It can suggest modules and test components that can be reused while recommending ways to make the portfolio more stable (and sustainable). Additionally, an effective test automation framework can be built using AI to distribute test cases to multiple resources, which can help in deciding which tests to automate and which to conduct manually.
Lastly, AI can play a major role in defect analysis. Using AI models, software testers can find the root cause of every bug or issue that causes a defect. Since AI-based algorithms can easily learn from past trends, they can go through the standard defect analysis process in the least amount of time while offering the most accurate root-cause analysis. Such analysis can help testers identify how issues can be prevented while reducing or eliminating the same detects from being injected into the system.
Test smarter with AI
Over the last couple of years, the level of autonomy that AI has enabled in self-driving cars or voice-assisted systems has been massive. In comparison, the use of AI in software testing is still in its infancy, but the technology holds much promise. From automating test scenarios to optimizing test strategies, self-healing defects to preventing testing redundancies, optimizing risk coverage to inspecting portfolios, and analyzing defects – AI can help organizations test smarter, reduce the defect rate, and bring products to market faster.