Learn what information software testers must gather before starting testing a new product, including AI, automation, compatibility, and release planning.
In my years of experience in software testing, I’ve observed a recurring challenge—incomplete product information at the start of testing. This issue is more prominent for freshers, junior testers, and newly appointed test leads.
With globally distributed teams, overlapping meetings, different time zones, and fragmented communication, testers/QA often miss critical updates. Important product details remain scattered across emails, tools, and meetings, making it difficult to get a single source of truth.
Based on my experience, I’ve compiled a comprehensive checklist of key questions every software tester should ask before starting testing on a new product, including AI and AI-agent–related considerations, which are becoming increasingly important in today’s software ecosystem.
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| Testing a New Product Guide |
1. Product Documentation & Functional Understanding
- Is the functional specification of the product available?
- Are there user manuals, admin guides, videos, or hands-on documents?
- Is there a centralized knowledge base or wiki?
- If the product uses AI features, is there documentation explaining:
- AI behaviour
- Input/output expectations
- Limitations and known constraints?
2. New Features & Release Scope
- What are the new features introduced in the current release?
Are any of these features:
AI-driven?- Rule-based vs model-based?
- What is the target product version for this release?
3. Project Timelines & Milestones
What are the key milestones?
- Feature Freeze
- System Integration (SI)
- Release Candidate (RC)
- Release to Market (RTM)
- EAR (w.r.t Mobile App release)
- GA (w.r.t Mobile App release)
- Canary Info (w.r.t Mobile App release)
- Are there AI model freeze dates separate from code freeze?
- Are there planned model re-training schedules?
4. Supported Platforms & Environments
- Supported Operating Systems (32-bit / 64-bit / Arm)?
- Supported Browsers and versions?
- Supported Mobile devices and OS versions?
- Supported languages (MUI)?
- Does AI behaviour vary across platforms or locales?
5. Build, Deployment & Model Delivery
- Where is the application build located?
- Which branch should be used (main, master, development, staging, release)?
How are builds delivered?
- Jenkins
- Testflight
- Internal repositories
If AI is involved:
- Are models packaged with the build or deployed separately?
- Are model versions tracked and documented?
6. Agile & Work Tracking Tools
Which tool is used to manage user stories and tasks?
- JIRA (on-prem instance or cloud instance)
- Azure DevOps
- AtTask
- Confluence (on-prem instance or cloud instance)
- Are AI stories clearly labeled (e.g., data change, model update)?
- Are test cases linked to AI acceptance criteria?
7. Upgrade, Downgrade & Compatibility Scenarios
- Is build-to-build upgrade supported?
- Is upgrade from previous versions supported?
- Is downgrade allowed?
- Compatibility questions:
- Can older clients work with a newer server?
- Can newer clients connect to older servers?
- For AI systems:
- Is model backward compatibility supported?
- How does the system behave if a model version changes?
8. High-Risk Areas & AI-Sensitive Modules
- Which modules need maximum testing effort?
Are there AI components where:
- Output may vary for the same input?
- Decisions impact users directly?
- Are there confidence thresholds or fallback mechanisms?
9. Bug Reporting & Defect Management Guidelines
- Are there bug reporting standards?
Important parameters to clarify:
- Found-in release
- Target release
- Severity definitions
- Developer or module owner contacts
- Default bug assignment to whom(Dev/Manager name)
- For AI-related bugs:
- Is this a data issue, model issue, or logic issue?
- Is the behaviour non-deterministic but acceptable?
10. Third-Party Integrations
Does the product integrate with:
- External APIs
- AI services (e.g., NLP, Vision, Recommendation engines)?
- Are sandbox or mock services available?
- Are rate limits and API failures handled gracefully?
11. Test Automation & AI-Assisted Testing
- Are automation scripts available?
- Which tools or frameworks are used?
Is the product suitable for:
- AI-based test case generation?
- Self-healing test automation?
- Are AI features testable via APIs or only via UI?
12. AI & AI Agent–Specific Questions Every Tester Should Ask
With the rise of AI agents and intelligent systems, testers must gather additional information:
AI Architecture & Behaviour
Is the system using:
- Rule-based logic?
- Machine learning models?
- Autonomous AI agents?
- What decisions are made by AI vs human logic?
- Data & Training
- What data is used to train the model?
- Is test data synthetic or production-like?
- How often is the model retrained?
Explainability & Observability
- Can AI decisions be explained or logged?
- Are confidence scores or reasoning available?
- Is there an audit trail for AI actions?
Ethics, Bias & Compliance
Are there checks for:
- Bias
- Fairness
- Hallucinations (for generative AI)?
- Is the system compliant with data privacy and security guidelines?
- Fallback & Safety Mechanisms
- What happens if AI fails or gives low confidence?
- Is there a manual override or fallback logic?
- Are guardrails defined for AI agents?
Why This Checklist Matters More Than Ever
Modern applications are no longer just rule-based—they are intelligent, adaptive, and data-driven. Without proper information gathering:
- AI bugs may go unnoticed
- Test results may appear inconsistent
- Critical risks may reach production
Early clarity ensures:
- Better test coverage
- Reduced ambiguity
- Faster onboarding
- Improved collaboration between QA, Dev, and Data teams
Final Thoughts
This checklist has helped me start testing new products with confidence, especially in projects involving AI and AI agents. While every product differs, asking the right questions at the right time makes a significant difference.
👉 I invite you to share your experience
What additional questions do you ask when testing software or AI-driven products? Please share your thoughts in the comments section.
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