S. Eder, B. Hauptmann, Maximilian Junker, Rudolf Vaas, Karl-Heinz Prommer
{"title":"Selecting manual regression test cases automatically using trace link recovery and change coverage","authors":"S. Eder, B. Hauptmann, Maximilian Junker, Rudolf Vaas, Karl-Heinz Prommer","doi":"10.1145/2593501.2593506","DOIUrl":null,"url":null,"abstract":"Regression tests ensure that existing functionality is not impaired by changes to an existing software system. However, executing complete test suites often takes much time. Therefore, a subset of tests has to be found that is sufficient to validate whether the system under test is still valid after it has been changed. This test case selection is especially important if regression tests are executed manually, since manual execution is time intensive and costly. To select manual test cases, many regression testing techniques exist that aim on achieving coverage of changed or even new code. Many of these techniques base on coverage data from prior test runs or logical properties such as annotated pre and post conditions in the source code. However, coverage information becomes outdated if a system is changed extensively. Also annotated logical properties are often not available in industrial software systems. We present an approach for test selection that is based on static analyses of the test suite and the system's source code. We combine trace link recovery using latent semantic indexing with the metric change coverage, which assesses the coverage of source code changes. The proposed approach works automatically without the need to execute tests beforehand or annotate source code. Furthermore, we present a first evaluation of the approach.","PeriodicalId":443108,"journal":{"name":"International Conference/Workshop on Automation of Software Test","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference/Workshop on Automation of Software Test","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2593501.2593506","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Regression tests ensure that existing functionality is not impaired by changes to an existing software system. However, executing complete test suites often takes much time. Therefore, a subset of tests has to be found that is sufficient to validate whether the system under test is still valid after it has been changed. This test case selection is especially important if regression tests are executed manually, since manual execution is time intensive and costly. To select manual test cases, many regression testing techniques exist that aim on achieving coverage of changed or even new code. Many of these techniques base on coverage data from prior test runs or logical properties such as annotated pre and post conditions in the source code. However, coverage information becomes outdated if a system is changed extensively. Also annotated logical properties are often not available in industrial software systems. We present an approach for test selection that is based on static analyses of the test suite and the system's source code. We combine trace link recovery using latent semantic indexing with the metric change coverage, which assesses the coverage of source code changes. The proposed approach works automatically without the need to execute tests beforehand or annotate source code. Furthermore, we present a first evaluation of the approach.