most citedA Catalog of Transformations to Remove Smells From Natural Language Tests

15 citations · 16 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2024

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…

cs.SE20241 cited

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…

cs.SE2024

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…

cs.SE202415 cited

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…

cs.SE2023

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…