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144 results for “Test generation”
Empirical Study Data for Test Program-Based Generative Fuzzing for Differential Testing of the Kotlin Compiler
<p>Empirical Study Dataset for MSc. Thesis titled "Test Program-Based Generative Fuzzing for Differential Testing of the Kotlin Compiler". The data contains automatically generated Kotlin files, the results of differentially testing the generated files, and aggregated data containing file information, and compilation features.</p>
Personalised Therapy for Metastatic ADPC Determined by Genetic Testing and Avatar Model Generation
ClinicalTrials.gov study NCT02795650. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Thrombin Generation Assay (TGA) as Predictive Test for Haemostatic. Effectiveness of FVIII Concentrates in Haemophiliac A With Inhibitors
ClinicalTrials.gov study NCT01505946. IPD Sharing: Not stated. Countries: 1. Publications: 18.
Towards a Wearable Alcohol Biosensor: Examining the Accuracy of BAC Estimates From New-Generation Transdermal Technology Using Large-Scale Human Testing and Machine Learning Algorithms
ClinicalTrials.gov study NCT05692830. IPD Sharing: UNDECIDED. Countries: 1. Publications: 27.
Assessing Physical Fitness in Cancer Patients With Cardiopulmonary Exercise Testing and Wearable Data Generation
ClinicalTrials.gov study NCT02786628. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Does the Thrombin Generation Test Performed During the Pharmacokinetic Profile of the Substitutive Factor VIII Bring Benefits to the Personalized Treatment of Pediatric Patients and Adult Hemophilia A
ClinicalTrials.gov study NCT02803502. IPD Sharing: Not stated. Countries: 1. Publications: 3.
A Worldwide Cancer Registry Enrolling Participants Profiled With a Next-Generation Sequencing Test
ClinicalTrials.gov study NCT04529122. IPD Sharing: YES. Countries: 32. Publications: 1.
Shock Testing to Unmask RV Lead and GEnerator Malfunction in ICD Patients: the SURGE-ICD Registry
ClinicalTrials.gov study NCT04858672. IPD Sharing: NO. Countries: 1. Publications: 10.
Testing the Efficacy of Self-Generation Treatment Protocol
ClinicalTrials.gov study NCT02032589. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Is biasing offspring sex ratio adaptive? a test of Fisher’s principle across multiple generations of a wild mammal in a fluctuating environment
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Analytical validation and field testing of a specific qPCR assay for environmental DNA detection of invasive European green crab - Collected and Generated Data
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Data from: Generation of a neutral FST baseline for testing local adaptation on gill-raker number within and between European whitefish ecotypes in the Baltic Sea basin
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Data from: Within-generation consequences of post-settlement mortality for trait composition in wild populations: an experimental test
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Data from: how to generate and test hypotheses about colour: insights from half a century of guppy research
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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>
Manually written or Generated tests? a study with developers and maintenance tasks
<p>Software testing is an important part of software development. In addition to finding faults in the code, good tests should help developers to correct faults and be easily updated in case of modifications due to code changes. Automatically generated tests can save time and may lead to higher code coverage. However, these tests may be less readable and not based on realistic scenarios.<br> Few pieces of research have been done to evaluate whether automatically generated tests are maintainable and support developers when maintaining code. To further investigate this issue, we perform an empirical study with 20 real developers to compare how they perform maintenance tasks with automatically generated (Evosuite or Randoop) and manually-written test cases. Our results indicate that automatically generated tests can be a great help for identifying faults during maintenance. Also, we found that all strategies were similar at helping to produce correct bug fixes and with similar efficiency. Therefore, we can say that developers may integrate generated test suites into the project at any stage.</p>
A Large Scale Study On the Effectiveness of Manual and Automatic Unit Test Generation
<p>Recently, an increasingly large amount of effort has been devoted to implementing tools to generate unit test suites automatically. Previous studies have investigated the effectiveness of these tools by comparing automatically generated test suites (ATSs) to manually written test suites (MTSs). Most of these studies report that ATSs can achieve higher code coverage, or even mutation coverage, than MTSs, particularly when suites are generated from defective code. However, these studies usually consider a limited amount of classes or subject programs, while the adoption of such tools in the industry is still low. This work aims to compare the effectiveness of ATSs and MTSs when applied as regression test suites. We conduct an empirical study, using ten programs (1368 classes), written in Java, that already have MTSs and apply two sophisticated tools that automatically generate test cases: Randoop and EvoSuite. To evaluate the test suites’ effectiveness, we use line and mutation coverage. Our results indicate that MTSs are, in general, more effective than ATSs regarding the investigated metrics. Moreover, the number of generated test cases may not indicate test suites’ effectiveness. Furthermore, there are situations when ATSs are more effective, and even when ATSs and MTSs can be complementary.</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>
Data from: A test of the hypothesis that correlational selection generates genetic correlations
Theory predicts that correlational selection on two traits will cause the major axis of the bivariate G matrix to orient itself in the same direction as the correlational selection gradient. Two testable predictions follow from this: for a given pair of traits (1) the sign of correlational selection gradient should be the same as that of the genetic correlation, and (2) the correlational selection gradient should be positively correlated with the value of the genetic correlation. We test this hypothesis with a meta-analysis utilizing empirical estimates of correlational selection gradients and measures of the correlation between the two focal traits. Our results are consistent with both predictions and hence support the underlying hypothesis that correlational selection generates a genetic correlation between the two traits and hence orients the bivariate G matrix.
Automated Test Case Generation - Master Thesis
<p>This is the dataset used and created within my master thesis.</p>
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OpenNeuro
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