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232 results for “smell”
Refactoring Code Smells in Open Source Projects: A Hands-on Approach to Teaching Software Maintenance
<p>Code smells are suboptimal code structures that can undermine software quality and maintainability. On the one hand, software engineers commonly apply refactoring techniques to address these deficiencies and improve internal quality attributes. On the other hand, when performed manually and without discipline, refactoring can lead to code degradation. Despite its importance, refactoring and code smells are rarely explored in depth in undergraduate computing courses, which can be reflected in industry practices. To address this gap, this paper presents a hands-on approach to teaching code smell refactoring through contributions to Open Source Software (OSS) projects, an environment where developers with diverse skill levels collaborate, and maintaining code quality is particularly challenging. Code smells accumulate over time in such scenarios, hindering software evolution and collaboration. Our study in two undergraduate Software Quality and Software Maintenance courses expands on previous findings by incorporating an in-depth analysis of students’ learning experiences. The results indicate that: (i) students rec- ognized improvements in code quality after refactoring; (ii) they identified strong connections between refactoring, testing, and debugging; (iii) their confidence decreased when refactoring required changes across multiple files; (iv) code complexity posed a significant challenge to refactoring; (v) students’ choices of refactoring techniques were influenced by project structure and personal preferences, often combining multiple techniques to address a single smell; (vi) in some cases, refactoring introduced new code smells; (vii) the longest refactoring efforts were also the most likely to reintroduce code smells; (viii) contributing to OSS projects improved students’ programming skills and fostered a sense of professional growth; (ix) students faced challenges in understanding OSS contribution processes, particularly regarding issue resolution, adherence to contribution guidelines, and responding to maintainer feedback; (x) automated checks and review workflows varied across projects, affecting students’ ability to submit successful contributions; and (xi) despite these challenges, engagement with OSS enabled students to gain practical experience in collaborative software development. Our findings offer valuable insights for software engineering educators seeking to integrate refactoring practices into coursework while leveraging OSS contributions as an educational tool.</p>
"On the Prevalence, Co-occurrence, and Impact of Infrastructure-as-Code Smells" Replication Package
<p>In this package, we provide the dataset for the paper: " On the Prevalence, Co-occurrence, and Impact of Infrastructure-as-Code Smells ''</p><p> </p><p>1 – we provide the generated data for each of the research questions.</p><p> </p><p>2 – we provide the scripts for each of the research questions.</p>
Repository of smell comments
Open the record for dataset details and reuse information.
The relationship between contextual factors and code smells: contextual factors, code smell tables and the investigated object software
<p>This dataset has research data regarding the impact of contextual factors on the incidence of code smells and is organized as follows:</p> <p> - In the 'datasetcodesmellsandcontextualfactors' folder there is the collected data referring to the contextual factors and code smells of the 419 systems used as a sample in the research</p> <p> - In the 'datasetonlycontextualfactors' folder there is data on the contextual factors of more than 450,000 software hosted on Github</p> <p> - In the 'softwares' folder, the 419 software that were used in the study are available, to enable further studies on this dataset</p> <p><br> In each folder there is a guide explaining the available fields and the structure of the folder</p>
Code Smells in Elixir: Early Results from a Grey Literature Review [DATASET]
<p>Dataset used in research submitted to ICPC ERA 2022 </p>
Code Smells seleccionados para la investigación categorizados.
<p>Planilla de Code Smells seleccionados para la investigación: <em>Análisis de defectos de diseño, </em>categorizados; Como parte del informe de Proyecto de Grado presentado al tribunal evaluador como requisito de graduación de la carrera Ingeniería en Computación de la Universidad de la República.</p>
Bash in the Wild: Language Usage, Code Smells, and Bugs - Dataset
<p>This is the data set for the paper "Bash in the Wild: Language Usage, Code Smells, and Bugs".</p>
Smell Pittsburgh: Engaging Community Citizen Science for Air Quality
<p>Link to the files and description of the Smell Pittsburgh Dataset –<br> <a href="https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2FCMU-CREATE-Lab%2Fsmell-pittsburgh-prediction%2Ftree%2Fmaster%2Fdataset%2Fv2&data=05%7C01%7Cy.c.hsu%40uva.nl%7C89562067341d40d0bad308da2c475652%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C637870982141190827%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=gWn0nGRUl5EAHvDfJwTlTNJN%2BWFH62NX6Mw%2B2web6XE%3D&reserved=0">https://github.com/CMU-CREATE-Lab/smell-pittsburgh-prediction/tree/master/dataset/v2</a></p> <p>Smell Pittsburgh (<a href="https://smellpgh.org">https://smellpgh.org</a>) is a mobile application for crowdsourcing reports of bad odors, such as those generated from air pollution. The data is used to train a machine learning model to predict the presence of bad smell and create push notifications to inform citizens about the bad smell. The motivation, background, and design of the Smell Pittsburgh application is described in the following paper.</p> <ul> <li>Yen-Chia Hsu, Jennifer Cross, Paul Dille, Michael Tasota, Beatrice Dias, Randy Sargent, Ting-Hao (Kenneth) Huang, and Illah Nourbakhsh. 2020. Smell Pittsburgh: Engaging Community Citizen Science for Air Quality. ACM Transactions on Interactive Intelligent Systems. 10, 4, Article 32. DOI:<a href="https://doi.org/10.1145/3369397">https://doi.org/10.1145/3369397</a>. Preprint:<a href="https://arxiv.org/pdf/1912.11936.pdf">https://arxiv.org/pdf/1912.11936.pdf</a>.</li> </ul>
Code Smells in Elixir: Results from a Mining Study on GitHub [DATASET]
<p>Dataset used in research</p>
QSage: Structural and Semantic Metric Analysis for Quantum Code Smell Detection
Open the record for dataset details and reuse information.
