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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…
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…
Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering
Tanghaoran Zhang, Yue Yu, Xinjun Mao +5
Code snippet adaptation is a fundamental activity in the software development process. Unlike code generation, code snippet adaptation is not a "free creation", which requires deve…
Enhancing Code Intelligence Tasks with ChatGPT
Kang Yang, Xinjun Mao, Shangwen Wang +6
Pre-trained code models have emerged as crucial tools in various code intelligence tasks. However, their effectiveness depends on the quality of the pre-training dataset, particula…
ROS package search for robot software development: a knowledge graph-based approach
Shuo Wang, Xinjun Mao, Shuo Yang +2
ROS (Robot Operating System) packages have become increasingly popular as a type of software artifact that can be effectively reused in robotic software development. Indeed, findin…