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Rohit Gheyi

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Review Open access Sep 2026

Can LLMs Detect and Refactor Test Smells in Performance Tests?

Performance testing is essential to ensure that software meets its responsiveness and scalability requirements, and tools such as k6 are used to write performance test scripts that exercise these aspects in a structured and optimized way. Like any code, regardless of the programming language, k6 scripts are subject to...

Adelson Ornellas, Manoel Aranda, Márcio Ribeiro et al. · 0 citations
Open access Sep 2026

An Exploratory Study on Prompting Strategies for LLM-Assisted Heuristic Evaluation of Web User Interfaces

Usability is a key non-functional requirement in interactive software systems, and heuristic evaluation can support the verification and validation of user interfaces by identifying violations of usability principles. However, this process still depends on human expertise, is subject to evaluator variability, and may b...

Ana Beatriz Farias, D. Albuquerque, Emanuel Dantas et al. · 0 citations
Jul 2026

AI-Conducted Interviews in Empirical Software Engineering: An Experience Report

AI interviewers should be treated as a complementary option for short, focused, low-risk studies, with protocol design, privacy guidance, artifact validation, and human oversight, but do not establish completion rates, time savings, summary fidelity, or equivalence to human-conducted interviews.

Rohit Gheyi, D. Albuquerque, Márcio Ribeiro et al. · 0 citations
Preprint Aug 2026

Evaluating Language Models on Cross-Language Code Functional Equivalence

This work investigates whether LLMs can accurately judge functional equivalence across different programming languages in human-written code, a setting that requires deeper reasoning beyond superficial similarity, and identifies a difficulty-dependent breakdown in equivalence judgment.

Hui Sun, Anderson G. Uchôa, Rohit Gheyi et al. · 2 citations
Preprint Aug 2026

Detecting Behavioral Changes in Python Refactoring Implementations with Foundation Models

This work proposes an approach based on a foundation model oracle that analyzes git-style diffs to identify behavioral changes introduced by Python refactorings and uncovered 13 distinct bugs among the seven refactoring types studied.

Jonhnanthan Oliveira, Rohit Gheyi, Márcio Ribeiro et al. · 0 citations

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