05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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Institute: search.filters.UBSInstitut.Institut für SoftwaretechnologieInstitute: search.filters.UBSInstitut.Fakultätsübergreifend / Sonstige EinrichtungPublication Type: search.filters.UBSTyp.KonferenzbeitragStart date: 2019End date: 2019

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    Interdisciplinary system courses - teaching agile systems engineering
    (2019) Seitz, Andreas; Avezum, Mariana; Bruegge, Bernd; Wagner, Stefan
    With the advent of technologies like the Internet of Things, Industry 4.0 and Cyber-Physical Systems, many software engineering courses turn into system engineering courses. Recent advances in technologies such as 3D printing and low-cost micro controllers enable to teach agile hard- and software co-design in system engineering courses. In this paper, we describe Interdisciplinary System Courses (ISC) - a teaching approach based on interdisciplinary projects, light-weight agile techniques and solving real problems by integrating industry customers. We describe our experiences from an exploratory case study where we applied ISC in a two-week international summer school with a customer from the aerospace industry. We derive a set of hypotheses on the effects of ISC.
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    Is the stack distance between test case and method correlated with test effectiveness?
    (2019) Niedermayr, Rainer; Wagner, Stefan
    Mutation testing is a means to assess the effectiveness of a test suite and its outcome is considered more meaningful than code coverage metrics. However, despite several optimizations, mutation testing requires a significant computational effort and has not been widely adopted in industry. Therefore, we study in this paper whether test effectiveness can be approximated using a more light-weight approach. We hypothesize that a test case is more likely to detect faults in methods that are close to the test case on the call stack than in methods that the test case accesses indirectly through many other methods. Based on this hypothesis, we propose the minimal stack distance between test case and method as a new test measure, which expresses how close any test case comes to a given method, and study its correlation with test effectiveness. We conducted an empirical study with 21 open-source projects, which comprise in total 1.8 million LOC, and show that a correlation exists between stack distance and test effectiveness. The correlation reaches a strength up to 0.58. We further show that a classifier using the minimal stack distance along with additional easily computable measures can predict the mutation testing result of a method with 92.9% precision and 93.4% recall. Hence, such a classifier can be taken into consideration as a light-weight alternative to mutation testing or as a preceding, less costly step to that.