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62 results for “Code Smells”
Replication package and appendixes for Causal inference of server- and client-side code smells in web apps evolution
<p>-Analysis <br>--R scripts used to make the analisys, divided by folders<br>--Data folders used in the questions</p> <p>-Appendixes - used in the article to shwo extra tables and plots</p> <p>-data folders - Aggregation of data, each app has two files, CSV and xls</p> <p>-separated data folders - 5 files for each app, with lines corresponding to the each released official version<br>--serversmells<br>--clientsmells<br>--javascriptsmells<br>--Cloc(metrics)<br>--version (all oficial releases)</p> <p>-issues_bugs<br>--data -issues by app by release <br>--data_bugs_more - the same but only bugs, by app by release<br>--scripts - scrips used to aggregate issues (from daily issues to by release) anf the same for bugs</p> <p> </p>
Reproduction package for paper "How far are we from reproducible research on code smell detection? A systematic literature review"
<p>Checklist and data extracted from publications analyzed for "How far are we from reproducible research on code smell detection? A systematic literature review" paper, together with processing scripts and calculations of Cohen's Kappa.</p> <p>Paper that describes details of the data is available here: https://doi.org/10.1016/j.infsof.2021.106783</p>
Towards a systematic approach to manual annotation of code smells - C# Dataset of Long Method and Large Class code smells
<p>This dataset includes open-source projects written in C# programing language, annotated for the presence of Long Method and God Class code smells. Each instance was manually annotated by at least two annotators. We explain our motivation and methodology for creating this dataset in our <a href="https://www.techrxiv.org/articles/preprint/Towards_a_systematic_approach_to_manual_annotation_of_code_smells/14159183/1">preprint</a>:</p> <p>Luburić, N., Prokić, S., Grujić, K.G., Slivka, J., Kovačević, A., Sladić, G. and Vidaković, D., 2021. Towards a systematic approach to manual annotation of code smells. </p> <p>The dataset contains two excel datasheets:</p> <ul> <li><em>DataSet_Large Class.xlsx</em> – C# classes annotated for the Large Class code smell severity.</li> <li><em>DataSet_Long Method.xlsx</em> – C# methods annotated for the Long method code smell severity.</li> </ul> <p> The columns in the datasheet represent:</p> <ul> <li><em>Code Snippet ID</em> – the full name of the code snippet. <ul> <li>For classes, this is the package/namespace name followed by the class name. The full name of inner classes also contains the names of any outer classes (e.g., <em>namespace.subnamespace.outerclass.innerclass</em>).</li> <li>For methods, this is the full name of the class and the methods’s signature (e.g., <em>namespace.class.method(param1Type, param2Type)</em> ).</li> </ul> </li> <li><em>Link </em>– The GitHub link to the code snippet, including the commit and the start and end LOC.</li> <li><em>Code Smell </em>– code smell for which the code snippet is examined (Large Class or Long Method).</li> <li><em>Project Link </em>– the link to the version of the code repository that was annotated.</li> <li><em>Metrics </em>– a list of metrics for the code snippet, calculated by our <a href="https://github.com/Clean-CaDET/platform#readme">platform</a>. Our dataset provides 25 class-level metrics for Large Class detection and 18 method-level metrics for Long Method detection The list of metrics and their definitions is available <a href="https://github.com/Clean-CaDET/platform/blob/c4acff95ec00ff6c25fa62dde4818c1f40e39d39/CodeModel/CaDETModel/CodeItems/CaDETMetrics.cs">here</a>.</li> <li><em>Final annotation </em>– a single severity score calculated by a majority vote. </li> <li><em>Annotators </em>– each annotator's (1, 2, or 3) assigned severity score.</li> </ul> <p>To help guide their reasoning for evaluating the presence and the severity of a code smell, three annotators independently annotated whether the considered heuristics apply to an evaluated code snippet. We provide these results in two separate excel datasheets:</p> <ul> <li><em>LargeClass_Heuristics.xlsx </em>- C# classes annotated for the presence of heuristics relevant for the Large Class code smell.</li> <li><em>LongMethod_Heuristics.xlsx </em>- C# classes annotated for the presence of heuristics relevant for the Large Class code smell.</li> </ul> <p>The columns of these two datasheets are:</p> <ul> <li><em>Code Snippet ID </em>- the full name of the code snippet (matching the IDs from <em>DataSet_Large Class.xlsx </em>and <em>DataSet_Long Method.xlsx</em>)</li> <li><em>Annotators</em> – heuristics labelled by each of the annotators (1, 2, or 3).</li> <li><em>Heuristics </em>– whether the heuristic is applicable to the examined code snippet or not (Section 1.2.4 lists heuristics relevant for the Large Class detection, and Section 1.2.5 lists the heuristics relevant for the Long Method detection).</li> </ul>
Mapeo entre Code Smells-AntiPatterns seleccionados para nuestra investigación.
