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232 results for “smell”
The Effect Of Baby Smell On The Amount Of Breast Milk, Salivary Cortisol Level And Mother Baby Attachment
ClinicalTrials.gov study NCT06096961. IPD Sharing: NO. Countries: 1. Publications: 12.
Smell and Taste Disorders in COVID-19 Patients
ClinicalTrials.gov study NCT04427332. IPD Sharing: NO. Countries: 1. Publications: 8.
Olfactory Training in COVID-19 Associated Loss of Smell
ClinicalTrials.gov study NCT05421221. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: I smell where you walked – how chemical cues influence movement decisions in ants
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A dataset of code smells
<p>This dataset contains code smells (implementation, design, and architecture smells) mined from 3073 open-source C# projects.</p>
An empirical study of automatically-generated tests from the perspective of test smells
<p>Developing software test code can be as or more expensive than developing software production code. Commonly, developers use automated unit test generators to speed up software testing. The purpose of such tools is to shorten production time without decreasing code quality. Nonetheless, unit tests usually do not have a quality check layer above testing code, which might be hard to guarantee the quality of the generated tests. An emerging strategy to verify the tests quality is to analyze the presence of test smells in software test code. Test smells are characteristics in the test code that possibly indicate weaknesses in test design and implementation. The presence of test smells in unit test code could be used as an indicator of unit test quality. In this paper, we present an empirical study aimed to analyze the quality of unit test code generated by automated test tools. We compare the tests generated by two tools (Randoop and EvoSuite) with the existing unit test suite of open-source software projects. We analyze the unit test code of twenty-one open-source Java projects and detected the presence of nineteen types of test smells. The results indicated significant differences in the unit test quality when comparing data from both automated unit test generators and existing unit test suites.</p>
Replication package with data used in the study: "The effect of code smells on the relationship between design patterns and defects. An empirical study"
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells on the relationship between design patterns and defects. An empirical study"</p> <p>This dataset contains the following folders:</p> <ul> <li> <p>"Analyzed systems" folder:</p> <ul> <li> <p>For each subject system (Ant-1.7, JEdit-4.2, Lucene-2.4, Camel-1.6, Log4j-1.2, Xalan-2.7, Poi-3.0, Ivy-2.0, Xerces-2.0, Velocity-1.6), we identify the following datasets: SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</p> </li> <li> <p>Those csv files include raw data about every class in every release. Each file includes columns which represent:</p> <ul> <li> <p>System: The analyzed system</p> </li> <li> <p>className: A fully qualified class name</p> </li> <li> <p>Pattern: if the class is part of any pattern the cell contains the name of the pattern, and “null” otherwise</p> </li> <li> <p>Smell: if the class is part of affected by any smell the cell contains the name of the smell, and “null” otherwise</p> </li> <li> <p>Bugs: Number of defects reported inside the class (extracted from the PROMISE dataset)</p> </li> </ul> </li> </ul> </li> <li> <p>A "detailed analysis" folder:</p> <ul> <li> <p>For each pattern, we report all the classes which participate in it in all the analyzed systems. The csv files inside this folder follow the same structure as the other csv files reported above</p> </li> </ul> </li> </ul> <p><br> </p>
An empirical study of automatically-generated tests from the perspective of test smells
<p>Developing software test code can be as or more expensive than developing software production code. Commonly, developers use automated unit test generators to speed up software testing. The purpose of such tools is to shorten production time without decreasing code quality. Nonetheless, unit tests usually do not have a quality check layer above testing code, which might be hard to guarantee the quality of the generated tests. A strategy to verify the tests quality is to analyze the presence of test smells in software test code. Test smells are characteristics in the test code that possibly indicate weaknesses in test design and implementation. Their presence in test code could be used as a quality indicator. In this paper, we present an empirical study to analyze the quality of unit test code generated by automated test tools. We compare the tests generated by two tools (Randoop and EvoSuite) with the existing unit test suite of twenty-one open-source Java projects. We analyze the unit test code to detect the presence of nineteen types of test smells. The results indicated significant differences in the unit test quality when comparing data from the automated unit test generators and existing unit test suites.</p>
Quantifying the Effects of Refactorings on Bad Smells
