1 citations · 1 across the 4 of their papers we have counts for
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The Code Whisperer: LLM and Graph-Based AI for Smell and Vulnerability Resolution
Mohammad Baqar, Raji Rustamov, Alexander Hughes
Code smells and software vulnerabilities both increase maintenance cost, yet they are often handled by separate tools that miss structural context and produce noisy warnings. This…
The Rise of Agentic Testing: Multi-Agent Systems for Robust Software Quality Assurance
Saba Naqvi, Mohammad Baqar, Nawaz Ali Mohammad
Software testing has progressed toward intelligent automation, yet current AI-based test generators still suffer from static, single-shot outputs that frequently produce invalid, r…
RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data
Mohammad Baqar
Modern software teams frequently encounter delays in resolving recurring or related issues due to fragmented knowledge scattered across JIRA tickets, developer discussions, and Git…
Breaking Barriers in Software Testing: The Power of AI-Driven Automation
Saba Naqvi, Mohammad Baqar
Software testing remains critical for ensuring reliability, yet traditional approaches are slow, costly, and prone to gaps in coverage. This paper presents an AI-driven framework t…
AI-Augmented CI/CD Pipelines: From Code Commit to Production with Autonomous Decisions
Mohammad Baqar, Saba Naqvi, Rajat Khanda
Modern software delivery has accelerated from quarterly releases to multiple deployments per day. While CI/CD tooling has matured, human decision points interpreting flaky tests, c…
Self-Healing Software Systems: Lessons from Nature, Powered by AI
Mohammad Baqar, Rajat Khanda, Saba Naqvi
As modern software systems grow in complexity and scale, their ability to autonomously detect, diagnose, and recover from failures becomes increasingly vital. Drawing inspiration f…