4 papers
Coding in a Bubble? Evaluating LLMs in Resolving Context Adaptation Bugs During Code Adaptation
Tanghaoran Zhang, Xinjun Mao, Shangwen Wang +8
Code adaptation is a fundamental but challenging task in software development, requiring developers to modify existing code for new contexts. A key challenge is to resolve Context…
AdaptEval: A Benchmark for Evaluating Large Language Models on Code Snippet Adaptation
Tanghaoran Zhang, Xinjun Mao, Shangwen Wang +6
Recent advancements in large language models (LLMs) have automated various software engineering tasks, with benchmarks emerging to evaluate their capabilities. However, for adaptat…
BDiff: Block-aware and Accurate Text-based Code Differencing
Yao Lu, Wanwei Liu, Tanghaoran Zhang +7
Code differencing is a fundamental technique in software engineering practice and research. While researchers have proposed text-based differencing techniques capable of identifyin…
Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Tasks
Kang Yang, Xinjun Mao, Shangwen Wang +7
Pre-trained code models rely heavily on high-quality pre-training data, particularly human-written reference comments that bridge code and natural language. However, these comments…