Why is traditional software testing failing?
Traditional software testing is increasingly limited by manual scenarios and pre-written checklists. Products are evolving faster, releases are more frequent, and changes in code and user scenarios occur almost continuously. As a result, test cases quickly become outdated, some logic remains untested, and so-called "blind spots" in coverage become sources of defects that are discovered in production—where the cost of an error is maximum.
AI in software testing changes the entire approach to quality: models analyze requirements, code structure, and real user actions, automatically generating test cases and proactively searching for non-trivial combinations of system states. This not only speeds up regression testing but also identifies risks that are difficult or impossible to predict manually. In practice, it is at this stage that teams ask the question—what scale of AI solution is needed and how much will its implementation cost for a specific product and processes.
How does AI change the approach to software testing?
Instead of manually describing scenarios, AI models analyze requirements, user logs, and bug history. Based on this, tests are formed that cover both typical and extreme cases.
- Test case generation—automatic creation of functional, regression, and edge-case scenarios.
- Blind spot detection—identifying code and user paths that are not covered by existing tests.
- Adaptation to changes—updating tests with every change in application logic.
Finding Blind Spots in Coverage: The Key Advantage of AI
AI analyzes real user scenarios, telemetry, logs, and sequences of actions in the application and compares them with the current test coverage. This approach allows you to see the gap between how the system should work according to documentation and how users actually use it in production. As a result, areas of logic and user paths that are formally considered secondary, but in practice turn out to be critical, are identified.
It is precisely in these "blind spots" that complex-to-reproduce errors most often occur: unstable bugs, failures with rare combinations of parameters, problems under load or atypical behavior. Unlike manual testing, AI proactively explores the system's state space, generating and testing non-standard scenarios. For businesses, this means more reliable releases, reduced number of incidents after updates, and a transition from constantly fixing bugs to active quality management and product improvement.
Real-world applications of AI in software testing: Test case generation and blind spot detection
Case #1: Meta (Facebook) Sapienz—automatic test design for finding blind spots and crashes in mobile applications
→ Meta's engineering blog describes Sapienz—an approach where AI/search algorithms automatically construct test sequences and run them in CI to find crashes and obscure defects at scale. The practical value here lies precisely in "blind spots": the system is not limited to "expected" scenarios, but proactively explores combinations of application state and user actions that rarely appear in manual checklists. For QA teams, this means less manual routine for inventing scenarios, more aggressive exploration of edge-case paths, and earlier detection of problems before release—especially in products with frequent updates and a wide variety of devices/environments.
Case #2: Microsoft Azure DevOps—transition from manual checks to AI-generated automated tests on Playwright (accelerating regression and relieving the team)
→ In the Microsoft DevBlogs blog, the Azure DevOps team shares their experience of how they automated previously manual checks using an AI-generated approach to create automated tests (Playwright) and reduce the test backlog. The case is indicative in that AI acts as a "speed booster" for test case generation: instead of writing scenarios from scratch and manually maintaining them with changes to the interface/logic, the team received a faster start to automated tests and freed up time for exploratory testing. In terms of business, the effect is usually expressed in more stable releases and reduced feedback loop: defects and regressions are caught earlier, and QA and development spend less time on repetitive checks sprint after sprint.
When should businesses implement AI in testing?
AI is particularly effective in projects with frequent releases, complex logic, and high error costs. In practice, implementation begins with a pilot—analysis of current coverage and generation of additional tests for critical modules. For understanding the architecture and risks, it is useful to
→ sign up for a free consultation with EasyByte expert.
📌FAQ: frequently asked questions about AI in software testing
Question: What data does AI use to generate test cases?
Answer: Requirements, API specifications, bug history, user action logs, and data from previous test runs are used.
Question: How to estimate the volume and cost of implementing AI in testing?
Answer: The cost depends on the size of the code base, test maturity, and project goals. It is convenient to start with a preliminary estimate, for example,
→ using the EasyByte neural network development cost calculator.
Question: Can AI completely replace QA engineers?
Answer: No, AI complements QA by automating routine tasks and helping to find obscure scenarios.
Question: How quickly does AI adapt to code changes?
Answer: When integrated into CI/CD, AI automatically updates test cases with every change in application logic.
Question: Where is the best place to start implementing AI in software testing?
Answer: It is optimal to start with a pilot project and discuss the architecture with experts. To choose the appropriate format and avoid architectural errors, it is useful to sign up for a free consultation with an expert.