6 research outputs found

    Reuse-Based Test Recommendation in Software Engineering

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    Still today, the development of effective and high-quality software tests is an expensive and very labor intensive process. It demands a high amount of problem awareness, domain knowledge and concentration from human software testers. Therefore, any technology that can help reduce the manual effort involved in the software testing process -- while ensuring at least the same level of quality -- has the potential to significantly reduce software development and maintenance costs. In this dissertation, we present a new idea for achieving this by reusing the knowledge bound up in existing tests. Over the last two decades, software reuse and code recommendation has received a wide variety of attention in academia and industry, but the research conducted in this area to date has focused on the reuse of application code rather than on the reuse of tests. By switching this focus, this thesis paves the way for the automated extraction of test data and knowledge from previous software projects. In particular, it presents a recommendation approach for software tests that leverages lessons learned from traditional software reuse to make test case reuse suggestions to software engineers while they are working. In contrast to most existing testing-assistance tools, which provide ex post assistance to test developers in the form of coverage assessments and test quality evaluations, our approach offers an automated, proactive, non-intrusive test recommendation system for efficient software test development

    Realizing Automated Test Recommendations in Software Development Environments

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    Software testing is a mainly manually performed and thus very labour intensive process. Beside time, it demands a high amount of domain knowledge, concentration and problem awareness from the developer. Although software reuse is a well examined area –in both academia and industry – it is mainly focussed on the reuse of different kinds of documentation and program code. In this thesis we create a client-side recommendation system for the novel idea for an automated test recommendation approach that is based on lessons learned from traditional software reuse and recommendation. While most existing testing assistance systems help a developer by providing information about various coverage criteria only ex post, we want to support the developer pro-actively while writing the test and create as little overhead as possible during his work. Thereby we benefit from the lessons learned in the area of ”traditional” software reuse and apply them in a kind of test reuse for test recommendation approach. To validate our theoretical considerations, we present a tool that will help writing tests with less effort

    Forschungsbericht Universität Mannheim 2010 / 2011

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    Der Forschungsbericht bietet Ihnen eine Übersicht über die Forschungsschwerpunkte der Fakultäten, Abteilungen und Forschungseinrichtungen der Universität Mannheim. Dazu enthält der vorliegende Forschungsbericht Informationen über Einzelprojekte in den jeweiligen Fachdisziplinen sowie über zumeist fächerübergreifende Verbundprojekte wie Sonderforschungsbereiche, Forschergruppen, Wissenschaftscampi, Graduiertenschulen und Promotionskollegs. Die aus den Forschungsaktivitäten hervorgegangenen Publikationen, die Sie in diesem Bericht aufgelistet finden, leisten wichtige Beiträge zum wissenschaftlichen Fortschritt innerhalb der Disziplinen. Die ebenfalls aufgeführten Transferleistungen stellen Beiträge der Grundlagenwissenschaft zur Lösung gesellschaftlicher und wirtschaftlicher Herausforderungen dar. Nicht zuletzt enthält der Forschungsbericht Angaben zu wissenschaftlichen Preisen und Auszeichnungen, zu Veranstaltungen und Tagungen sowie zu akademischen Qualifikationen im Sinne von Promotionen und Habilitationen. Diese Angaben reflektieren die Reputation der Wissenschaftlerinnen und Wissenschaftler und ergänzen die sonstigen forschungsbezogenen Leistungen an der Universität Mannheim

    Search-enhanced testing: NIER track

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    Discrepancy Discovery in Search-Enhanced Testing

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