6 papers · 1 filter
LLM-Based Invariant Testing for Software Functional Bugs
Ruogu Yang, Yifeng He, Yundi Xu +2
Manually writing unit tests to uncover functional bugs in software libraries is not only time-consuming but also requires a deep understanding of the intended semantics of the APIs…
Code Generation by Differential Test Time Scaling
Yifeng He, Ethan Wang, Jicheng Wang +2
Test-time scaling has emerged as a promising approach for improving code generation by exploring large solution spaces at inference time. However, existing methods often rely on pu…
ContractBench: Can LLM Agents Preserve Observation Contracts?
Jicheng Wang, Yifeng He, Zili Wang +3
Tool-augmented LLM agents call APIs whose intermediate outputs, such as presigned URLs, session tokens, and OAuth state parameters, are observation contracts: artifacts whose later…
FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation
Yifeng He, Jicheng Wang, Yuyang Rong +1
Testing is essential to modern software engineering for building reliable software. Given the high costs of manually creating test cases, automated test case generation, particular…
RepoGenReflex: Enhancing Repository-Level Code Completion with Verbal Reinforcement and Retrieval-Augmented Generation
Jicheng Wang, Yifeng He, Hao Chen
In real-world software engineering tasks, solving a problem often requires understanding and modifying multiple functions, classes, and files across a large codebase. Therefore, on…
UniTSyn: A Large-Scale Dataset Capable of Enhancing the Prowess of Large Language Models for Program Testing
Yifeng He, Jiabo Huang, Yuyang Rong +3
The remarkable capability of large language models (LLMs) in generating high-quality code has drawn increasing attention in the software testing community. However, existing code L…