Test Case Selection for Black-Box Regression Testing of Database Applications
2013 (English)In: Information and Software Technology, ISSN 0950-5849, Vol. 55, no 10, 1781-1795 p.Article in journal (Refereed) Published
Context: This paper presents an approach for selecting regression test cases in the context of large-scale, database applications. We focus on a black-box (speciﬁcation-based) approach, relying on classiﬁcation tree models to model the input domain of the system under test (SUT), in order to obtain a more practical and scalable solution. We perform an industrial case study where the SUT is a large database application in Norway’s tax department. Objective: We investigate the use of similarity-based test case selection for supporting black box regression testing of database applications. We have developed a practical approach and tool (DART) for functional black-box regression testing of database applications. In order to make the regression test approach scalable for large database applications, we needed a test case selection strategy that reduces the test execution costs and analysis e ort. We used classiﬁcation tree models to partition the input domain of the SUT in order to then select test cases. Rather than selecting test cases at random from each partition, we incorporated a similarity-based test case selection, hypothesizing that it would yield a higher fault detection rate. Method: An experiment was conducted to determine which similarity-based selection algorithm was the most suitable in selecting test cases in large regression test suites, and whether similarity-based selection was a worthwhile and practical alternative to simpler solutions. Results: The results show that combining similarity measurement with partition-based test case selection, by using similarity-based test case selection within each partition, can provide improved fault detection rates over simpler solutions when speciﬁc conditions are met regarding the partitions. Conclusions: Under the conditions present in the experiment the improvements were marginal. However, a detailed analysis concludes that the similarity-based selection strategy should be applied when a large number of test cases are contained in each partition and there is signiﬁcant variability within partitions. If these conditions are not present, incorporating similarity measures is not worthwhile, since the gain is negligible over a random selection within each partition.
Place, publisher, year, edition, pages
Elsevier , 2013. Vol. 55, no 10, 1781-1795 p.
Test case selection, Regression testing, Database applications, Similarity measures
IdentifiersURN: urn:nbn:se:bth-6874DOI: 10.1016/j.infsof.2013.04.004ISI: 000323298000008Local ID: oai:bth.se:forskinfoD44746DA532D70BBC1257B7D003F1C2FOAI: oai:DiVA.org:bth-6874DiVA: diva2:834428