6 citations · 6 across the 3 of their papers we have counts for
6 papers
Distilling Reasoning Traces into Advisory Prompts for Software Engineering Tasks
Faizan Faisal, Prem Devanbu, Toufique Ahmed
Language models are widely used for generating and otherwise processing code (e.g., identifying code hallucinations, possible inputs, or predicting outputs); however, LLMs can make…
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…
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…
Learning to Find Usages of Library Functions in Optimized Binaries
Toufique Ahmed, Premkumar Devanbu, Anand Ashok Sawant
Much software, whether beneficent or malevolent, is distributed only as binaries, sans source code. Absent source code, understanding binaries' behavior can be quite challenging, e…
Review4Repair: Code Review Aided Automatic Program Repairing
Faria Huq, Masum Hasan, Mahim Anzum Haque Pantho +3
Context: Learning-based automatic program repair techniques are showing promise to provide quality fix suggestions for detected bugs in the source code of the software. These tools…
Learning Lenient Parsing & Typing via Indirect Supervision
Toufique Ahmed, Premkumar Devanbu, Vincent Hellendoorn
Both professional coders and teachers frequently deal with imperfect (fragmentary, incomplete, ill-formed) code. Such fragments are common in STACKOVERFLOW; students also frequentl…