<p>Refactoring aims to remove bad smells and increase software maintainability by improving the software structure without changing its behavior. Even with the existence of tools to assist refactoring, many developers do not trust in their solutions, claiming that some studies show that refactoring may even introduce new bad smells into the source code. However, we do not find a complete catalog that states when this may occur. To investigate this subject deeply, the goal of this dissertation is to evaluate the effects of refactoring on the detection of bad smells. Specifically, we want to know if and what refactoring removes bad smells or introduces them. To achieve our goal, we plan to conduct empirical studies to provide a catalog showing these situations.</p>
Investigating Non-Usually Employed Features in the Identification of Architectural Smells: A Machine Learning-Based Approach
<p>Architectural smells (ASs) negatively affect the maintenance and evolution of software at the architectural level. Most of the current approaches for ASs identification rely on the same small and well-known set of usually employed metrics (UE-Ms) with fixed thresholds. Machine learning (ML) is a promising technique for smell identification as algorithms can learn from a rich set of metrics/features, covering several characteristics of the software and incorporating a certain degree of subjectivity. This has been explored by building datasets with a robust and rich set of features, including not only the UE-Ms but also other non-usually employed metrics (NUE-Ms). However, usually the UE-Ms determine the output of the algorithms, obfuscating other metrics that have the potential to improve the classification. This also leads to inflated and difficult to maintain datasets. <br> In this paper, we investigate the accuracy of some ML algorithms employing only NUE-Ms. We scoped our study in the classification of two smells: God Component and Unstable Dependency. This investigation revealed a set of NUE-Ms that can be also used to identify these smells and the contribution of each one for the classification. Based on this information, software engineers can then build a final dataset just with the potential features. We also briefly present our tool, called InSet, that was used by academics and practitioners to identify smells in their systems. The feedback of them was used as the oracle to compare our tool to other approaches and good results were reached. </p>
JNose: Java Test Smell Detector
<p>Several strategies have been proposed for test quality measurement and analysis. Code coverage is likely the most widely used one. It enables to verify the ability of a test case to cover as many source code branches as possible. Although code coverage has been widely used, novel strategies have been recently employed. It is the case of test smells analysis, which has been introduced as an affordable strategy to evaluate the quality of test code. Test smells are poor design choices in implementation, and their occurrence in test code might reduce the quality of test suites. Test smells identification is clearly dependent on tool support, otherwise it could become a cost-ineffective strategy. However, as far as we know, there is no tool that combines code coverage and test smells to address test quality measurement. In this work, we present the JNose Test, a tool aimed to analyze test suite quality in the perspective of test smells. JNose Test detects code coverage and software evolution metrics and a set of test smells throughout software versions.</p>
Replication Package for the Paper: "Code Smells Detection via Code Review: An Empirical Study"
<p>This repository contains the data and results from the paper "Code Smells Detection via Code Review: An Empirical Study" submitted to ESEM 2020.</p> <p> </p> <p><strong>1. data folder</strong></p> <p>The data folder contains the retrieved 269 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell Term, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2. scripts floder</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>smell-term/general_smell_terms.txt</em> contains general code smell terms, such as "code smell".</li> <li><em>smell-term/specific_smell_terms.txt</em> contains specific code smell terms, such as "dead code".</li> <li><em>smell-term/misspelling_terms_of_smell.txt</em> contains the misspelling terms of 'smell', such as "ssell".</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>smell_search.py</em> is used for searching review comments that contain code smell terms.</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>
Replication package with data used in the study: The effect of code smells and design patterns on two change-related metrics: An exploratory study
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells and design patterns on two change-related metrics: An exploratory study"</p> <p>This dataset contains the following folders:</p> <ul> <li>Aggregated Results Per System <ul> <li> For each subject system (AOI, Jedit, JHotDraw), we identify the following datasets: DP, nDP, S, nS ,SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</li> <li> Those csv files include raw data about every class in every release, the csv files also include columns which represent: <ul> <li>- CHURN (CLPLPR(C)*100): defined as the sum of added and deleted lines in a class in a release, adjusted to the size of the class and to the number of revisions in the release;</li> <li>- and FREQ (MTPR(C)*100): defined as the average number of changes made to a class in a release, adjusted to the number of revisions in the release</li> </ul> </li> </ul> </li> <li>Detailed Results Per Smell Or Pattern <ul> <li> For each specific code smell (S) in each public release (Rel) of all subject systems, we identify SDP and SnDP datasets. Each dataset is in a separate .csv file</li> <li> For each specific design pattern (DP) in each public release (Rel) of all subject systems, we identify SDP and nSDP </li> </ul> </li> <li>Plots<br> We also include QQ plots for CHURN, FREQ values for every dataset in every system, that could serve as a supplementary data for the paper.</li> </ul>
