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An Experimental Evaluation of Data Flow and Mutation Testing
Author(s) -
Offutt A. Jefferson,
Pan Jie,
Tewary Kanupriya,
Zhang Tong
Publication year - 1996
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/(sici)1097-024x(199602)26:2<165::aid-spe5>3.0.co;2-k
Subject(s) - computer science , measure (data warehouse) , test data , test (biology) , data mining , data flow diagram , mutation , keyword driven testing , algorithm , reliability engineering , software , engineering , programming language , software system , paleontology , software construction , biochemistry , chemistry , database , gene , biology
Two experimental comparisons of data flow and mutation testing are presented. These techniques are widely considered to be effective for unit‐level software testing, but can only be analytically compared to a limited extent. We compare the techniques by evaluating the effectiveness of test data developed for each. We develop ten independent sets of test data for a number of programs: five to satisfy the mutation criterion and five to satisfy the all‐uses data‐flow criterion. These test sets are developed using automated tools, in a manner consistent with the way a test engineer might be expected to generate test data in practice. We use these test sets in two separate experiments. First we measure the effectiveness of the test data that was developed for one technique in terms of the other. Second, we investigate the ability of the test sets to find faults. We place a number of faults into each of our subject programs, and measure the number of faults that are detected by the test sets. Our results indicate that while both techniques are effective, mutation‐adequate test sets are closer to satisfying the data flow criterion, and detect more faults.