activity
20232025
most citedTest Smell: A Parasitic Energy Consumer in Software Testing

4 citations · 11 across the 8 of their papers we have counts for

collaborators

8 papers

cs.SE2025

Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks

E. G. Santana, Gabriel Benjamin, Melissa Araujo +7

A growing variety of prompt engineering techniques has been proposed for Large Language Models (LLMs), yet systematic evaluation of each technique on individual software engineerin…

cs.SE2025

Consider What Humans Consider: Optimizing Commit Message Leveraging Contexts Considered By Human

Jiawei Li, David Faragó, Christian Petrov +1

Commit messages are crucial in software development, supporting maintenance tasks and communication among developers. While Large Language Models (LLMs) have advanced Commit Messag…

cs.SE2025

Enhancing LLM Performance Through Debate: An Empirical Study on Multi-Agent Debate for Coding Tasks

Yong Jin Chun, Qihong Chen, Jiawei Li +1

Large Language Models (LLMs) have advanced autonomous agents' planning and decision-making, yet they struggle with complex tasks requiring diverse expertise and multi-step reasonin…

cs.SE2025

Optimization is Better than Generation: Optimizing Commit Message Leveraging Human-written Commit Message

Jiawei Li, David Faragó, Christian Petrov +1

Commit messages are crucial in software development, supporting maintenance tasks and communication among developers. While Large Language Models (LLMs) have advanced Commit Messag…

cs.SE2024★ 1 cited

An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We?

Hyunjae Suh, Mahan Tafreshipour, Jiawei Li +2

Artificial Intelligence (AI) techniques, especially Large Language Models (LLMs), have started gaining popularity among researchers and software developers for generating source co…

cs.SE2024★ 3 cited

A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why?

QiHong Chen, Jiachen Yu, Jiawei Li +3

Recent advancements in Large Language Models (LLMs) have led to their widespread application in automated code generation. However, these models can still generate defective code t…

cs.SE2023★ 3 cited

Automated Repair of Declarative Software Specifications in the Era of Large Language Models

Md Rashedul Hasan, Jiawei Li, Iftekhar Ahmed +1

The growing adoption of declarative software specification languages, coupled with their inherent difficulty in debugging, has underscored the need for effective and automated repa…

cs.SE2023★ 4 cited

Test Smell: A Parasitic Energy Consumer in Software Testing

Md Rakib Hossain Misu, Jiawei Li, Adithya Bhattiprolu +3

Traditionally, energy efficiency research has focused on reducing energy consumption at the hardware level and, more recently, in the design and coding phases of the software devel…