A Controlled Experiment Assessing Test Case Prioritization Techniques via Mutation Faults

Regression testing is an important part of software maintenance, but it can also be very expensive. To reduce this expense, software testers may prioritize their test cases so that those that are more important are run earlier in the regression testing process. Previous work has shown that prioritization can improve a test suite’s rate of fault detection, but the assessment of prioritization techniques has been limited to hand-seeded faults, primarily due to the belief that such faults are more realistic than automatically generated (mutation) faults. A recent empirical study, however, suggests that mutation faults can be representative of real faults. We have therefore designed and performed a controlled experiment to assess the ability of prioritization techniques to improve the rate of fault detection techniques, measured relative to mutation faults. Our results show that prioritization can be effective relative to the faults considered, and they expose ways in which that effectiveness can vary with characteristics of faults and test suites. We also compare our results to those collected earlier with respect to the relationship between hand-seeded faults and mutation faults, and the implications this has for researchers performing empirical studies of prioritization.

Scaling a Dataflow Testing Methodology to the MultiparadigmWorld of Commercial Spreadsheets

Spreadsheets are widely used but often contain faults. Thus, in prior work we presented a data-flow testing methodology for use with spreadsheets, which studies have shown can be used cost-effectively by end-user programmers. To date, however, the methodology has been investigated across a limited set of spreadsheet language features. Commercial spreadsheet environments are multiparadigm languages, utilizing features not accommodated by our prior approaches. In addition, most spreadsheets contain large numbers of replicated formulas that severely limit the efficiency of data-flow testing approaches. We show how to handle these two issues with a new data-flow adequacy criterion and automated detection of areas of replicated formulas, and report results of a controlled experiment investigating the feasibility of our approach.