Statistical software debugging: From bug predictors to the main causes of failure

Abstract

Detecting latent errors is a key challenging issue in the software testing process. Latent errors could be best detected by bug predictors. A bug predictor manifests the effect of a bug on the program execution state. The aim has been to find the smallest reasonable subset of the bug predictors, manifesting all possible bugs within a program. In this paper, a new algorithm for finding the smallest subset of bug predictors is presented. The algorithm, firstly, applies a LASSO method to detect program predicates which have relatively higher effect on the termination status of the program. Then, a ridge regression method is applied to select a subset of the detected predicates as independent representatives of all the program predicates. Program control and data dependency graphs can be best applied to find the causes of bugs represented by the selected bug predictors. Our proposed approach has been evaluated on two well-known test suites. The experimental results demonstrate the effectiveness and accuracy of the proposed approach.