14 citations · 102 across the 23 of their papers we have counts for
7 papers · 1 filter
Can LLMs Replace Manual Annotation of Software Engineering Artifacts?
Toufique Ahmed, Premkumar Devanbu, Christoph Treude +1
Experimental evaluations of software engineering innovations, e.g., tools and processes, often include human-subject studies as a component of a multi-pronged strategy to obtain gr…
Trojans in Large Language Models of Code: A Critical Review through a Trigger-Based Taxonomy
Aftab Hussain, Md Rafiqul Islam Rabin, Toufique Ahmed +3
Large language models (LLMs) have provided a lot of exciting new capabilities in software development. However, the opaque nature of these models makes them difficult to reason abo…
Calibration of Large Language Models on Code Summarization
Yuvraj Virk, Premkumar Devanbu, Toufique Ahmed
A brief, fluent, and relevant summary can be helpful during program comprehension; however, such a summary does require significant human effort to produce. Often, good summaries a…
Robustness, Security, Privacy, Explainability, Efficiency, and Usability of Large Language Models for Code
Zhou Yang, Zhensu Sun, Terry Zhuo Yue +2
Large language models for code (LLM4Code), which demonstrate strong performance (e.g., high accuracy) in processing source code, have significantly transformed software engineering…
RepairAgent: An Autonomous, LLM-Based Agent for Program Repair
Islem Bouzenia, Premkumar Devanbu, Michael Pradel
Automated program repair has emerged as a powerful technique to mitigate the impact of software bugs on system reliability and user experience. This paper introduces RepairAgent, t…
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…
Calibration and Correctness of Language Models for Code
Claudio Spiess, David Gros, Kunal Suresh Pai +6
Machine learning models are widely used, but can also often be wrong. Users would benefit from a reliable indication of whether a given output from a given model should be trusted,…