17 citations · 45 across the 24 of their papers we have counts for
26 papers
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…
Investigating the Performance of Small Language Models in Detecting 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 crucial for uncovering issues not easily captured by automation. However,…
RefModel: Detecting Refactorings using Foundation Models
Pedro Simões, Rohit Gheyi, Rian Melo +3
Refactoring is a common software engineering practice that improves code quality without altering program behavior. Although tools like ReExtractor+, RefactoringMiner, and RefDiff…
Bugs in the Shadows: Static Detection of Faulty Python Refactorings
Jonhnanthan Oliveira, Rohit Gheyi, Márcio Ribeiro +1
Python is a widely adopted programming language, valued for its simplicity and flexibility. However, its dynamic type system poses significant challenges for automated refactoring…
Assessing the Capability of LLMs in Solving POSCOMP Questions
Cayo Viegas, Rohit Gheyi, Márcio Ribeiro
Recent advancements in Large Language Models (LLMs) have significantly expanded the capabilities of artificial intelligence in natural language processing tasks. Despite this progr…
Code Generation with Small Language Models: A Codeforces-Based Study
Débora Souza, Rohit Gheyi, Lucas Albuquerque +2
Large Language Models (LLMs) demonstrate capabilities in code generation, potentially boosting developer productivity. However, their adoption remains limited by high computational…
Agentic LMs: Hunting Down Test Smells
Rian Melo, Pedro Simões, Rohit Gheyi +5
Test smells reduce test suite reliability and complicate maintenance. While many methods detect test smells, few support automated removal, and most rely on static analysis or mach…
Evaluating the Effectiveness of Small Language Models in Detecting Refactoring Bugs
Rohit Gheyi, Marcio Ribeiro, Jonhnanthan Oliveira
Popular IDEs frequently contain bugs in their refactoring implementations. Ensuring that a transformation preserves a program's behavior is a complex task. Traditional detection me…
Assessing Python Style Guides: An Eye-Tracking Study with Novice Developers
Pablo Roberto, Rohit Gheyi, José Aldo Silva da Costa +1
The incorporation and adaptation of style guides play an essential role in software development, influencing code formatting, naming conventions, and structure to enhance readabili…
Evaluating Large Language Models in Detecting Test Smells
Keila Lucas, Rohit Gheyi, Elvys Soares +2
Test smells are coding issues that typically arise from inadequate practices, a lack of knowledge about effective testing, or deadline pressures to complete projects. The presence…
Evaluating the Capability of LLMs in Identifying Compilation Errors in Configurable Systems
Lucas Albuquerque, Rohit Gheyi, Márcio Ribeiro
Compilation is an important process in developing configurable systems, such as Linux. However, identifying compilation errors in configurable systems is not straightforward becaus…
A Catalog of Transformations to Remove Smells From Natural Language Tests
Manoel Aranda, Naelson Oliveira, Elvys Soares +6
Test smells can pose difficulties during testing activities, such as poor maintainability, non-deterministic behavior, and incomplete verification. Existing research has extensivel…
Manual Tests Do Smell! Cataloging and Identifying Natural Language Test Smells
Elvys Soares, Manoel Aranda, Naelson Oliveira +7
Background: Test smells indicate potential problems in the design and implementation of automated software tests that may negatively impact test code maintainability, coverage, and…
Learning Quick Fixes from Code Repositories
Reudismam Rolim, Gustavo Soares, Rohit Gheyi +2
Code analyzers such as Error Prone and FindBugs detect code patterns symptomatic of bugs, performance issues, or bad style. These tools express patterns as quick fixes that detect…
Learning Syntactic Program Transformations from Examples
Reudismam Rolim, Gustavo Soares, Loris D'Antoni +5
IDEs, such as Visual Studio, automate common transformations, such as Rename and Extract Method refactorings. However, extending these catalogs of transformations is complex and ti…