Investigation of logistic regression as a discriminant of software quality

We investigated the possibility that Logistic Regression Functions (LRFs) w hen used in combination with Boolean Discriminant functions (BDFs), which w e had previously developed, would improve the quality classification abilit y of BDFs when used alone. This was the case; when the union of a BDF and L RF was used to classify quality, the predicative accuracy of quality and in spection cost was improved over that of using either function alone for the Space Shuttle. Also, the LRFs proved useful for ranking the quality of mod ules in a build. The significance of these results is that very high qualit y classification accuracy (1.25% error) can be obtained while reducing the inspection cost incurred in achieving high quality. This is particularly im portant for safety critical systems. Because the methods are general and no t particular to the Shuttle, they could be applied to other domains. A key parr of the LRF development was a method for identifying the critical value (i.e. threshold) that could discriminate between high and low quality and at the same rime constrain the cost of inspection to a reasonable value.