activity
20232025
most citedCan LLMs Replace Manual Annotation of Software Engineering Artifacts?

3 citations · 7 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2025

Leveraging Big Data Frameworks for Spam Detection in Amazon Reviews

Mst Eshita Khatun, Halima Akter, Tasnimul Rehan +1

In this digital era, online shopping is common practice in our daily lives. Product reviews significantly influence consumer buying behavior and help establish buyer trust. However…

cs.SE2025

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…

cs.SE2024

Prompting and Fine-tuning Large Language Models for Automated Code Review Comment Generation

Md. Asif Haider, Ayesha Binte Mostofa, Sk. Sabit Bin Mosaddek +2

Generating accurate code review comments remains a significant challenge due to the inherently diverse and non-unique nature of the task output. Large language models pretrained on…

cs.SE2024★ 3 cited

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…

cs.SE2024★ 2 cited

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…

cs.SE2024

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,…

cs.SE2023★ 2 cited

Occlusion-based Detection of Trojan-triggering Inputs in Large Language Models of Code

Aftab Hussain, Md Rafiqul Islam Rabin, Toufique Ahmed +2

Large language models (LLMs) are becoming an integrated part of software development. These models are trained on large datasets for code, where it is hard to verify each data poin…