6 papers
Social Bias in LLM-Generated Code: Benchmark and Mitigation
Fazle Rabbi, Lin Ling, Song Wang +1
Large Language Models (LLMs) are increasingly deployed to generate code for human-centered applications where demographic fairness is critical. However, existing evaluations focus…
Specification-Driven Code Translation Powered by Large Language Models: How Far Are We?
Soumit Kanti Saha, Fazle Rabbi, Song Wang +1
Large Language Models (LLMs) are increasingly being applied across various domains, including code-related tasks such as code translation. Previous studies have explored using LLMs…
Engineering Pitfalls in AI Coding Tools: An Empirical Study of Bugs in Claude Code, Codex, and Gemini CLI
Ruixin Zhang, Wuyang Dai, Hung Viet Pham +3
The rapid integration of Large Language Models (LLMs) into software development workflows has given rise to a new class of AI-assisted coding tools, such as Claude-Code, Codex, and…
BabelCoder: Agentic Code Translation with Specification Alignment
Fazle Rabbi, Soumit Kanti Saha, Tri Minh Triet Pham +2
As software systems evolve, developers increasingly work across multiple programming languages and often face the need to migrate code from one language to another. While automatic…
Secure-Instruct: An Automated Pipeline for Synthesizing Instruction-Tuning Datasets Using LLMs for Secure Code Generation
Junjie Li, Fazle Rabbi, Bo Yang +2
Although Large Language Models (LLMs) show promising solutions to automated code generation, they often produce insecure code that threatens software security. Current approaches (…
Bias Unveiled: Investigating Social Bias in LLM-Generated Code
Lin Ling, Fazle Rabbi, Song Wang +1
Large language models (LLMs) have significantly advanced the field of automated code generation. However, a notable research gap exists in evaluating social biases that may be pres…