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20242026
most citedBias in Large Language Models: Origin, Evaluation, and Mitigation

24 citations · 30 across the 11 of their papers we have counts for

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15 papers · 1 filter

cs.SE2026

How Reasoning Shapes Social Bias in LLM-Generated Code?

Weifeng Sun, Jieke Shi, Zhou Yang +4

Large language models (LLMs) are increasingly used for code generation, yet generated programs may exhibit social bias through unfair or differential treatment of sensitive demogra…

cs.SE2026

Fail-Fast, Restart-Smart: Early Failure Prediction and Restart for SWE Agentic Tasks

Chenyu Wang, Yunbo Lyu, Junda He +4

Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates. Failed runs tend to be longer a…

cs.SE2026

Bridging Behavior and Implementation: Automated Java Glue Code Generation for Behavior-Driven Development

Xinyu Shi, Zhou Yang, An Ran Chen

Behavior-Driven Development (BDD) helps technical and non-technical stakeholders share a common understanding of software requirements through natural-language scenarios. Glue code…

cs.SE2026

How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study

Yunbo Lyu, David Williams, Jieke Shi +5

The rise of Software Engineering (SE) agents, i.e., LLM-based agents that can understand large codebases and carry out engineering tasks with limited human intervention, has been m…

cs.SE20265 cited

Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions

Chenyu Wang, Zhou Yang, Yunbo Lyu +3

Artificial Intelligence (AI) is now used across nearly every industry, making AI model quality essential for building reliable and trustworthy systems. Historically, correctness ha…

cs.SE2026

Hidden Licensing Risks in the LLMware Ecosystem

Bo Wang, Yueyang Chen, Jieke Shi +5

Large Language Models (LLMs) are increasingly integrated into software systems, giving rise to a new class of systems referred to as LLMware. Beyond traditional source-code compone…