<p>Planilla de mapeo entre Code Smells-AntiPatterns seleccionados para la investigación: <em>Análisis de defectos de diseño</em>; 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>
Landfill: An open dataset of code smells with public evaluation
<p>Code smells are symptoms of poor design and implementation choices that may hinder code comprehension and possibly increase the change- and fault-proneness of source code. Several techniques have been proposed in the literature for detecting code smells. These techniques are generally evaluated by comparing their accuracy on a set of detected candidate code smells against a manually-produced oracle. Unfortunately, such comprehensive sets of annotated code smells are not available in the literature, with only a few exceptions. This dataset provides 243 instances of five types of code smells identified from 20 open-source software projects. In particular, it contains a SQL file with the information concerning such instances and a zip file with their source code.</p>
Code Smells seleccionados para la investigación.
<p>Planilla de Code Smells seleccionados para la investigación: <em>Análisis de defectos de diseño</em>; 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>
Code Smells Dataset (oracles)
<p>This repository contains the datasets, obtained in 3 years, resulting from the Crowdsmelling methodology.<br> Each file contains the dataset (oracle) of the year or set of years, for the code smells Long Method, God Class, and Feature Envy. The file Exercise-Code smells detection (ESII 2020).pdf describes the exercise used in the validation of code smells, and the file code-classification-statistics.csv shows statistics about the percentages of teams that classified the methods and classes.</p> <p>More information about the datasets can be found in the article:</p> <p>Reis, José Pereira dos , Abreu, Fernando Brito e . & Carneiro, Glauco de Figueiredo. Crowdsmelling: A preliminary study on using collective knowledge in code smells detection. <em>Empir Software Eng</em> <strong>27, </strong>69 (2022). https://doi.org/10.1007/s10664-021-10110-5</p> <p>DATASET STRUCTURE<br> - project name<br> - package name<br> - class name<br> - method name<br> - code metrics [1]<br> - code smell classification</p> <p> </p> <p>REFERENCES</p> <p>[1] Metrics description can be found in the study: "Fontana, F. A., Mantyla, M. V., Zanoni, M., and Marino, A. (2015), Comparing and experimenting machine learning techniques for code smell detection, Empirical Software Engineering"</p> <p> </p>
Dataset of smell comments in Code Review Discussions
<p>The raw data contains 104,321 code review comments, with records between January 2014 to August 2023. After a keyword search, 18,850 comments were manually analyzed by 26 developers. The analyzed data resulted in 3,798 smell comments. This meticulously curated dataset was used to collect 4,058 more smell comments through semantic search, comprising a total of 7,856 smell comments, which represents 13,27% of the original data collected. </p>
Online Appendix - Scented Since the Beginning: On the Diffuseness of Test Smells in Automatically Generated Test Code
<p>Online appendix for the paper "Scented Since the Beginning: On the Diffuseness of Test Smells in Automatically Generated Test Code".</p> <p>The full description of the content of this appendix can be found in the README file.</p>
QScored: A Large Dataset of Code Smells and Quality Metrics
<p>The dataset contains code quality information of more than 86 thousand GitHub repositories containing more than 1.1 billion lines of code mainly written in C# and Java. The code quality information contains detected 7 kinds of architecture smells, 19 kinds of design smells, and 11 kinds of implementation smells, and 27 commonly used code quality metrics computed at project, package, class, and method levels.</p>
Supplementary data for A Systematic Literature Review on the Code Smells Datasets and Validation Mechanisms
<p>The attached Microsoft Excel files contain the data and diagrams of the paper:</p> <p><strong>A systematic literature review on the code smells datasets and validation mechanisms</strong></p> <p>The article is under review in the ACM Computing Surveys.</p> <p> </p>
Empirical Study of the Relationship between Design Patterns and Code Smells
