Issue No. 05 - May (1992 vol. 18)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/32.135775
<p>The use of the statistical technique of discriminant analysis as a tool for the detection of fault-prone programs is explored. A principal-components procedure was employed to reduce simple multicollinear complexity metrics to uncorrelated measures on orthogonal complexity domains. These uncorrelated measures were then used to classify programs into alternate groups, depending on the metric values of the program. The criterion variable for group determination was a quality measure of faults or changes made to the programs. The discriminant analysis was conducted on two distinct data sets from large commercial systems. The basic discriminant model was constructed from deliberately biased data to magnify differences in metric values between the discriminant groups. The technique was successful in classifying programs with a relatively low error rate. While the use of linear regression models has produced models of limited value, this procedure shows great promise for use in the detection of program modules with potential for faults.</p>
statistical technique; discriminant analysis; fault-prone programs; principal-components procedure; simple multicollinear complexity metrics; uncorrelated measures; orthogonal complexity domains; group determination; quality measure; large commercial systems; deliberately biased data; metric values; relatively low error rate; linear regression models; program modules; computational complexity; program testing; quality control; software metrics; software reliability
J. Munson and T. Khoshgoftaar, "The Detection of Fault-Prone Programs," in IEEE Transactions on Software Engineering, vol. 18, no. , pp. 423-433, 1992.