14 citations · 102 across the 23 of their papers we have counts for
23 papers
Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time
Razvan Mihai Popescu, David Gros, Andrei Botocan +3
The rise of large language models for code has reshaped software development. Autonomous coding agents, able to create branches, open pull requests, and perform code reviews, now a…
CoDocBench: A Dataset for Code-Documentation Alignment in Software Maintenance
Kunal Pai, Premkumar Devanbu, Toufique Ahmed
One of the central tasks in software maintenance is being able to understand and develop code changes. Thus, given a natural language description of the desired new operation of a…
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,…
Towards Understanding What Code Language Models Learned
Toufique Ahmed, Dian Yu, Chengxuan Huang +3
Pre-trained language models are effective in a variety of natural language tasks, but it has been argued their capabilities fall short of fully learning meaning or understanding la…
Better patching using LLM prompting, via Self-Consistency
Toufique Ahmed, Premkumar Devanbu
Large Language models (LLMs) can be induced to solve non-trivial problems with "few-shot" prompts including illustrative problem-solution examples. Now if the few-shots also includ…
A Survey of Trojans in Neural Models of Source Code: Taxonomy and Techniques
Aftab Hussain, Md Rafiqul Islam Rabin, Toufique Ahmed +4
In this work, we study literature in Explainable AI and Safe AI to understand poisoning of neural models of code. In order to do so, we first establish a novel taxonomy for Trojan…
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.…
Large Language Models and Simple, Stupid Bugs
Kevin Jesse, Toufique Ahmed, Premkumar T. Devanbu +1
With the advent of powerful neural language models, AI-based systems to assist developers in coding tasks are becoming widely available; Copilot is one such system. Copilot uses Co…
Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries
Ali Al-Kaswan, Toufique Ahmed, Maliheh Izadi +3
Reverse engineering binaries is required to understand and analyse programs for which the source code is unavailable. Decompilers can transform the largely unreadable binaries into…
NatGen: Generative pre-training by "Naturalizing" source code
Saikat Chakraborty, Toufique Ahmed, Yangruibo Ding +2
Pre-trained Generative Language models (e.g. PLBART, CodeT5, SPT-Code) for source code yielded strong results on several tasks in the past few years, including code generation and…
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…
Learning code summarization from a small and local dataset
Toufique Ahmed, Premkumar Devanbu
Foundation models (e.g., CodeBERT, GraphCodeBERT, CodeT5) work well for many software engineering tasks. These models are pre-trained (using self-supervision) with billions of code…
Recent formation and likely cometary activity of near-Earth asteroid pair 2019 PR2 -- 2019 QR6
Petr Fatka, Nicholas A. Moskovitz, Petr Pravec +13
Asteroid pairs are genetically related asteroids that recently separated (few million years), but still reside on similar heliocentric orbits. A few hundred of these systems hav…
SYNFIX: Automatically Fixing Syntax Errors using Compiler Diagnostics
Toufique Ahmed, Noah Rose Ledesma, Premkumar Devanbu
Beginning programmers struggle with the complex grammar of modern programming languages like Java, and make lot of syntax errors. The diagnostic syntax error messages from compiler…
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…
Code to Comment "Translation": Data, Metrics, Baselining & Evaluation
David Gros, Hariharan Sezhiyan, Prem Devanbu +1
The relationship of comments to code, and in particular, the task of generating useful comments given the code, has long been of interest. The earliest approaches have been based o…
Patching as Translation: the Data and the Metaphor
Yangruibo Ding, Baishakhi Ray, Premkumar Devanbu +1
Machine Learning models from other fields, like Computational Linguistics, have been transplanted to Software Engineering tasks, often quite successfully. Yet a transplanted model'…