DEVELOPING INTERPRETABLE MODELS WITH OPTIMIZED SET REDUCTION FOR IDENTIFYING HIGH-RISK SOFTWARE COMPONENTS

Applying equal testing and verification effort to all parts of a softw are system is not very efficient, especially when resources are limite d and scheduling is tight. Therefore, one needs to be able to differen tiate low/high fault frequency components so that testing/verification effort can be concentrated where needed. Such a strategy is expected to detect more faults and thus improve the resulting reliability of th e overall system. This paper presents the Optimized Set Reduction appr oach for constructing such models, intended to fulfill specific softwa re-engineering needs. Our approach to classification is to measure the software system and build multivariate stochastic models for predicti ng high-risk system components. We present experimental results obtain ed by classifying Ada components into two classes: is or is not likely to generate faults during system and acceptance test. Also, we evalua te the accuracy of the model and the insights it provides into the err or-making process.