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There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?
Correct Answer: B
AI and ML models can play a significant role in optimizing defect resolution processes. According to the ISTQB Certified Tester AI Testing (CT-AI) Syllabus, ML models can be used toanalyze defect reports, prioritize critical defects, and assign defects to developersbased on historical defect resolution patterns. The key AI applications for defect management include: * Defect Categorization- NLP techniques can analyze defect reports and classify them based on metadata like severity and impact. * Defect Prioritization- ML models trained on past defects can predict which issues are likely to cause failures, allowing teams toprioritizethe most critical issues. * Defect Assignment- AI-based models can suggest which developers are best suited for specific defects, optimizing the resolution process based on past performance and specialization. From the given answer choices: * Option A (Automatic Prioritization)is useful but does not directlyreduce backlog efficientlyby considering developer expertise and workload balancing. * Option C (Root Cause Analysis for Process Improvement)is along-term strategybut does not directly address backlog reduction. * Option D (Defect Prediction for Testing Focus)helps preemptively identify issues but does not resolve the existing backlog. Thus,Option Bis the best choice as it aligns with AI's capability toassign defects to the most suitable developersbased on historical data, ensuring efficient defect resolution and backlog reduction. Certified Tester AI Testing Study Guide References: * ISTQB CT-AI Syllabus v1.0, Section 11.2 (Using AI to Analyze Reported Defects) * ISTQB CT-AI Syllabus v1.0, Section 11.5 (Using AI for Defect Prediction).
