Browsing by Autor "Luigi Mateus"

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    Identifying and Addressing Test Smells in JavaScript: A Developer-Centric Study
    (2025) Jamille Carmo Oliveira; Luigi Mateus; Gabriel Amaral; Tássio Virgínio; Carla Bezerra; Ivan Machado; Larissa Rocha
    Test smells are poor practices in test code that can compromise maintainability, reliability, and clarity. While the concept has been widely studied in languages such as Java and Python, research on test smells in JavaScript remains limited—despite its prominence in modern development. To address this gap, we conducted a focus group study with JavaScript developers of varying experience levels to explore their perceptions of seven test smells. These smells—Anonymous Test, Comments Only Test, Overcommented Test, General Fixture, Test Without Description, Transcripting Test, and Sensitive Equality—are particularly relevant to the JavaScript ecosystem and had not been systematically examined in this context prior to our study. We applied thematic analysis to transcribed discussions, uncovering developers’ concerns, recognition patterns, and proposed mitigation strategies. Our results show that experience level strongly influences the ability to detect and refactor test smells, with junior developers often struggling to identify more subtle patterns. To the best of our knowledge, this is the first study to investigate JavaScript developers’ perceptions of test smells using a qualitative approach. Our findings reveal key challenges, offer practical insights for test improvement, and support the development of better training and tooling for JavaScript test quality.
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    SNUTS.js: Sniffing Nasty Unit Test Smells in Javascript
    (2024) Jamille Carmo Oliveira; Luigi Mateus; Tássio Virgínio; Larissa Rocha
    Test smells indicate potential issues or weaknesses within the test code, which can compromise its effectiveness and maintainability. They highlight areas where improvements can enhance the overall quality of the test suite or testing practices. For instance, an example of a test smell is the Anonymous Test, where the test’s name lacks descriptive information about its function or purpose. Addressing these test smells can result in more robust and maintainable test suites, thus improving the reliability of the testing process. Despite significant research on these issues, tools are scarce for automatically detecting them, particularly in certain programming languages such as JavaScript. In the current landscape, existing test smell detection tools for JavaScript lack intuitiveness and graphical interfaces, and require extensive configuration, which may lead to low adoption within the developer community. To address this gap, we propose SNUTS.js, a tool designed to streamline the detection of test smells in JavaScript. Designed as an API, SNUTS.js offers versatility, allowing integration with various tools and environments. This tool goes beyond existing solutions by identifying previously undetected test smells, including the Anonymous Test, Comments Only Test, Overcommented, General Fixture, Transcripting Test, and Sensitive Equality. We also introduce a new test smell termed Test Without Description, which denotes a test case lacking descriptive text. In a preliminary evaluation, we constructed a dataset of tests sourced from real-world projects on GitHub. Through manual analysis, we identified 285 instances of test smells. SNUTS.js demonstrated a detection accuracy of 100% for three specific types of test smells, Anonymous Test, Overcommented, and General Fixture, all tailored to the JavaScript environment. Link to the video: https://youtu.be/89z0jy4Nu0s