TestFly’s Start Smart, Scale Strategically, Win Big Methodology

 

Step 1: Identify a High-Impact Starting Point

AI integration starts with something simple, visible, and high-frequency - typically, a task that is repetitive, data-rich, and takes valuable time away from human testers. We have provided examples of typical AI areas below as use cases.

Use Case: Automating Smoke Tests as we begin with the basics:

  • Automating game startup, menu navigation, level loading, and shutdown

  • Using rule-based or computer vision models to confirm successful operations

  • Measuring the number of successful and failed test cycles handled by AI versus manual testers

Why this works:

  • Low technical barrier to entry

  • Quick measurable wins

  • Builds confidence with devs and producers

  • Opens the path for automated regression testing

Critical Success Factors:

  • Percentage of smoke tests automated

  • Time saved per test run

  • Accuracy of AI-detected issues vs. manual detection

  • Team adoption rate and feedback from developers

Step 2: Use Early Wins to Build Momentum

Once the AI automation proves itself, we capitalize on the results. Teams share key metrics (e.g., “smoke tests now take 4 minutes vs. 20”), gather cross-functional feedback, and iterate fast. This momentum builds stakeholder trust and clears the way for more advanced tooling. We have provided examples of typical AI areas below as use cases.

Use Case: Regression Suite Expansion

  • Build upon the initial smoke test automation to expand into broader regression coverage

  • Automate high-priority gameplay sequences and system checks

Why this works:

  • Builds on already validated processes

  • Encourages wider team collaboration

  • Creates momentum for future AI investment

  • Populates data for ML processing

Critical Success Factors:

  • Increase in automated regression test coverage

  • Reduction in manual testing effort across builds

  • Reduction in escaped bugs (bugs found post-release)

  • Speed of QA cycle turnaround

Step 3: Expand Into Smart, Scalable AI Use Cases

With a solid foundation, we guide clients into deeper AI territory; we have provided examples of typical AI areas below as use cases:

Use Case 1: AI-Driven Level Completion Testing

  • Rule-based or ML agents auto-play through levels

  • Monitor player flow, transitions, and critical path coverage

  • Flag bugs like geometry issues, loading stalls, and softlocks

Why this works:

  • Scales testing efficiently without requiring manual playthroughs

  • Increases confidence in level design and progression

Critical Success Factors:

  • Number of levels successfully auto-played

  • Bugs discovered by AI agents vs. humans

  • Time reduction in full level test passes

Use Case 2: Reinforcement Learning for Edge-Case Discovery

  • AI agents learn by doing, improving with each run

  • Simulate unpredictable player behavior

  • Surface hard-to-reach bugs or exploitable paths

Why this works:

  • Identifies edge cases humans may not anticipate

  • Improves overall game robustness

Critical Success Factors:

  • Number of unique edge-case bugs discovered

  • Reusability of learned AI test agents across builds

  • Developer prioritization of AI-found issues

 

Use Case 3: Natural Language Processing for Bug Triage

  • NLP models auto-tag bug reports by severity, area, or duplication

  • Prioritize issues for devs, reducing manual triage overhead

Why this works:

  • Reduces QA triage time significantly

  • Ensures critical bugs are surfaced faster

Critical Success Factors:

  • Time saved in bug triage process

  • Reduction in misclassified or duplicate bug reports

  • Accuracy of NLP prioritization compared to human QA

Step 4: Mature the AI QA Ecosystem

By now, AI has earned its place in your QA pipeline. The final step is integration and we have provided examples of typical AI areas below as use cases:

Use Case: Predictive QA and Autonomous Testing Agents

  • Sync AI systems with CI/CD pipelines to enable real-time quality insights

  • Leverage live player telemetry and in-game analytics to dynamically guide test focus

  • Deploy autonomous AI agents that adaptively test high-risk areas based on prediction models

  • Use machine learning to forecast bug clusters, progression blockers, or balance issues before they surface

Why this works:

  • Proactively enhances QA coverage by anticipating where bugs are likely to occur

  • Enables continuous testing cycles with AI that scales alongside development

  • Reduces reliance on rigid, pre-scripted test plans by allowing agents to explore adaptively

Critical Success Factors:

  • Accuracy of predicted defect areas vs. actual in-game issues

  • Rate of coverage increase from autonomous agents

  • Reduction in testing blind spots

  • Speed and reliability of integration with CI/CD environments

  • Time from prediction to actionable test plan updates

 
Jacob Ferguson