arXiv subjects

Roham Koohestani

Publications and source records attributed to Roham Koohestani.

10 recordsLinked to original sources

arXiv2026

Are Agents Probabilistic Automata? A Trace-Based, Memory-Constrained Theory of Agentic AI

This paper studies standard controller architectures for agentic AI and derives automata-theoretic models of their interaction behavior via trace semantics and abstraction. We model an agent implementation as a finite control program augmented with explicit memory primitives (bounded buffers, a call stack, or read/write external memory) and a stochastic policy component (e.g., an LLM) that selects among architecturally permitted actions. Instead of equating the concrete agent with a deterministic acceptor, we treat the agent-environment closed loop as inducing a probability distribution over finite interaction traces. Given an abstraction function $\Abs$ from concrete configurations to a finite abstract state space, we obtain a probabilistic trace language and an abstract probabilistic transition model $M_{\Abs}$ suitable for probabilistic model checking. Imposing explicit, framework-auditable restrictions on memory access and control flow, we prove that the support of the resulting trace language is regular for bounded-memory controllers, context-free for strict call-return controllers, and recursively enumerable for controllers equipped with unbounded read/write memory. These correspondences allow the reuse of existing verification methods for finite-state and pushdown systems, and they delineate precisely when undecidability barriers arise. The probabilistic semantics leads to quantitative analyses such as: what is the probability of entering an unsafe abstract region, and how can we bound this probability in the presence of environment nondeterminism.

cs.AI↗

arXiv2026

TriCEGAR: A Trace-Driven Abstraction Mechanism for Agentic AI

Agentic AI systems act through tools and evolve their behavior over long, stochastic interaction traces. This setting complicates assurance, because behavior depends on nondeterministic environments and probabilistic model outputs. Prior work introduced runtime verification for agentic AI via Dynamic Probabilistic Assurance (DPA), learning an MDP online and model checking quantitative properties. A key limitation is that developers must manually define the state abstraction, which couples verification to application-specific heuristics and increases adoption friction. This paper proposes TriCEGAR, a trace-driven abstraction mechanism that automates state construction from execution logs and supports online construction of an agent behavioral MDP. TriCEGAR represents abstractions as predicate trees learned from traces and refined using counterexamples. We describe a framework-native implementation that (i) captures typed agent lifecycle events, (ii) builds abstractions from traces, (iii) constructs an MDP, and (iv) performs probabilistic model checking to compute bounds such as Pmax(success) and Pmin(failure). We also show how run likelihoods enable anomaly detection as a guardrailing signal.

cs.AI↗

arXiv2026

AST-PAC: AST-guided Membership Inference for Code

Code Large Language Models are frequently trained on massive datasets containing restrictively licensed source code. This creates urgent data governance and copyright challenges. Membership Inference Attacks (MIAs) can serve as an auditing mechanism to detect unauthorized data usage in models. While attacks like the Loss Attack provide a baseline, more involved methods like Polarized Augment Calibration (PAC) remain underexplored in the code domain. This paper presents an exploratory study evaluating these methods on 3B--7B parameter code models. We find that while PAC generally outperforms the Loss baseline, its effectiveness relies on augmentation strategies that disregard the rigid syntax of code, leading to performance degradation on larger, complex files. To address this, we introduce AST-PAC, a domain-specific adaptation that utilizes Abstract Syntax Tree (AST) based perturbations to generate syntactically valid calibration samples. Preliminary results indicate that AST-PAC improves as syntactic size grows, where PAC degrades, but under-mutates small files and underperforms on alphanumeric-rich code. Overall, the findings motivate future work on syntax-aware and size-adaptive calibration as a prerequisite for reliable provenance auditing of code language models.

cs.AI↗

arXiv2025

Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality

Benchmarks are essential for unified evaluation and reproducibility. The rapid rise of Artificial Intelligence for Software Engineering (AI4SE) has produced numerous benchmarks for tasks such as code generation and bug repair. However, this proliferation has led to major challenges: (1) fragmented knowledge across tasks, (2) difficulty in selecting contextually relevant benchmarks, (3) lack of standardization in benchmark creation, and (4) flaws that limit utility. Addressing these requires a dual approach: systematically mapping existing benchmarks for informed selection and defining unified guidelines for robust, adaptable benchmark development. We conduct a review of 247 studies, identifying 273 AI4SE benchmarks since 2014. We categorize them, analyze limitations, and expose gaps in current practices. Building on these insights, we introduce BenchScout, an extensible semantic search tool for locating suitable benchmarks. BenchScout employs automated clustering with contextual embeddings of benchmark-related studies, followed by dimensionality reduction. In a user study with 22 participants, BenchScout achieved usability, effectiveness, and intuitiveness scores of 4.5, 4.0, and 4.1 out of 5. To improve benchmarking standards, we propose BenchFrame, a unified framework for enhancing benchmark quality. Applying BenchFrame to HumanEval yielded HumanEvalNext, featuring corrected errors, improved language conversion, higher test coverage, and greater difficulty. Evaluating 10 state-of-the-art code models on HumanEval, HumanEvalPlus, and HumanEvalNext revealed average pass-at-1 drops of 31.22% and 19.94%, respectively, underscoring the need for continuous benchmark refinement. We further examine BenchFrame's scalability through an agentic pipeline and confirm its generalizability on the MBPP dataset. All review data, user study materials, and enhanced benchmarks are publicly released.

cs.SE↗

arXiv2025

Does In-IDE Calibration of Large Language Models work at Scale?