<p>Software systems are often developed in such a way that good practices in the object-oriented paradigm are not met, causing the occurrence of specific disharmonies which are sometimes called code smells. Design patterns catalogue best practices for developing object-oriented software systems. Although code smells and design patterns are widely divergent, there might be a co-occurrence relation between them. The objective of this paper is to empirically evaluate if the presence of design patterns is related to the presence of code smells at different granularity levels. We performed an empirical replication study using 20 design patterns and 13 code smells in ten small-size to medium-size, open-source Java-based systems. We applied statistical analysis and association rules. Results confirm that classes participating in design patterns have less smell-proneness and smell frequency than classes not participating in design patterns. We also noticed that every design pattern category act in the same way in terms of smell-proneness in the subject systems. However, we observed, based on the association rules learning and the proposed validation technique, that some patterns may be linked with certain smells in some cases. For instance, Command patterns can co-occur with God Class, Blob and External Duplication smell.</p> <p><strong>The published data set contains the following:</strong></p> <ol> <li>List of the selected systems (source code files)</li> <li>The P-MARt: the design pattern repository as XML for the selected systems. </li> <li>Data of design patterns and code smells: We processed this data by parsing the design pattern XML file and running the smell detection tool (inFusion).</li> <li>The data of the data mining analysis.</li> </ol>
The relationship between code smells and design patterns: an external replicated experiment
<p>Dataset of the paper "The relationship between code smells and design patterns: an external replicated experiment"</p> <p>File descriptions in readme.txt.</p>
Replication Package for the Paper: "An Empirical Analysis of the Manual Detection of Code Smells via Code Review"
<p>This repository contains the data and results from the paper "An Empirical Analysis of the Manual Detection of Code Smells via Code Review" submitted to SANER 2021.</p> <p> </p> <p><strong>1. "data.zip" file contains the following three folders: </strong></p> <p> </p> <p><strong>1). data folder</strong></p> <p>The data folder contains the retrieved 1,174 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2). scripts folder</strong></p> <p>The scripts folder contains the Python script that was used to search for code smell terms and the list of code smell terms.</p> <ul> <li><em>keywords.txt</em> contains the keywords keywords asociated with code smells, such as "smell, duplication, and dead".</li> <li><em>get_changes.py</em> is used for getting code changes from OpenStack.</li> <li><em>get_comments.py</em> is used for getting review comments for each code change.</li> <li><em>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</li> <li>keywords_improve.py is used for improving the keyword-based mining approach.</li> <li>tools.py is used for supporting the process of keywords improving.</li> </ul> <p> </p> <p><strong>3). project folder</strong></p> <p>The project folder contains the MAXQDA project files. The files can be opened by MAXQDA 12 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download.</p> <ul> <li><em>Data Labeling & Encoding for RQ2.mx12</em> is the results of data labeling and encoding for RQ2, which were analyzed by the MAXQDA tool.</li> <li><em>Data Labeling & Encoding for RQ3.mx12</em> is the results of data labeling and encoding for RQ3, which were analyzed by the MAXQDA tool.</li> </ul> <p> </p> <p><strong>2. Keywords associated with code smells.pdf</strong></p> <p>This file contains the final set of keywords asociated with code smells that we identified by following the systematic approach proposed by Bosu and his colleagues in their paper: Identifying the Characteristics of Vulnerable Code Changes: An Empirical Study, FSE 2014.</p>
Replication Package for the Paper: "Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community"
