138 citations · 477 across the 59 of their papers we have counts for
11 papers · 1 filter
Students' Perspective on AI Code Completion: Benefits and Challenges
Wannita Takerngsaksiri, Cleshan Warusavitarne, Christian Yaacoub +2
AI Code Completion (e.g., GitHub's Copilot) has revolutionized how computer science students interact with programming languages. However, AI code completion has been studied from…
Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey
Xinyu She, Yue Liu, Yanjie Zhao +5
Modern language models (LMs) have been successfully employed in source code generation and understanding, leading to a significant increase in research focused on learning-based co…
ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We?
Michael Fu, Chakkrit Tantithamthavorn, Van Nguyen +1
Large language models (LLMs) like ChatGPT (i.e., gpt-3.5-turbo and gpt-4) exhibited remarkable advancement in a range of software engineering tasks associated with source code such…
Unit Testing Challenges with Automated Marking
Chakkrit Tantithamthavorn, Norman Chen
Teaching software testing presents difficulties due to its abstract and conceptual nature. The lack of tangible outcomes and limited emphasis on hands-on experience further compoun…
Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challenges
Aastha Pant, Rashina Hoda, Simone V. Spiegler +2
Ethics in AI has become a debated topic of public and expert discourse in recent years. But what do people who build AI - AI practitioners - have to say about their understanding o…
Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues
Yue Liu, Thanh Le-Cong, Ratnadira Widyasari +4
We systematically study the quality of 4,066 ChatGPT-generated code implemented in two popular programming languages, i.e., Java and Python, for 2,033 programming tasks. The goal o…
Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities
Michael Fu, Trung Le, Van Nguyen +2
Deep learning (DL) models have become increasingly popular in identifying software vulnerabilities. Prior studies found that vulnerabilities across different vulnerable programs ma…
AIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilities
Michael Fu, Chakkrit Tantithamthavorn, Trung Le +4
Many ML-based approaches have been proposed to automatically detect, localize, and repair software vulnerabilities. While ML-based methods are more effective than program analysis-…
A3Test: Assertion-Augmented Automated Test Case Generation
Saranya Alagarsamy, Chakkrit Tantithamthavorn, Aldeida Aleti
Test case generation is an important activity, yet a time-consuming and laborious task. Recently, AthenaTest -- a deep learning approach for generating unit test cases -- is propos…
A Systematic Literature Review of Explainable AI for Software Engineering
Ahmad Haji Mohammadkhani, Nitin Sai Bommi, Mariem Daboussi +3
Context: In recent years, leveraging machine learning (ML) techniques has become one of the main solutions to tackle many software engineering (SE) tasks, in research studies (ML4S…
On the Reliability and Explainability of Language Models for Program Generation
Yue Liu, Chakkrit Tantithamthavorn, Yonghui Liu +1
Recent studies have adopted pre-trained language models, such as CodeT5 and CodeGPT, for automated program generation tasks like code generation, repair, and translation. Numerous…