Publications (25)
Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)
Toufique Ahmed, Kunal Suresh Pai, Premkumar Devanbu +1
Large Language Models (LLM) are a new class of computation engines, "programmed" via prompt engineering. We are still learning how to best "program" these LLMs to help developers.…
Few-shot training LLMs for project-specific code-summarization
Toufique Ahmed, Premkumar Devanbu
Very large language models (LLMs), such as GPT-3 and Codex have achieved state-of-the-art performance on several natural-language tasks, and show great promise also for code. A par…
Multilingual training for Software Engineering
Toufique Ahmed, Premkumar Devanbu
Well-trained machine-learning models, which leverage large amounts of open-source software data, have now become an interesting approach to automating many software engineering tas…
Studying LLM Performance on Closed- and Open-source Data
Toufique Ahmed, Christian Bird, Premkumar Devanbu +1
Large Language models (LLMs) are finding wide use in software engineering practice. These models are extremely data-hungry, and are largely trained on open-source (OSS) code distri…
BugSwarm: Mining and Continuously Growing a Dataset of Reproducible Failures and Fixes
David A. Tomassi, Naji Dmeiri, Yichen Wang +5
Fault-detection, localization, and repair methods are vital to software quality; but it is difficult to evaluate their generality, applicability, and current effectiveness. Large,…
A Survey of Machine Learning for Big Code and Naturalness
Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu +1
Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of…