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 to 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 of with high potential for faults.

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This study presents a methodology that will produce a viable fault surrogate. The focus of the effort is on the precise measurement of software development process and product outcomes. Tools and processes for the static measurement of the source code have been installed and made operational in a large embedded software system. Source code measurements have been gathered unobtrusively for each build in the software evolution process. The measurements are synthesized to obtain the fault surrogate. The complexity of sequential builds is compared and a new measure, code churn, is calculated. This paper will demonstrate the effectiveness of code complexity churn by validating it against the testing problem reports.

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