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232 results for “smell”
Introduction to Smells
<p>While the repercussions of smells on code quality have been empirically assessed, there is still only anecdotal evidence on when and why bad smells are introduced. To fill this gap, we conducted a large empirical study over the change history of 200 open source projects from different software ecosystems and investigated when bad smells are introduced by developers, and the circumstances and reasons behind their introduction. Our study required the development of a strategy to identify smell-introducing commits, the mining of over 0.5M commits, and the manual analysis of 9,164 of them (i.e., those identified as smell-introducing). Our findings mostly contradict common wisdom stating that smells are being introduced during evolutionary tasks. In the light of our results, we also call for the need to develop a new generation of recommendation systems aimed at properly planning smell refactoring activities.</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>
Character Study Strong Smell | Messerschmidt
Character Study Strong Smell, circa 1770-1781 CE. From Austria, Pressburgh, now Slovakia, Bratislava. By Franz Xaver Messerschmidt. Franz Xaver Messerschmidt (February 6, 1736 – August 19, 1783) was a German-Austrian sculptor most famous for his "character heads", a collection of busts with faces contorted in extreme facial expressions. Created in RealityCapture by Capturing Reality from 179 images Source: Objaverse 1.0 / Sketchfab
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>
Featured Scents: Assessing Architectural Smells for Self-Adaptive Systems at Runtime
<p>Self-adaptive systems (SAS) change their behavior and structure at runtime to answer the changes in their environment. Such systems combine different architectural fragments or solutions via feature binding/unbinding at runtime. Moreover, this combination may negatively impact the system's architectural qualities, exhibiting architectural bad smells (ABS). These issues are challenging to detect in the code due to the combinatorial explosion of interactions amongst features. Since SAS do not document these features in their source code, design time smell detection ignores them and risks reporting spurious smells. This paper assesses this risk to understand how ABS occur at runtime for different feature combinations. We look for cyclic dependency and hub-like ABS in various runtime adaptations of two SAS, Adasim and mRubis. Our results indicate that architectural smells are feature-dependent and that their number is highly variable from one adaptation to the other. Some ABS appear in all runtime adaptations, some in only a few. We discuss the reasons behind these architectural smells for each system and draw some lessons for targeted analyses of ABS in SAS.</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>
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>
Video Game Bad Smells: What they are and how Developers Perceive Them - Online dataset
<p>This artifact contains the online replication package of the manuscript "Video Game Bad Smells: What they are and how Developers Perceive Them"</p>
Replication Package for "On the correlation between Architectural Smells and Static Analysis Warnings"
<div> <div> <div> <div> <div> <h1>Replication Package for the Paper: "On the Relation between Architectural Smells and Static Analysis Warnings"</h1> <h2>Authors</h2> <ul> <li>Matteo Esposito, University of Oulu, Finland</li> <li>Mikel Robredo, University of Oulu, Finland</li> <li>Francesca Arcelli Fontana, University of Milano-Bicocca, Italy</li> <li>Valentina Lenarduzzi, University of Oulu, Finland</li> </ul> <h2>Content Overview</h2> <p>This replication package contains the following materials:</p> <ul> <li><strong>Tables:</strong> Excel files that include all hypothesis testing data, including normality tests for each hypothesis.</li> <li><strong>Data:</strong> RAW Qualitas Corpus dataset and aggregated SAT output.</li> <li><strong>SAT Instructions:</strong> A PDF file providing detailed instructions for the SAT setup and execution.</li> </ul> <h2>Contact Information</h2> <p>For any issues, questions, or further assistance, please do not hesitate to contact the authors of the paper. We are here to help!</p> </div> </div> </div> </div> </div>
Supporting material for the paper " Test Smells Learning by a Gamification Approach"
<p>Table reporting the frequencies of answers to the 31 questions (1 = Strongly Disagree, 5 = Strongly Agree) to the survey about the use of the TSGame tool.</p> <p>Figures reporting:</p> <ul> <li>the architecture of the TSGame tool</li> <li>Detection Game Web page</li> <li>Refactoring Game Web page</li> <li>Solution Sharing Web page</li> </ul>
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>
Smell the stress: Subjective ratings of body odors reliably indicate stress levels
<p><strong><span>Background:</span></strong><span> Chemosensory signals like body odor carry information about the emotional state of individuals like fear or stress. Analysis of sweat odor could therefore deliver an easy option of assessing stress. Our aim was to examine if subjective odor ratings can reliably and validly capture characteristics of sweat odor. For this we presented odor samples of stressed individuals to naïve participants who rated these concerning their intensity, valence, and stress. Furthermore, we compared odor rating results to well-studied physiological markers of stress that we obtained from the odor donors during the stress task.</span></p> <p><strong><span>Method:</span></strong><span> Odor donors were 30 men and 30 women who performed a standardized protocol to induce social stress while wearing axillary pads under each armpit. Additionally, we measured <span> </span><span> </span>cortisol in saliva, adrenaline in blood and recorded participants’ heart rate. For odor ratings, we recruited an independent sample of 40 individuals. Participants rated the odor samples using visual analog scales.</span></p> <p><strong><span>Results:</span></strong><span> Intraclass correlations revealed a fair level of clinical significance for intensity and valence of sweat odors. Additionally, retest reliability was moderate for these two odor qualities. Ratings of stress also showed a moderate retest reliability, but the intraclass correlation was on a poor level. In further analyses we found significant relationships between all three qualities. Correlations between subjective ratings of body odors and physiological stress markers of odor donors were not significant. </span></p> <p><strong><span>Discussion: </span></strong><span>Our data show that<strong> </strong>subjective ratings of body odor qualities, in particular intensity and valence are reliable. Further research on validity is needed.</span></p>
