54 citations · 65 across the 13 of their papers we have counts for
14 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 patch software bugs using planning, reasoning, and tools. Although these ag…
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…
Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Models
Sanjay Vishwakarma, Francis Harkins, Siddharth Golecha +7
Quantum programs are typically developed using quantum Software Development Kits (SDKs). The rapid advancement of quantum computing necessitates new tools to streamline this develo…
Qiskit Code Assistant: Training LLMs for generating Quantum Computing Code
Nicolas Dupuis, Luca Buratti, Sanjay Vishwakarma +5
Code Large Language Models (Code LLMs) have emerged as powerful tools, revolutionizing the software development landscape by automating the coding process and reducing time and eff…
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…
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…
Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain
Marcus J. Min, Yangruibo Ding, Luca Buratti +4
Code Large Language Models (Code LLMs) are being increasingly employed in real-life applications, so evaluating them is critical. While the conventional accuracy evaluates the perf…
CONCORD: Clone-aware Contrastive Learning for Source Code
Yangruibo Ding, Saikat Chakraborty, Luca Buratti +4
Deep Learning (DL) models to analyze source code have shown immense promise during the past few years. More recently, self-supervised pre-training has gained traction for learning…
Automated Code generation for Information Technology Tasks in YAML through Large Language Models
Saurabh Pujar, Luca Buratti, Xiaojie Guo +8
The recent improvement in code generation capabilities due to the use of large language models has mainly benefited general purpose programming languages. Domain specific languages…
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts
Yangruibo Ding, Luca Buratti, Saurabh Pujar +3
Understanding the functional (dis)-similarity of source code is significant for code modeling tasks such as software vulnerability and code clone detection. We present DISCO(DIS-si…
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
Ruchir Puri, David S. Kung, Geert Janssen +14
Over the last several decades, software has been woven into the fabric of every aspect of our society. As software development surges and code infrastructure of enterprise applicat…
D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis
Yunhui Zheng, Saurabh Pujar, Burn Lewis +6
Static analysis tools are widely used for vulnerability detection as they understand programs with complex behavior and millions of lines of code. Despite their popularity, static…
Exploring Software Naturalness through Neural Language Models
Luca Buratti, Saurabh Pujar, Mihaela Bornea +8
The Software Naturalness hypothesis argues that programming languages can be understood through the same techniques used in natural language processing. We explore this hypothesis…