The introduction of large language models into integrated development environments (IDEs) is revolutionizing software engineering, yet it poses challenges to the usefulness and reliability of Artificial Intelligence-generated code. Post-hoc calibration of internal model confidences aims to align probabilities with an acceptability measure. Prior work suggests calibration can improve alignment, but at-scale evidence is limited. In this work, we investigate the feasibility of applying calibration of code models to an in-IDE context. We study two aspects of the problem: (1) the technical method for implementing confidence calibration and improving the reliability of code generation models, and (2) the human-centered design principles for effectively communicating reliability signal to developers. First, we develop a scalable and flexible calibration framework which can be used to obtain calibration weights for open-source models using any dataset, and evaluate whether calibrators improve the alignment between model confidence and developer acceptance behavior. Through a large-scale analysis of over 24 million real-world developer interactions across multiple programming languages, we find that a general, post-hoc calibration model based on Platt-scaling does not, on average, improve the reliability of model confidence signals. We also find that while dynamically personalizing calibration to individual users can be effective, its effectiveness is highly dependent on the volume of user interaction data. Second, we conduct a multi-phase design study with 3 expert designers and 153 professional developers, combining scenario-based design, semi-structured interviews, and survey validation, revealing a clear preference for presenting reliability signals via non-numerical, color-coded indicators within the in-editor code generation workflow.

cs.SE↗

arXiv2025

Code4MeV2: a Research-oriented Code-completion Platform

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 within large corporations. This creates a barrier for the academic community, as researchers must often develop dedicated platforms to conduct studies on human--AI interaction, making reproducible research and large-scale data analysis impractical. In this work, we introduce Code4MeV2, a research-oriented, open-source code completion plugin for JetBrains IDEs, as a solution to this limitation. Code4MeV2 is designed using a client--server architecture and features inline code completion and a context-aware chat assistant. Its core contribution is a modular and transparent data collection framework that gives researchers fine-grained control over telemetry and context gathering. Code4MeV2 achieves industry-comparable performance in terms of code completion, with an average latency of 200~ms. We assess our tool through a combination of an expert evaluation and a user study with eight participants. Feedback from both researchers and daily users highlights its informativeness and usefulness. We invite the community to adopt and contribute to this tool. More information about the tool can be found at https://app.code4me.me.

cs.SE↗

arXiv2025

AgentGuard: Runtime Verification of AI Agents

The rapid evolution to autonomous, agentic AI systems introduces significant risks due to their inherent unpredictability and emergent behaviors; this also renders traditional verification methods inadequate and necessitates a shift towards probabilistic guarantees where the question is no longer if a system will fail, but the probability of its failure within given constraints. This paper presents AgentGuard, a framework for runtime verification of Agentic AI systems that provides continuous, quantitative assurance through a new paradigm called Dynamic Probabilistic Assurance. AgentGuard operates as an inspection layer that observes an agent's raw I/O and abstracts it into formal events corresponding to transitions in a state model. It then uses online learning to dynamically build and update a Markov Decision Process (MDP) that formally models the agent's emergent behavior. Using probabilistic model checking, the framework then verifies quantitative properties in real-time.

cs.AI↗

arXiv2025

HyperSeq: A Hyper-Adaptive Representation for Predictive Sequencing of States

In the rapidly evolving world of software development, the surge in developers' reliance on AI-driven tools has transformed Integrated Development Environments into powerhouses of advanced features. This transformation, while boosting developers' productivity to unprecedented levels, comes with a catch: increased hardware demands for software development. Moreover, the significant economic and environmental toll of using these sophisticated models necessitates mechanisms that reduce unnecessary computational burdens. We propose HyperSeq - Hyper-Adaptive Representation for Predictive Sequencing of States - a novel, resource-efficient approach designed to model developers' cognitive states. HyperSeq facilitates precise action sequencing and enables real-time learning of user behavior. Our preliminary results show how HyperSeq excels in forecasting action sequences and achieves remarkable prediction accuracies that go beyond 70%. Notably, the model's online-learning capability allows it to substantially enhance its predictive accuracy in a majority of cases and increases its capability in forecasting next user actions with sufficient iterations for adaptation. Ultimately, our objective is to harness these predictions to refine and elevate the user experience dynamically within the IDE.

cs.SE↗

arXiv2025

Rethinking IDE Customization for Enhanced HAX: A Hyperdimensional Perspective

As Integrated Development Environments (IDEs) increasingly integrate Artificial Intelligence, Software Engineering faces both benefits like productivity gains and challenges like mismatched user preferences. We propose Hyper-Dimensional (HD) vector spaces to model Human-Computer Interaction, focusing on user actions, stylistic preferences, and project context. These contributions aim to inspire further research on applying HD computing in IDE design.

cs.SE↗

arXiv2024

Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests

Automated unit test generators, particularly search-based software testing tools like EvoSuite, are capable of generating tests with high coverage. Although these generators alleviate the burden of writing unit tests, they often pose challenges for software engineers in terms of understanding the generated tests. To address this, we introduce UTGen, which combines search-based software testing and large language models to enhance the understandability of automatically generated test cases. We achieve this enhancement through contextualizing test data, improving identifier naming, and adding descriptive comments. Through a controlled experiment with 32 participants from both academia and industry, we investigate how the understandability of unit tests affects a software engineer's ability to perform bug-fixing tasks. We selected bug-fixing to simulate a real-world scenario that emphasizes the importance of understandable test cases. We observe that participants working on assignments with UTGen test cases fix up to 33% more bugs and use up to 20% less time when compared to baseline test cases. From the post-test questionnaire, we gathered that participants found that enhanced test names, test data, and variable names improved their bug-fixing process.

cs.SE↗