5 papers
How Do LLMs Read Bug Reports? An Empirical Study of Attention in LLMs for Automated Program Repair
Ramtin Ehsani, Irene Manotas, Saurabh Pujar +2
Large Language Model (LLM)-based Automated Program Repair systems are advancing rapidly, yet their performance remains inconsistent. Even when provided with the same contextual inf…
Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Study
Ira Ceka, Hailie Mitchell, Saurabh Pujar +5
Automated Program Repair (APR) agents leverage Large Language Models (LLMs) to autonomously diagnose and fix software bugs through reasoning, planning, and tool use. Despite impres…
Cross-lingual Transfer in Programming Languages: An Extensive Empirical Study
Razan Baltaji, Saurabh Pujar, Louis Mandel +3
Large language models (LLMs) have achieved state-of-the-art performance in various software engineering tasks, including error detection, clone detection, and code translation, pri…
SeaView: Software Engineering Agent Visual Interface for Enhanced Workflow
Timothy Bula, Saurabh Pujar, Luca Buratti +2
Auto-regressive LLM-based software engineering (SWE) agents, henceforth SWE agents, have made tremendous progress (>60% on SWE-Bench Verified) on real-world coding challenges inclu…
Insights from the Usage of the Ansible Lightspeed Code Completion Service
Priyam Sahoo, Saurabh Pujar, Ganesh Nalawade +3
The availability of Large Language Models (LLMs) which can generate code, has made it possible to create tools that improve developer productivity. Integrated development environme…