The smell of cooperation: rats increase helpful behaviour when receiving odour cues of a conspecific performing a cooperative task
<p>Reciprocity can explain cooperative behaviour among non-kin, where individuals help others depending on their experience in previous interactions. Norway rats <i>(Rattus norvegicus)</i> cooperate reciprocally according to direct and generalized reciprocity. In a sequence of four consecutive experiments, we show that odour cues from a cooperating conspecific are sufficient to induce altruistic help of rats in a food-exchange task. When rats were enabled to help a non-cooperative partner while receiving olfactory information from a rat helping a conspecific in a different room, they helped their non-cooperative partner as if it was a cooperative one. We further show that the cues inducing altruistic behaviour are released during the act of cooperation and do not depend on the identity of the cue provider. Remarkably, olfactory cues seem to be more important for cooperation decisions than experiencing a cooperative act per se. This suggests that rats signal their cooperation propensity to social partners, which increases their chances to receive help in return.</p>
Data from: Wake up and smell the conflict: odour signals in female competition
Odour signals used in competitive and aggressive interactions between males are well studied in the context of sexual selection. By contrast, relatively little is known about comparable signals used by females, despite current interest in the evolution of female ornaments and weaponry. Available evidence suggests that odour signals are important in competitive interactions between female mammals, with reductions or reversals of male-biased sexual dimorphism in signalling where female competition is intense. Scent marking is often associated with conflict between females over access to resources or reproductive opportunities. Female scent marks may therefore provide reliable signals of competitive ability that could be used both by competitors and potential mates. Consistent with this hypothesis, we report that aggressive behaviour of female house mice is correlated with the amount of major urinary protein (MUP) excreted in their urine, a polymorphic set of proteins that are used in scent mark signalling. Under semi-natural conditions, females with high MUP output are more likely to produce offspring sired by males that have high reproductive success, and less likely to produce offspring by multiple different sires, suggesting that females with strong MUP signals are monopolized by males of particularly high quality. We conclude that odour signals are worthy of more detailed investigation as mediators of female competition.
Data from: The smell of parents: breeding status influences cuticular hydrocarbon pattern in the burying beetle Nicrophorus vespilloides
The waxy layer of the cuticle has been shown to play a fundamental role in recognition systems of insects. The biparental burying beetle Nicrophorus vespilloides is known to have the ability to discriminate between breeding and non-breeding conspecifics and also here cuticular substances could function as recognition cue. However, it has not yet been demonstrated that the pattern of cuticular lipids can reflect the breeding status of a beetle or of any other insect. With chemical analysis using coupled gas chromatography–mass spectrometry, we showed that the chemical signature of N. vespilloides males and females is highly complex and changes its feature with breeding status. Parental beetles were characterized by a higher amount of some unusual unsaturated hydrocarbons than beetles which are not caring for larvae. The striking correlation between cuticular profiles and breeding status suggests that cuticular hydrocarbons inform the beetles about parental state and thus enable them to discriminate between their breeding partner and a conspecific intruder. Furthermore, we found evidence that nutritional conditions also influence the cuticular profile and discuss the possibility that the diet provides the precursors for the unsaturated hydrocarbons observed in parental beetles. Our study underlines the fact that the cuticular pattern is rich of information and plays a central role in the burying beetles' communication systems.
Can personality types be blamed for code smells
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COMMUNICATION SYSTEM AND MEANS OF SMELL
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Replication package - Triaging Microservice Security Smells, with TriSS
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Replication Package: Architectural Smell Detection
<p>Replication package of architectural smell detection paper</p>
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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)
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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.