17 citations · 45 across the 24 of their papers we have counts for
10 papers · 1 filter
Evaluating Language Models on Cross-Language Code Functional Equivalence
Hui Sun, Anderson Uchôa, Rohit Gheyi +1
Background: Large Language Models (LLMs) have demonstrated strong performance across a variety of code-understanding tasks, leading many to believe that they can reason about progr…
Detecting Behavioral Changes in Python Refactoring Implementations with Foundation Models
Jonhnanthan Oliveira, Rohit Gheyi, Márcio Ribeiro +1
Python is a widely adopted programming language, valued for its simplicity and flexibility. However, automated refactoring for Python remains challenging, even though refactoring i…
Comprehending Python Repetition Structures: An Eye-Tracking Study with Novice Developers
José Júnior Silva da Costa, Rohit Gheyi, José Aldo Silva da Costa +1
Code comprehension is central to software maintenance and evolution, yet different Python repetition structures may impose distinct cognitive demands. We conducted a controlled eye…
AI-Conducted Interviews in Empirical Software Engineering: An Experience Report
Rohit Gheyi, Danyllo Albuquerque, Márcio Ribeiro +1
Semi-structured interviews are widely used in empirical software engineering (ESE), but they are resource-intensive and difficult to coordinate across schedules, locations, and nat…
Prompting GPT-5 on Scrum Certification Questions: An Empirical Accuracy Study
Mirko Perkusich, Danyllo Albuquerque, João Paiva +6
Large Language Models (LLMs) are increasingly used in Agile Software Development for documentation, coaching, and training. As practitioners adopt these tools to prepare for certif…
An Empirical Study of Gemini 3 for Detecting Natural Language Test Smells in Manual Test Cases
Keila Lucas, Rohit Gheyi, Márcio Ribeiro +3
Manual testing, in which testers follow natural language instructions to validate system behavior, remains essential for uncovering issues that are difficult to capture with automa…
Foundation Models as Oracles for Refactoring Correctness Detection
Rohit Gheyi, Rian Melo, Jonhnanthan Oliveira +2
Refactoring tools in popular Integrated Development Environments (IDEs) can introduce unintended behavioral changes or compilation errors, a persistent challenge that undermines de…
Refactoring for Novices in Java: An Eye Tracking Study on the Extract vs. Inline Methods
José Aldo Silva da Costa, Rohit Gheyi, José Júnior Silva da Costa +5
Developers often extract methods to improve readability, understanding, and reuse, while inlining keeps logic in one block. Prior work based on static metrics has not shown clear d…
Adoption of Large Language Models in Scrum Management: Insights from Brazilian Practitioners
Mirko Perkusich, Danyllo Albuquerque, Allysson Allex Araújo +4
Scrum is widely adopted in software project management due to its adaptability and collaborative nature. The recent emergence of Large Language Models (LLMs) has created new opport…
An Empirical Study of Foundation Models for Variability-Induced Compilation Errors in Configurable C Code
Rohit Gheyi, Lucas Albuquerque, Márcio Ribeiro +3
In configurable systems, conditional compilation can hide compilation errors under untested feature combinations. We investigate foundation models for detecting such errors and, in…