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20232026
most citedTrustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps

2 citations · 2 across the 7 of their papers we have counts for

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5 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

SciCodePile: A 128GB Corpus and Executable Benchmark for Challenging Scientific Code Generation

Weifeng Sun, Ye Fan, Yuchen Chen +6

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 limit…

cs.SE2025

Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs

Fanyu Wang, Chetan Arora, Yonghui Liu +5

Acceptance criteria (ACs) play a critical role in software development by clearly defining the conditions under which a software feature satisfies stakeholder expectations. However…

cs.SE2025

Enhancing Project-Specific Code Completion by Inferring Internal API Information

Le Deng, Xiaoxue Ren, Chao Ni +3

Project-specific code completion is a critical task that leverages context from a project to generate accurate code. State-of-the-art methods use retrieval-augmented generation (RA…

cs.SE2023★ 2 cited

Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps

David Lo

For decades, much software engineering research has been dedicated to devising automated solutions aimed at enhancing developer productivity and elevating software quality. The pas…