5 citations · 8 across the 11 of their papers we have counts for
11 papers
Static Program Slicing Using Language Models With Dataflow-Aware Pretraining and Constrained Decoding
Pengfei He, Shaowei Wang, Tse-Hsun Chen +1
Static program slicing is a fundamental software engineering technique for isolating code relevant to specific variables. While recent learning-based approaches using language mode…
CI-Repair-Bench: A Repository-Aware Benchmark for Automated Patch Validation via CI Workflows
Rabeya Khatun Muna, Md Nakhla Rafi, Tse-Hsun +1
Continuous Integration (CI) enforces repository-level correctness through multi-stage workflows and is central to modern software development, yet diagnosing and repairing CI failu…
When LLMs Lag Behind: Knowledge Conflicts from Evolving APIs in Code Generation
Ahmed Nusayer Ashik, Shaowei Wang, Tse-Hsun Chen +2
The rapid evolution of software libraries creates a significant challenge for Large Language Models (LLMs), whose static parametric knowledge often becomes stale post-training. Whi…
A Survey of Code Review Benchmarks and Evaluation Practices in Pre-LLM and LLM Era
Taufiqul Islam Khan, Shaowei Wang, Haoxiang Zhang +1
Code review is a critical practice in modern software engineering, helping developers detect defects early, improve code quality, and facilitate knowledge sharing. With the rapid a…
SLICET5: Static Program Slicing using Language Models with Copy Mechanism and Constrained Decoding
Pengfei He, Shaowei Wang, Tse-Hsun Chen
Static program slicing is a fundamental technique in software engineering. Traditional static slicing tools rely on parsing complete source code, which limits their applicability t…
Studying the Impact of Early Test Termination Due to Assertion Failure on Code Coverage and Spectrum-based Fault Localization
Md. Ashraf Uddin, Shaowei Wang, An Ran Chen +3
An assertion is commonly used to validate the expected programs behavior (e.g., if the returned value of a method equals an expected value) in software testing. Although it is a re…
CODEPROMPTZIP: Code-specific Prompt Compression for Retrieval-Augmented Generation in Coding Tasks with LMs
Pengfei He, Shaowei Wang, Tse-Hsun Chen
Retrieval-Augmented Generation (RAG) enhances coding tasks by incorporating retrieved code examples into prompts. However, lengthy prompts, often exceeding tens of thousands of tok…
Evaluating the Effectiveness and Efficiency of Demonstration Retrievers in RAG for Coding Tasks
Pengfei He, Shaowei Wang, Shaiful Chowdhury +1
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external knowledge bases, achieving state-of-the-art results in various coding tasks. The…
A Comprehensive Framework for Evaluating API-oriented Code Generation in Large Language Models
Yixi Wu, Pengfei He, Zehao Wang +3
Large language models (LLMs) like GitHub Copilot and ChatGPT have emerged as powerful tools for code generation, significantly enhancing productivity and accelerating software deve…
ZS4C: Zero-Shot Synthesis of Compilable Code for Incomplete Code Snippets using LLMs
Azmain Kabir, Shaowei Wang, Yuan Tian +3
Technical Q&A sites are valuable for software developers seeking knowledge, but the code snippets they provide are often uncompilable and incomplete due to unresolved types and mis…
Studying and Recommending Information Highlighting in Stack Overflow Answers
Shahla Shaan Ahmed, Shaowei Wang, Yuan Tian +3
Context: Navigating the knowledge of Stack Overflow (SO) remains challenging. To make the posts vivid to users, SO allows users to write and edit posts with Markdown or HTML so tha…