<p>This repository contains the data and results from the paper "Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community" submitted to ICPC 2021.</p> <p> </p> <p><strong>1. "data.zip" contains the following three folders:</strong></p> <p> </p> <p><strong>1) data folder</strong></p> <p>The data folder contains the retrieved 1,190 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2) scripts folder</strong></p> <p>The scripts folder contains the Python scripts that were used to search for code smell terms and the list of code smell terms.</p> <ul> <li> <p><em>keyword.txt</em> contains the keywords associated with code smells, such as "smell, duplication, and dead".</p> </li> <li> <p><em>get_changes.py</em> is used for getting code changes from OpenStack.</p> </li> <li> <p><em>get_comments.py</em> is used for getting review comments for each code change.</p> </li> <li> <p><em>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</p> </li> <li> <p><em>random_select.py</em> is used for randomly selecting review comments that do not contain any keyword.</p> </li> <li> <p><em>keywords_improve.py</em> is used for improving the keyword-based mining approach.</p> </li> <li> <p><em>tools.py</em> is used for supporting the process of keywords improving.</p> </li> </ul> <p> </p> <p><strong>3) project folder</strong></p> <p>The project folder contains the MAXQDA project files. The files can be opened by MAXQDA 12 or higher versions, which are available at <a href="https://www.maxqda.com/">https://www.maxqda.com/</a> for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at <a href="https://www.maxqda.com/trial">https://www.maxqda.com/trial</a> for download.</p> <ul> <li> <p><em>Data Labeling & Encoding for RQ2.mx12</em> is the results of data labeling and encoding for RQ2, which were analyzed by the MAXQDA tool.</p> </li> <li> <p><em>Data Labeling & Encoding for RQ3.mx12</em> is the results of data labeling and encoding for RQ3, which were analyzed by the MAXQDA tool.</p> </li> </ul> <p> </p> <p><strong>2. Keywords associated with code smells.pdf</strong></p> <p>This file contains the final set of keywords associated with code smells that we identified by following the systematic approach proposed by Bosu and his colleagues in their paper: Identifying the Characteristics of Vulnerable Code Changes: An Empirical Study, FSE 2014.</p>
On the Practitioners' Understanding of Coupling Smells -- A Grey Literature Based Grounded-Theory Study: Dataset and Code
<p>This is the dataset and related code artifact for the article "On the Practitioners' Understanding of Coupling Smells -- A Grey Literature Based Grounded-Theory Study".</p> <p> </p> <p>Abstract of the article:</p> <p> </p> <p>Context: Code and design smells, such as the coupling smells examined in this article, are widely studied. Existing empirical studies reveal gaps between the scientific theory and practice, not yet explained by the scientific literature. Only basic coupling smell detection approaches and metrics seem to have been transferred to practice so far.</p> <p> </p> <p>Objective: This article aims to study the current practitioner's understanding of coupling smells.</p> <p> </p> <p>Method: Based on grey literature sources containing practitioner views on coupling smells, we performed a Grounded Theory (GT) study. We used UML-based modeling to precisely encode our findings and performed a rigorous analysis of our codes and models.</p> <p> </p> <p>Results: Our results are defining factors of coupling smells, as well as smell impacts, trade-offs, relationships to other smells, relationships to practices and patterns, and fix options as perceived by practitioners. We further identified gaps in the understanding of coupling smells between science and practice, and derived opportunities and challenges for future scientific work.</p> <p> </p> <p>Conclusions: Five lessons are presented as opportunities and challenges for future research. Our results can help scientists to get a better understanding of practitioner concerns, and practitioners to get an overview of the current perception of other practitioners on coupling smells.</p> <p> </p> <p> </p>
Tailoring Code Smells to Model-View-Controller Architectures: Online appendix
<p>Appendix for our paper "Tailoring Code Smells to Model-View-Controller Architectures" (EMSE journal)</p>
Unraveling the Impact of Code Smell Agglomerations on Code Stability
<p>This replication package contains instructions on how to use our scripts, and all the necessary data to further replicate/expand the work Unraveling the Impact of Code Smell Agglomerations on Code Stability.</p> <p>Read the README file to replicate or expand our work!</p>
An Exploratory Evaluation of Continuous Feedback to Enhance Machine Learning Code Smell Detection
