Can more than 1 AI tool be used for same project/application?
AI is best for repetitive tasks like generating CRUD tests, regression scaffolding, selector suggestions, and running large volumes of checks. Humans should handle intent, risk decisions, exploratory testing, UX considerations, and safety or legal validations. AI executes; humans decide what matters.
Organizations can balance automation with human judgment by using a tiered trust model: let machines handle low-risk tasks, while humans review high-impact decisions. Set confidence thresholds, require human checks for uncertain outputs, and maintain clear audit trails to understand recommendations before accepting them.
Organizations can systematically test and secure generated code by treating it like any external contribution: use static analysis, linters, security checks, unit tests, and sandboxed integration tests. Enforce human code reviews and CI gates, add property-based tests or fuzzing where needed, and maintain versioning and metadata for each snippet.
To minimize risks and ensure robustness in LLM-generated automation scripts, combine standard software engineering practices like CI/CD, testing, and code reviews with ML-specific checks such as dataset and prompt versioning, deterministic replay of generated actions, model-conformance tests, and running new scripts in shadow mode before full deployment.
AI can help identify flaky tests, highlight high-risk test paths, create realistic test data variations, reproduce bugs reliably, and summarize long failure logs, letting testers focus on complex scenarios and strategic testing.
Morning: review the prioritized risk list and flagged regressions. Midday: work on complex tests using suggestions for selectors and data variations. Afternoon: validate test results, label any false positives, and guide teammates on tricky scenarios. End: push reliable changes while letting the system handle broader coverage, with humans ensuring depth and accuracy.
Full AI works well for repetitive tasks like trivial UI navigation, standard API checks, and generating synthetic load cases. Human oversight is still needed for defining acceptance criteria, security-sensitive flows, ethical considerations, and areas requiring domain expertise such as finance or healthcare.
To prepare teams and infrastructure for a gradual shift towards AI-centric test automation, invest in observability, dataset and version control, model governance, and experiment pipelines. Implement a human-in-the-loop review process, start with pilots in low-risk areas, instrument all processes, and train teams to interpret automation insights effectively.
QA can detect inconsistencies in the RAG pipeline by setting up strong observability, using dataset and version control, applying model governance, running experiments, and including a human review step. Start with low-risk pilots, track everything carefully, and train teams to interpret the system’s signals.
Use clustering of queries and multi-run comparisons: run the same prompt with slight paraphrases and compare retrieved sources + answers; flag high variance. Store provenance and implement a consistency score — anything below threshold goes to human review.
The best processes to ensure quality and security of generated automation scripts include code reviews, security scans, enforcing test coverage, sandboxed execution, and gradual rollouts. Adding a “provenance + confidence” header with model/version/prompt context helps reviewers understand the source and reliability of the code.
Yes — humans will focus more on exploratory testing, risk assessment, and complex cross-system scenarios. The role shifts from writing repetitive checks to designing creative tests and guiding overall quality strategy.
AI should be trusted with limited control, handling non-critical tasks or suggesting actions, while humans review and approve decisions in critical systems. Full automation without human oversight is not advisable.
Every organization is different. In my experience, about 10–30% of test execution and triage is initially driven by AI, with autonomy mostly limited to running tests and reporting results. Ownership increases as trust and monitoring improve, but rarely exceeds 50% for critical areas.
Prompt-based testing works well for quickly generating test cases, identifying obvious gaps, and creating clear, human-readable test ideas. Manual implementation is better when you need precise control, predictable performance, or strict compliance with security policies.
SDETs can streamline debugging by summarizing stack traces, linking errors to recent commits, identifying likely root causes, and outlining clear reproduction steps. They then verify these insights and provide precise feedback to developers, reducing overall time-to-fix.