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26 results for “test smells”
DATASET - AVALIAÇÃO EMPÍRICA DA GERAÇÃO AUTOMATIZADA DE TESTES DE SOFTWARE SOB A PERSPECTIVA DE TEST SMELLS
<p>A constante busca pela qualidade sempre está em destaque na área de Engenharia de Software. Dentre as diversas disciplinas dedicadas a essa temática, o teste de software tem se estabelecido como uma das mais importantes, dado sua eficácia na identificação de defeitos, em momento prévio à liberação de sistemas de software para o mercado. O teste de software é atividade-chave para o desenvolvimento de software de qualidade. Entretanto, desenvolver testes é tão ou mais custoso do que desenvolver o código de produção. Uma alternativa para a redução dos custos associados ao teste de software se dá pelo uso intensivo de ferramentas de automação de testes. A proposta dessas ferramentas é reduzir o tempo de produção sem afetar a qualidade do código. Apesar dessa premissa, não é comum encontrar abordagens que incluam uma camada de verificação de qualidade dos testes gerados automaticamente, o que pode reduzir a confiabilidade da eficácia desses testes. Neste cenário, a proposta dessa dissertação é analisar empiricamente massas de dados de teste, sob a perspectiva de test smells, no sentido de avaliar a qualidade dos testes produzidos por ferramentas de geração automatizada de testes de software. Test smells são más escolhas no design dos testes e tem características sintomáticas e podem acarretar diminuição na qualidade dos sistemas. Considerando os test smells em código de teste, o estudo analisa os testes gerados por duas ferramentas amplamente aceitas pela comunidade de testes: Evosuite e Randoop. Um conjunto de vinte e um projetos de software de código aberto, disponíveis na plataforma Github foram considerados no estudo. A análise considerou a dispersão de test smells no código de teste desses projetos, bem como a existência de potenciais correlações entre test smells e as relações com as métricas estruturais. Como principais resultados, encontramos fortes correlações entre os test smells e as métricas de cobertura do código, diferenças significativas entre os dados encontrados nas suítes de testes geradas automaticamente e com os testes pré-existentes nos projetos avaliados.</p>
Replication package of "Revisiting Test Smells in Automatically Generated Tests: Limitations, Pitfalls, and Opportunities"
<p><strong>Abstract:</strong><br> Test smells attempt to capture design issues in test code that reduce their maintainability. Previous work found such smells to be highly common in automatically generated test-cases, but based this result on specific static detection rules; although these are based on the original definition of “test smells”, a recent empirical study showed that developers perceive these as overly strict and non-representative of the maintainability and quality of test suites. This leads us to investigate how effective such test smell detection tools are on automatically generated test suites. In this paper, we build a dataset of 2,340 test cases automatically generated by EVOSUITE for 100 Java classes. We performed a multi-stage, cross-validated manual analysis to identify six types of test smells and label their instances. We benchmark the performance of two test smell detection tools: one widely used in prior work, and one recently introduced with the express goal to match developer perceptions of test smells. Our results show that these test smell detection strategies poorly characterized the issues in automatically generated test suites; the older tool’s detection strategies, especially, misclassified over 70% of test smells, both missing real instances (false negatives) and marking many smell-free tests as smelly (false positives). We identify common patterns in these tests that can be used to improve the tools, refine and update the definition of certain test smells, and highlight as of yet uncharacterized issues. Our findings suggest the need for (i) more appropriate metrics to match development practice; and (ii) more accurate detection strategies, to be evaluated primarily in industrial contexts.</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>
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>
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>
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>
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>
Validation of a Rapid Quantitative Test for Loss of Smell in COVID-19 Subjects
ClinicalTrials.gov study NCT04431908. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Refactoring Assertion Roulette and Duplicate Assert test smells: a controlled experiment [DATA]
<p>Data of the experiment with the RAIDE tool.</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>
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>
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>
SNUTS.js: Sniffing Nasty Unit Test Smells in Javascript
<p>Manual Test Smell Detect</p>
SNUTS.js: Sniffing Nasty Unit Test Smells in Javascript Creators
<p>projects selected for the article</p>
On The Relation of Test Smells to Software Code Quality. Appendix.
<p>Dataset of "On The Relation of Test Smells to Software Code Quality"</p> <p>This is the replication package of the paper "On The Relation of Test Smells to Software Code Quality", accepted at the 34th IEEE International Conference on Software Maintenance and Evolution (ICSME 18).</p>
Experiments to Test How and Why the Sense of Smell Differs Between People
ClinicalTrials.gov study NCT00220038. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Impact of Smell Testing on Health Outcomes
ClinicalTrials.gov study NCT07222891. IPD Sharing: YES. Countries: 1. Publications: 0.
Taste And Smell TEsting in End Stage Renal Disease
ClinicalTrials.gov study NCT03043144. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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