Dissimilarity-based test case prioritization through data fusion

Rubing Huang, Dave Towey, Yinyin Xu, Yunan Zhou, Ning Yang

Research output: Journal PublicationArticlepeer-review

3 Citations (Scopus)


Test case prioritization (TCP) aims at scheduling test case execution so that more important test cases are executed as early as possible. Many TCP techniques have been proposed, according to different concepts and principles, with dissimilarity-based TCP (DTCP) prioritizing tests based on the concept of test case dissimilarity: DTCP chooses the next test case from a set of candidates such that the chosen test case is farther away from previously selected test cases than the other candidates. DTCP techniques typically only use one aspect/granularity of the information or features from test cases to support the prioritization process. In this article, we adopt the concept of data fusion to propose a new family of DTCP techniques, data-fusion-driven DTCP (DDTCP), which attempts to use different information granularities for prioritizing test cases by dissimilarity. We performed an empirical study involving 30 versions of five subject programs, investigating the testing effectiveness and efficiency by comparing DDTCP against DTCP techniques that use a dissimilarity granularity. The experimental results show that not only does DDTCP have better fault-detection rates than single-granularity DTCP techniques, but it also appears to only incur similar prioritization costs. The results also show that DDTCP remains robust over multiple system releases.

Original languageEnglish
Pages (from-to)1352-1377
Number of pages26
JournalSoftware - Practice and Experience
Issue number6
Early online date18 Jan 2022
Publication statusPublished - Jun 2022


  • data fusion
  • dissimilarity
  • regression testing
  • software testing
  • test case prioritization


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