Author

Baishakhi Ray

h-index 7 View on Semantic Scholar

We have 3 of 16 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#artificial intelligence Preprint Oct 2026

TestJack: Should you trust the results in coding benchmarks? Agentic Coding Benchmarks Auditing via Evaluator Evolution

Large language model (LLM) agents are rapidly reshaping software engineering, accompanied by an explosion of new code benchmarks. Yet nearly all existing benchmarks still rely on the same decades-old criterion: a solution is correct if it passes a fixed set of unit tests. Such tests are often insufficient: they check o...

Shuang-Jie Yao, Hao Wang, Koushik Sen et al. · 0 citations

#artificial intelligence Preprint Oct 2026

Cost-Efficient Theorem Proving via Agent Orchestration in Program Verification

Program verification establishes software correctness through machine-checkable proofs constructed in theorem provers. It's a guarantee especially valuable for code generated by large language models (LLMs), which is fluent but carries no assurance of correctness. Almost all existing provers, however, pursue pass rates...

Shuang-Jie Yao, Nikolaus Holzer, Mark Santolucito et al. · 0 citations

#software testing Preprint Sep 2026

Adversarial Testing of Automated Program Repair Agents for Security Vulnerabilities

It is concluded that autonomous APR agents cannot be trusted yet in production deployment, given their susceptibility to adversarial attacks.

F. Trad, Si-Min Chen, H. Pham et al. · 0 citations