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232 results for “smell”
Additional Material for Architecture Smell Ontology
<p>This is the dataset of our literature review study on Architecture Smells. We provide the decisive exclusion criteria for excluded studies and the classification for included studies. We exported OWL file of the ontology from Protégé and included the tables of the architecture smells and the connected quality attributes and design principles. The study protocol is also included.</p> <p> </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>
Law Smells (Data)
<p>Data to replicate the results from the paper:</p> <p>"Law Smells: Defining and Detecting Problematic Patterns in Legal Drafting", Artificial Intelligence and Law 2022.</p>
Supplementary Material for the paper "Developers' Perception Matters: Machine Learning to Detect Developer-sensitive Smells"
<p>Supplementary material for the paper "Daniel Oliveira; Wesley K. G. Assunção; Alessandro Garcia; Baldoino Fonseca; and Márcio Ribeiro. Developers' Perception Matters: Machine Learning to Detect Developer-sensitive Smells. In: Empirical Software Engineering. 2022. Springer."</p>
Selected Resources for a Literature Survey on Software Smells
<p>It contains the bibliography information about the selected articles in each phase of a literature survey on software smells.</p>
Replication Package for "Catching Smells in the Act: A GitHub Actions Workflow Investigation" (SCAM 2024)
<p>Welcome to our artifact! In here we provide additional information on how to retrace our steps performed during the research. We have split up our content into four sections based on the RQ's we have answered. Below you can find a quick summary of the contents of each folder, each folder also contains additional information regarding any data and scripts present.</p> <ul> <li>RQ1 + 2: Contains excel files with the commits we have analyzed and the scripts we have used to automate this process.</li> <li>RQ3: Contains our smell detector and evaluation of the detector</li> <li>RQ4: Contains the data on our contribution study</li> </ul>
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>
Smelling smells using Deep Learning
<p>We have two objectives to carry out this research. The first objective is to explore the feasibility of applying deep learning models to detect smells without extensive feature engineering, just by feeding the source code elements in tokenized form. Another goal of the work is to investigate the possibility of applying transfer-learning in the context of deep learning models for smell detection by investigating transferability of results between programming languages.</p> <p>This repository contains supporting material of the experiment.</p>
Refactoring Test Smells: A Perspective from Open-Source Developers
<p>Presentation video for the <strong>5th Brazilian Symposium on Systematic and Automated Software Testing (SAST)</strong>, during the <strong>11th Brazilian Conference on Software: Practice and Theory (CBSoft 2020)</strong></p>
Analyzing the Impact of Refactoring on Bad Smells
<p>Refactoring aims to remove bad smells and increase software maintainability by improving the software structure without changing its behavior. However, some studies show that refactoring tools may introduce new bad smells into the source code, but to the best of our knowledge, we have not been able to find a complete catalog that states the bad smells introduced from refactoring. To bridge this gap, this paper goal is to evaluate the impacts of refactoring on the detection of bad smells in open-source Java systems. Hence, we want to know if and when the automated refactoring removes or introduces bad smells. </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>
Troll that smells Christian blood
Trold, der vejrer kristenblod (English: Troll that smells Christian blood) is a sculpture made by Niels Hansen Jacobsen (1861-1941). It was modelled between 1895-96, and a bronze cast was ordered by brewer Carl Jacobsen in 1901-02. It was originally placed in front of Jacobsen's church in Valby, Copenhagen, Jesuskirken, but was too controversial for the parish, so it was moved to the garden of Ny Carlsberg Glyptotek. In 2002 the church wanted the sculpture back, but the Glyptotek would not part with it, so a copy was made, and placed in front of the church. The name of the statue is taken from a story in Norse folklore where the hero hides in the troll's castle. Thereafter, whenever the troll enters the castle, he cries: "I smell a Christian man's blood!"  Source: Objaverse 1.0 / Sketchfab
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>
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