Understanding practitioners' strategies to handle test smells: a multi-method study [DATA]
<p>Test smells are poor design and implementation choices that can affect the test code's understanding and maintainability. Recent studies show the industry is not aware of the test smells concept, while software engineers commonly encounter obstacles to maintain test code. This study investigated test creation and maintenance strategies through developers' perception of eight test smells types. We surveyed 87 software testers and interviewed eight to understand their view on test smells in practice. Our results show that most participants use manual strategies for creating and maintaining test cases. Based on data captured from software testers' perspectives, this study contributes with possible directions and treatments to analyze test smells, seeking to understand how the test affects and potential solutions for test smells.</p>
Data from: Insectivorous birds can see and smell systemically herbivore‐induced pines
<p>Several studies have shown that insectivorous birds are attracted to herbivore-damaged trees even when they cannot see or smell the actual herbivores or their faeces. However, it often remained an open question whether birds are attracted by herbivore-induced changes in leaf odour or in leaf light reflectance or by both types of changes. Our study addressed this question by investigating the response of great tits (<em>Parus major</em>) and blue tits (<em>Cyanistes caeruleus</em>) to Scots pine (<em>Pinus sylvestris</em>) damaged by pine sawfly larvae (<em>Diprion pini</em>). We released the birds individually to a study booth, where they were simultaneously offered a systemically herbivore-induced and a non-infested control pine branch. In the first experiment, the birds could see the branches, but not smell them, because each branch was kept inside a transparent, air-tight cylinder. In the second experiment, the birds could smell the branches, but not see them, because each branch was placed inside a non-transparent cylinder with a mesh lid. The results show that the birds were more attracted to the herbivore-induced branch in both experiments. Hence, either type of the tested cues, the herbivore-induced visual plant cue alone as well as the olfactory cues <em>per se</em>, is attractive to the birds.</p>
Supplementary material for the paper "PyNose: A Test Smell Detector for Python"
<p>This archive contains:</p> <ol> <li>Lists of projects used in our study.</li> <li>A full list of examples of the newly introduced Suboptimal Assert test smell.</li> <li>An example of a change graph used to discover Python-specific test smells.</li> <li>The results of the small-scale mapping study: a full list of papers, a full list of test smells, and the correlation between them.</li> </ol> <p>You can find all the details in README.txt</p>
On the influence of Test Smells on Test Coverage
<p>The tests are part of the software quality assurance process, however, they do not have a quality check layer above, ie, there are no tests for the tests, which makes it difficult to guarantee the quality of the tests and for consequence of the software. However, the coverage metric can be applied to the test to measure its effectiveness. Even with high coverage, tests may have poor design choices, called \ textit {test smells}.<br> The aim of this research is to conduct a quantitative study to analyze possible correlations between test coverage, test size and quantification and typing of test smells.<br> Therefore, some improvements have been made to the Test Smell Detector tool, which we applied in eleven open source projects to obtain the search database. The R language and correlation tests were used to analyze the data.<br> From the exploration of the data, the research shows that there is little or no relationship between coverage and quantity of test smell, between coverage and lines of code, but on the other hand, there is a strong relation between number of smells and the size of the test.<br> In addition to the research to make contributions regarding the detection of smells, the data presented can help in a better understanding of the behavior of these bad choices in the test codes.</p>
JUnit Test Files with manual check of 5 test smells
<p>854 JUnit test methods and 146 helper methods from eight large open source GitHub repositories, which were manually checked for 5 different test smells: anonymous test, assertion roulette, long test, rotten green test and conditional test logic.</p> <p>Used in the following project: https://github.com/MaierFlorian/Deep-Test-Code-Analyzer</p>
The size of a smell: assessment of rival's size from femoral secretions in the common wall lizards
<p>Animal communication depends on signals conveying information to a receiver who must perceive and decode them. Signals involved in territoriality are usually complex stimuli that should be correctly interpreted to avoid unnecessary conflicts. Lacertids use both visual and chemical stimuli in modulating their aggressive response against conspecifics and the rival's size is one of the most important information, affecting the success probability in a combat. To assess the actual ability of decoding information about rival's size based on its chemical stimulus alone, 60 males of Podarcis muralis were tested for three consecutive days in an arena bearing a mirror (to simulate an equal-sized intruder), and the chemical cues (femoral secretions) from an unknown individual of different size. Significant differences were observed in tongue-flicks number, which grew as the size difference between the focal lizard and the secretion donor decreased. This can be interpreted as the need for the lizard to better evaluate the potential competitor's characteristics. The size difference also affected the number of bites against the mirror, which increased when the size of the focal lizard was larger than the donor triggering the aggressive response with a higher probability of winning the contest, as expected if the focal lizard had correctly decoded the information about the opponent's size by chemical stimulus. Although previous studies have shown that some components of the chemical signals are potentially informative about the signaller's size, this is the first demonstration that male P. muralis are actually able to decode and use such information.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.