Urvish Gajjar
Sr Test Manager
Health Care Service Corporation, Chicago · US
0009-0000-1420-5478
2
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About
I have 12+ years of experience in computer engineering, software quality assurance, and department leadership. To highlight how my specific expertise aligns with the needs of a peer reviewer or session chair, here is a brief summary of my background: Current Leadership Role: I serve as a Senior Test Manager at Blue Cross Blue Shield Texas, where I lead a team of 5 test managers across integrated product delivery lines. I oversee quality strategy, release management, and cross-functional coordination with stakeholders and directors . AI & Technical Expertise: My recent work focuses heavily on leading QA for cutting-edge projects, including implementing AI search experiences and platforms using OpenAI and Microsoft Co-pilot. Scalability & Scale Experience: Throughout my career at Fortune 500 and mid-sized companies, I have scaled a QA department from 3 to 52 engineers and managed major product releases serving over 15 million daily active users. Academic Background: I hold an M.S. in IT Management and an M.S. in Engineering. Given my strong background in AI implementation, agile frameworks, and large-scale technical operations, I am well-equipped to provide rigorous, constructive feedback on technical papers or to manage event tracks efficiently.
Research Interests
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Published Papers
https://doi.org/10.64823/ijter.2506008
The integration of large language models (LLMs) into mobile applications introduces adaptive tutoring, conversational question answering, and automated feedback generation, but it also breaks the deterministic input-output assumptions on which conventional mobile test automation relies. This paper proposes an AI-augmented testing framework for LLM-integrated e-learning mobile applications that combines automated test-case generation, a hybrid test oracle built from semantic similarity scoring and LLM-as-a-judge rubric evaluation, and cross-platform mobile UI execution using Appium. We present a layered system architecture that situates the AI test harness as a first-class component alongside the prompt orchestrator, retrieval-augmented knowledge base, and application microservices. We further describe an implementation using Python, pytest, sentence-transformer embeddings, and Appium WebDriver, and report an empirical evaluation comparing manual/scripted testing against the proposed approach across authoring effort, regression-cycle duration, defect-escape rate, and oracle false-negative rate. Results indicate substantial reductions in authoring time and regression duration alongside improved defect detection, while highlighting open challenges in oracle calibration and non-determinism of LLM outputs.
https://doi.org/10.64823/ijter.2501016
Quality assurance (QA) organizations increasingly rely on multiple disconnected tools — test management systems, issue trackers, and continuous integration (CI) pipelines — to plan, execute, and report on testing activity. This fragmentation makes it difficult for QA managers to obtain a timely, unified view of test coverage, defect leakage, and automation maturity. This paper presents a solution architecture that integrates qTest (test case management and execution), Jira (sprint and defect tracking), and Grafana (visualization and alerting) into a single QA test-matrix dashboard. We define a metrics framework covering seven manager-facing indicators — test coverage, missing test coverage, production bugs, in-sprint bugs, in-sprint automation, regression automation, and sprint predictability — and describe the data pipeline, panel design, and alerting rules required to compute them. We further present an illustrative deployment based on a synthetic eight-sprint dataset to demonstrate how the dashboard surfaces coverage gaps and regression debt earlier than manual reporting. The proposed approach reduces manual reporting effort, improves defect-leakage visibility, and gives QA managers an evidence-based basis for test-strategy decisions. We discuss implementation considerations, limitations, and directions for extending the framework with predictive analytics.