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62 results for “code smells”
Applying an Inconsistency Repair Mechanism for clone-and-own Code Smell Analysis: the Apo-games Case Study (Evaluation Data)
<p>This is a repository containing the artifacts and the results of the evaluation of the solution paper "Applying an Inconsistency Repair Mechanism for <em>clone-and-own</em> Code Smell Analysis: the Apo-games Case Study"</p>
Supplementary material for "can you tell me if it smells? A study on how developers discuss code smells and anti-patterns in Stack Overflow"
<p>This dataset contains all data and results from the "Can you tell me if it smells? A study on how developers discuss code smells and anti-patterns in Stack Overflow" paper that was accepted at the 22nd International Conference on Evaluation andAssessment in Software Engineering (EASE), Christchurch, New Zealand.</p>
A PL/SQL Code Smell Data Set
<p>A PL/SQL Code Smells data set extracted from 20 open source projects on GitHub.</p>
Detecting Code Smells using ChatGPT: Initial Insights
<h2><strong>Replication Package: Detecting Code Smells using ChatGPT: Initial Insights</strong></h2> <h3><br><strong>Replication Package</strong></h3> <p>This repository contains data files to replicate our study presented in the paper <em><strong>Detecting Code Smells using ChatGPT: Initial Insights</strong></em>.</p> <h3>Dataset Structure</h3> <p>We provide raw and preprocessed data in different formats: </p> <ul> <li><code>csv - the database in CSV format with comma separated columns.</code></li> <li><code>csv-semi-comma - the database in CSV format with semi-comma separated columns.</code></li> <li><code>html - the database in HTML format to ease the visualization of the dataset. </code></li> <li><code>sql - the original SQL (schema and data) of the database used in this study.</code></li> </ul> <p>Each compressed directory contains a README file explaining which information each column includes. </p> <h3>Dataset Overview</h3> <p>A. <code>sql</code> directory consists of two tables: </p> <p>1. The table <code>tb_unique_bad_smell</code> contains information we collect from ChatGPT. </p> <p><br><code>CREATE TABLE public.tb_unique_bad_smell (</code><br><code> id_unique_bad_smell integer NOT NULL,</code><br><code> id_bad_smell bigint,</code><br><code> id_source_code bigint,</code><br><code> chat_gpt_response text,</code><br><code> question text,</code><br><code> badsmell_base text,</code><br><code> bad_smell_gpt text,</code><br><code> found_any boolean,</code><br><code> valid_bad_smell boolean,</code><br><code> bad_smell_in_base boolean,</code><br><code> bad_smell_not_in_the_base text,</code><br><code> bad_smell_not_found text,</code><br><code> index integer,</code><br><code> index_base integer,</code><br><code> url_github text,</code><br><code> id_base bigint,</code><br><code> dt_insertion timestamp without time zone,</code><br><code> nr_question smallint</code><br><code>);</code><br><br></p> <p><code>id_source_code</code> - integer identifier when we imported the original dataset to our database.<br><code>chat_gpt_response</code> - contains ChatGPT's response to our prompts. <br><code>question</code> - This column contains all the prompts we submitted to ChatGPT. Each question includes the prompt under evaluation and the source code we were interested in evaluating.<br><code>badsmell_base</code> - code smells assigned in the original dataset. <br><code>bad_smell_gpt</code> - code smells identified by the ChatGPT. We extracted these smells from `chat_gpt_response`.<br><code>found_any</code> - a boolean field indicating if any of the smells found by ChatGPT are in the dataset. In other words, if ChatGPT answered yes, finding any smell (even if it is not in the original dataset).<br><code>valid_bad_smell</code> - text field containing the smells in the original dataset that the ChatGPT identified.<br><code>bad_smell_in_base</code> - a boolean field indicating if the smells found by ChatGPT are in the original dataset.<br><code>bad_smell_not_in_the_base</code> - text field containing the smells that ChatGPT found and they are not in the dataset. <br><code>bad_smell_not_found</code> - text field containing the smells in the dataset that ChatGPT did not detect.<br><code>index</code> and <code>index_base</code> - indexes imported from the original dataset. GitHub provided them.<br><code>url_github</code> - the GitHub URL of the source code extracted from the original dataset. <br><code>id_base</code> - id field in the original dataset.<br><code>nr_question</code> - integer field containing 1 or 2, identifying the prompt we submitted to ChatGPT. </p> <p><br>2. The table `tb_unique_source_code` contains information we imported from the original dataset we used to submit to ChatGPT and information to evaluate ChatGPT's performance. The primary data we relied on are:</p> <p><br><code>smell</code> - presents the smell assigned to the code.<br><code>severity</code> - the severity of the smells, Major, Critical, Minor. <br><code>type</code> - function or class.<br><code>code_name</code> - the full path for the smell, e.g., <code>nm_package.nm_class.nm_method</code><br><code>start_line</code> - the code where the smell starts.<br><code>end_line</code> - line in the code where the smell ends.<br><code>link</code> - the GitHub URL of the source code evaluated.</p> <p> </p>
Trusted Code Smells Dataset
<p>This code smells dataset collected from Git history of top-100 Java projects. It contains 5912 samples of smelly code with fixing it by developers themselves. Collected code smell types are: Complex Method, Long Method and God Class.</p>
Code and Data for the paper "Active smelling in the American cockroach"
<p>Code and Data used in the paper "Active smelling in the american cockroach" by Antoine Hoffmann and Einat Couzin-Fuchs.<br> Code author: Antoine Hoffmann<br> The file structure indicated in the R scripts needs to be replicated for the code to work as is.</p>
MLCQ: Industry-relevant code smell data set
<p>The MLCQ data set with nearly 15000 code samples was created by software developers with professional experience who reviewed industry-relevant, contemporary Java open source projects. </p> <p>We expect that this data set should stay relevant for a longer time than data sets that base on code released years ago and, additionally, will enable researchers to investigate the relationship between developers' background and code smells' perception.</p> <p><strong>If you use this data set please cite the following paper:</strong></p> <p>Lech Madeyski and Tomasz Lewowski. MLCQ: Industry-relevant code smell data set. In <em>Evaluation and Assessment in Software Engineering (EASE2020)</em>, April 15–17, 2020, Trondheim, Norway.ACM, New York, NY, USA, 6 pages, DOI: <a href="https://doi.org/10.1145/3383219.3383264">3383219.3383264</a> URL: https://doi.org/10.1145/3383219.3383264</p> <p>Note: Pre-print should be available soon from <a href="http://madeyski.e-informatyka.pl">http://madeyski.e-informatyka.pl</a></p>
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>
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>
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>
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>
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International Brain Laboratory public data
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OpenNeuro
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