Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/654
Title: Search algorithms for regression test case prioritization
Authors: Li, Z
Harman, M
Hierons, RM
Keywords: Search techniques;Test case prioritisation;Regression testing
Issue Date: 2007
Publisher: IEEE
Citation: IEEE Transactions on Software Engineering, 33(4): 225-237, Apr 2007.
Abstract: Regression testing is an expensive, but important, process. Unfortunately, there may be insufficient resources to allow for the re-execution of all test cases during regression testing. In this situation, test case prioritisation techniques aim to improve the effectiveness of regression testing, by ordering the test cases so that the most beneficial are executed first. Previous work on regression test case prioritisation has focused on Greedy Algorithms. However, it is known that these algorithms may produce sub-optimal results, because they may construct results that denote only local minima within the search space. By contrast, meta-heuristic and evolutionary search algorithms aim to avoid such problems. This paper presents results from an empirical study of the application of several greedy, meta-heuristic and evolutionary search algorithms to six programs, ranging from 374 to 11,148 lines of code for 3 choices of fitness metric. The paper addresses the problems of choice of fitness metric, characterisation of landscape modality and determination of the most suitable search technique to apply. The empirical results replicate previous results concerning Greedy Algorithms. They shed light on the nature of the regression testing search space, indicating that it is multi-modal. The results also show that Genetic Algorithms perform well, although Greedy approaches are surprisingly effective, given the multi-modal nature of the landscape.
URI: http://bura.brunel.ac.uk/handle/2438/654
Appears in Collections:Computer Science
Dept of Computer Science Research Papers
Software Engineering (B-SERC)
Software Engineering (B-SERC)

Files in This Item:
File Description SizeFormat 
Fulltext.pdf3.67 MBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.