<p><em>Context</em>: Code smells are symptoms of bad design choices implemented on the source code. To manage and enhance software quality, it is important to be aware of code smells and refactor them whenever possible. As a result, several code smell detection tools and techniques have been proposed over the years. These tools and techniques present different strategies to detect code smells. More recently, machine learning algorithms have also been proposed to support code smell detection. However, we lack empirical evidence on how expert feedback could improve detection of these machine learning based techniques. <em>Objective:</em> This paper aims to propose and evaluate a machine-learning based strategy to improve detection of code smells by means of continuous feedback provided by the system expert.<em> Method: </em>To evaluate the strategy, we follow an experimental design to compare results of the detection before and after the feedback, both when feedback is provided at once and continuously. We focus on four code smells - God Class, Long Method, Feature Envy, and Refused Bequest - detected in twenty Java systems by using five code smell detection tools. We also extracted class- and method-level metrics from the systems for training the machine learning algorithms. <em>Results:</em> We observed that continuous feedback improves the performance of code smell detection. For the detection of God Class, a code smell with a detection performance initially good, we achieved an average improvement of 0.13 in terms of F1. For Refused Bequest, another class-level code smell, we achieved an average improvement of 0.58 in terms of F1 after all interactions of the strategy. For the method-level code smells, Long Method and Feature Envy, we achieved an average improvement of 0.66 and 0.72 in terms of F1, respectively.<em> Conclusions: </em>Our promising results are a stepping stone towards the development of tools relying on continuous feedback for machine learning detection of code smells.</p>
Replication Package for the Paper: "Code Smells Detection via Modern Code Review: A Study of the OpenStack and Qt Communities"
<p>This repository contains the data and results from the paper "Code Smells Detection via Modern Code Review: A Study of the OpenStack and Qt Communities" submitted to the ICPC 2021 special issue of the Empirical Software Engineering Journal, 2021.</p> <p> </p> <p>The replication package contains the following two folders:</p> <p> </p> <p><strong>1) data folder</strong></p> <p>The data folder contains the following four folders, which is organized by research questions (RQs).</p> <ul> <li>RQ1: The RQ1 folder contains the retrieved 1,539 code reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</li> <li>RQ2: The RQ2 folder contains the coded data for RQ2, called <em>Data Labeling & Encoding for RQ2.mx18</em>. It is the results of data labeling and encoding for RQ2, which was analyzed by the MAXQDA tool.</li> <li>RQ3 and RQ5: <ul> <li><em>Extracted data for RQ3.1.xlsx</em>: this file contains the extracted data (i.e., specific refactoring actions suggested by reviewers) for RQ3.1.</li> <li><em>Data Labeling & Encoding for RQ3 and RQ5.mx18</em>: this file contains the extracted data for RQ3 (excluding the specific refactoring actions in RQ3.1) and RQ5.</li> <li><em>Code change status for RQ5.xlsx</em>: this file contains the information of status of code changes where the developers disagreed with the reviewers and chose to ignore the identified code smells.</li> </ul> </li> <li>RQ4: The RQ4 folder contains the extracted data for RQ4, called <em>Extracted data for RQ4.xlsx</em>.</li> </ul> <p>Note: The mx18 files can be opened by MAXQDA 18 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download.</p> <p> </p> <p><strong>2) scripts folder</strong></p> <p>The scripts folder contains the Python scripts that were used to search for code smell terms and the list of code smell terms.</p> <ul> <li><em>keyword.txt</em> contains the keywords associated with code smells, such as "smell, duplication, and dead".</li> <li><em>get_changes.py</em> is used for getting code changes from OpenStack and Qt.</li> <li><em>get_comments.py</em> is used for getting review comments for each code change.</li> <li><em>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</li> <li><em>random_select.py</em> is used for randomly selecting review comments that do not contain any keyword.</li> <li><em>keywords_improve.py</em> is used for improving the keyword-based mining approach.</li> <li><em>tools.py</em> is used for supporting the process of keywords improving.</li> </ul>
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