5 papers
Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests
Junda Zhao, Shurui Zhou, Eldan Cohen
While Large Language Models (LLMs) show great promise for automating unit test generation, recent studies suggest that the quality of generated tests can be negatively impacted whe…
Do Coverage and Mutation Scores of LLM-Generated Test Suites Correlate with Their Effectiveness? (Replicability Study)
Junda Zhao, Shurui Zhou, Eldan Cohen
Recent advances in large language models (LLMs) have driven growing interest in using LLMs to automate test generation. Prior work commonly evaluates generated test suites using pr…
Formalize, Don't Optimize: The Heuristic Trap in LLM-Generated Combinatorial Solvers
Haoyu Wang, Yuliang Song, Tao Li +5
Large Language Models (LLMs) struggle to solve complex combinatorial problems through direct reasoning, so recent neuro-symbolic systems increasingly use them to synthesize executa…
CP-SynC: Multi-Agent Zero-Shot Constraint Modeling in MiniZinc with Synthesized Checkers
Yuliang Song, Eldan Cohen
Constraint Programming (CP) is a powerful paradigm for solving combinatorial problems, yet translating natural language problem descriptions into executable models remains a signif…
Variational Prefix Tuning for Diverse and Accurate Code Summarization Using Pre-trained Language Models
Junda Zhao, Yuliang Song, Eldan Cohen
Recent advancements in source code summarization have leveraged transformer-based pre-trained models, including Large Language Models of Code (LLMCs), to automate and improve the g…