Large language models (LLMs) are increasingly used for code generation, yet generated programs may exhibit social bias through unfair or differential treatment of sensitive demographic attributes. While prior work mainly studies direct code generation, bias in reasoning-based generation remains underexplored. We conduc...
Wei-Feng Sun, Jie-Ke Shi, Zhou Yang et al.· 0 citations
A typed domain-specific language that captures recurring tensor structures, such as repeated regions and floating-point fields, through a set of reversible operators, is designed, which formulates lossless tensor compression as program synthesis.
Jie-Ke Shi, Jun-Da He, Wenjia Jiang et al.· 0 citations
Agent systems rely on LLM APIs for every response, but these APIs can return server errors, truncated responses, or corrupted content that propagates through downstream agents and causes task failure. Evaluating robustness under these faults is crucial for reliable deployment. Existing fault injection methods are offli...
Gou Tan, Zhensu Sun, Jie-Ke Shi et al.· 2 citations
Delta (Differential Testing for DRL Agents) is proposed, a novel and comprehensive framework that automatically identifies both safety-critical and optimality bugs in DRL agents and investigates the effectiveness of three offline RL algorithms in generating challenger agents.
Jun-Da He, Jie-Ke Shi, Zhou Yang et al.· Proceedings of the ACM on so...· 0 citations
Large language models (LLMs) excel at general-purpose code generation, yet how well they handle scientific code remains an open question. Existing datasets and benchmarks are limited in scale, domain coverage, or executable verification, leaving the true gap between current LLMs and reliable scientific code generators...
Weifeng Sun, Ye Fan, Yuchen Chen et al.· arXiv.org· 0 citations
This paper is the first to study how SE processes are changing in the development of SE agents and what challenges developers face, and describes a seven-stage workflow and five process shifts, including a move toward evaluation-driven development.
Yunbo Lyu, David Williams, Jieke Shi et al.· arXiv.org· 0 citations
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