LLM-Powered Defect Triage: Intelligent Root Cause Analysis in Minutes
Defect triage is traditionally a time-intensive activity involving thorough manual inspection to uncover domain-specific defects. It involves rich contextual information, cross-referencing test artifacts, logs, and code changes - resulting in long turnaround times, high operational cost, and delayed release cycles. To revolutionize triage defects with minimal supervision, automation in troubleshooting through the use of Large Language Models (LLMs) is introduced.
Accompanying extensive reasoning data, traditional styles of triaging pose problems, too, such as scaling up to large volumes of defects, lack of link traces to code changes or test failures, and inconsistency in categorization. The adaptability of rule based automation frameworks is low because they were not built to generalize over varying non-spatial defect patterns and changing data schemata. We began by introducing LLMs into modern software engineering. We outline striking mark data derived from accomplishment's draws.
We uncovered hurdles stemming from the accumulation of incomplete or vague data. There were restraints in adaptation by developers. Set boundaries were fuelled when a precise, clear cut was presented. This shortened the span emerging to resting form. It empowered required insight aids. These aids derived shape improving analysis depth.
Utsav Patel
UTSAV PATEL, PhD - Sr. Manager/Architect, Test Automation Engineering
With over 14 years of rich experience in software quality engineering and test automation, Utsav Patel is a pioneering force in transforming traditional QA practices into intelligent, AI-powered ecosystems. A PhD in Technology and Innovation Management, Utsav currently leads Quality Engineering at FEI Systems, driving excellence across 18+ QA teams with a sharp focus on Generative AI, Large Language Models (LLMs), and NLP technologies.
Utsav has architected AI-driven automation frameworks that not only self-heal but also intelligently generate test cases and predict defects, drastically improving coverage and reducing manual effort. His work in computer vision, synthetic data generation, and performance engineering sets new benchmarks in intelligent quality validation.
A strong advocate for Quality Intelligence (QI), Utsav integrates strategic quality initiatives with cutting-edge tools like UFT, Selenium, Katalon, Jenkins, and Azure DevOps-creating a seamless fusion of innovation, scalability, and compliance (HIPAA, SOC2). His patented solutions and published research reflect his commitment to advancing the quality landscape through data governance and AI enablement.
As a thought leader and mentor, Utsav continues to inspire teams to rethink quality-not just as a checkpoint, but as a continuously evolving intelligence engine.