Code Smells and their Collocations : A Large-scale Experiment on Open-source Systems
<p>This dataset includes classes with code smells, acquired from Qualitas Corpus (QC).<br> Folder 'all' contains data coming from the QC rev.20130901 (92 systems).<br> Folder 'domains' contains data coming from QC rev.20111026 (76 systems updated to their most recent releases from rev.20130901). <br> Folder 'pca' includes results of the PCA analysis, generated with the R prcomp() function for regular PCA, and logisticPCA() function for the binary data.</p> <p>Filenames include information about the base release of the QC, and a number (25, 50 or 75) that specifies the minimum number of detectors that identified a specific smell instance (25%, 50%, and 75%, respectively). For example, if a given code smell in a class X has been identified by 1 out of 4 available detecting tools, then the smell for the class X will be reported in the respective file 25, but not in 50 or 75. Please note, that for smells detected with only one tool, the values would be equal in all datasets (in that case, the smell was detected by 0% or 100% of tools)</p> <p>In all files, "1" denotes that the smell was identified (subject to the limitations with the number of detectors, described above), and “0” that the smell was not found in a given class.</p> <p>The filename also includes the domain abbreviation (app, css, dev, dgdv) or a keyword ALL, which indicates that the dataset includes data from all domains.</p> <p>The smells have been detected by 11 tools. Most of the tools detect more than one smell. <br> Information about the tool used to detect a given smell is given in headers of each file. Additionally, in 'smell detectors.csv' file we present the information about smells detected by a specific tool.</p>
Configuration smells dataset
<p>Data produced for configuration smell empirical analysis</p>
An Empirical Catalog of Code Smells for the Presentation Layer of Android Apps: Appendix
<p>The appendix of our "An Empirical Catalog of Code Smells for the Presentation Layer of Android Apps" paper.</p>
Dataset of the Paper "Copilot-in-the-Loop: Fixing Code Smells in Copilot-Generated Python Code using Copilot"
<p>This dataset contains a list of 102 code smells detected from Copilot-generated Python code, along with Python code files generated by Copilot from the <em>Repositories</em> and <em>Code</em> label, respectively. This dataset also includes Copilot Chat’s responses to fixing the 102 detected code smells. A brief description of each document and folder in the dataset is provided below:</p> <p><strong>1. files folder</strong></p> <p>contains 311 Python code files generated by Copilot. In the 311 Python files, 171 are retrieved under the <em>Repositories</em> label, indicating Python code files entirely generated by Copilot, and 140 are retrieved under the <em>Code</em> label, indicating Python code snippets generated by Copilot.</p> <p><strong>2. results of RQ1.xlsx</strong></p> <p>contains a list of 102 code smells detected from Copilot-generated Python code.</p> <p><strong>3. results of RQ2.xlsx</strong></p> <p>contains Copilot Chat’s responses to fixing the detected 102 code smells instructed by three prompts of varying detail levels.</p>
Detecting Code Smells in React-based Web Apps
<p>DBR</p>
Replication package for PHP code smells in web apps: evolution, survival and anomalies
<p>Replication package (dataset and programs/scripts) and extra documents for article:</p> <p><strong>PHP code smells in web apps: evolution, survival and anomalies</strong></p> <p>Folder zips RQ1-5 - Extra graphics for all applications studied. In the article, due to lack of space, we only present graphics for two applications.</p> <p>Data folder zip - data used in the study and suitable for replication. The folder is divided in subfolders and there is an "explanation.txt".</p> <p>scripts.zip - PHP scripts used to pre-process data</p> <p>R scripts.zip - R scripts used to analyze and graphics</p>
Online Survey: Impacts of Smells and Refactorings for Microservice Security
<p>This file contains the responses given to an online survey by practitioners and researchers working with microservice applications. The survey was aimed at measuring their agreement/disagreement with the possible impacts of smells and refactorings for microservices security [1], which were elicited by means of thematic analysis of both white and grey literature on the topic.</p> <p>[1] https://doi.org/10.1016/j.jss.2022.111393</p>
Refactoring Assertion Roulette and Duplicate Assert test smells: a controlled experiment [DATA]
<p>Data of the experiment with the RAIDE tool.</p>
Fig. 1 in What pollinators see does not match what they smell: Absence of color-fragrance association in the deceptive orchid Ionopsis utricularioides
Fig. 1. Spectral sensitivities of the bee models used for this study, Apis mellifera (A) and Melipona quadrifasciata (B), based on Peitsch et al. (1992), and color hexagon models for Ionopsis utricularioides using the spectral sensitivity of A. mellifera (C) and M. quadrifasciata (D). Each point represents the color loci of one individual. All flowers appear in the bee blue-green region of the hexagon and the variation is related to an increase of color saturation, as the points vary mostly in the distance from the center of the hexagon. Points are color-coded according to the continuous color saturation variation in I. utricularioides. The insets in (C) and (D) show the hexagon and its sections defined based on opponent processing of photoreceptor signals (B: blue; G: green; UV: ultraviolet). The circle at the center of the color hexagon has a radius of 0.1 hexagon units, for scale. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in What pollinators see does not match what they smell: Absence of color-fragrance association in the deceptive orchid Ionopsis utricularioides
Fig. 2. Scatterplots of Pearson correlation (with 95% confidence regression interval) between classes of compounds and color saturation based on Apis mellifera (top row, pale yellow regression line) and Melipona quadrifasciata (bottom row, pale green regression line). None of the classes of compounds found on the orchid's fragrance is correlated with the color saturation. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
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OpenNeuro
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