9 papers
AST-PAC: AST-guided Membership Inference for Code
Roham Koohestani, Ali Al-Kaswan, Jonathan Katzy +1
Code Large Language Models are frequently trained on massive datasets containing restrictively licensed source code. This creates urgent data governance and copyright challenges. M…
TriCEGAR: A Trace-Driven Abstraction Mechanism for Agentic AI
Roham Koohestani, AteÅ GörpelioÄlu, Egor Klimov +2
Agentic AI systems act through tools and evolve their behavior over long, stochastic interaction traces. This setting complicates assurance, because behavior depends on nondetermin…
Are Agents Probabilistic Automata? A Trace-Based, Memory-Constrained Theory of Agentic AI
Roham Koohestani, Ziyou Li, Anton Podkopaev +1
This paper studies standard controller architectures for agentic AI and derives automata-theoretic models of their interaction behavior via trace semantics and abstraction. We mode…
Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality
Roham Koohestani, Philippe de Bekker, Begüm Koç +1
Benchmarks are essential for unified evaluation and reproducibility. The rapid rise of Artificial Intelligence for Software Engineering (AI4SE) has produced numerous benchmarks for…
Does In-IDE Calibration of Large Language Models work at Scale?
Roham Koohestani, Agnia Sergeyuk, David Gros +4
The introduction of large language models into integrated development environments (IDEs) is revolutionizing software engineering, yet it poses challenges to the usefulness and rel…
Code4MeV2: a Research-oriented Code-completion Platform
Roham Koohestani, Parham Bateni, Aydin Ebrahimi +3
The adoption of AI-powered code completion tools in software development has increased substantially, yet the user interaction data produced by these systems remain proprietary wit…