PROJECT TITLE :
Clustering Deviations for Black Box Regression Testing of Database Applications
Regression tests typically lead to many deviations (variations between two system versions), either because of changes or regression faults. For the tester to analyze such deviations efficiently, it might be helpful to accurately group them, such that each group contains deviations representing one unique modification or regression fault. Because it's unlikely that a general resolution to the higher than downside can be found, we focus our work on a common sort of software system: database applications. We investigate the utilization of clustering, based mostly on database manipulations and check specifications (from take a look at models), to group regression check deviations consistent with the faults or changes inflicting them. We additionally propose assessment criteria based on the concept of entropy to match various clustering strategies. To validate our approach, we have a tendency to ran a giant scale industrial case study, and our results show that our clustering approach will indeed function an accurate strategy for grouping regression test deviations. Among the four take a look at campaigns assessed, deviations were clustered perfectly for 2 of them, while for the opposite two, the clusters were all homogenous. Our analysis suggests that this approach can considerably cut back the trouble spent by testers in analyzing regression check deviations, increase their level of confidence, and therefore build regression testing more scalable.
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