Early bug detection in deployed software using support vector machine

Abstract

Software crashes may be disastrous and cause great economical damages. Therefore, the reliability and safety of software products in some circumstances may be very vital and critical. In this paper a new mechanism to detect errors and prevent software crashes at run time, is presented. The novelty of the proposed technique is the use of Support Vector Machine (SVM) method to accelerate the detection of bugs early before they cause program crashes. By applying the SVM method, two thoroughly distinguishable patterns of failing and passing execution of the program are constructed in a relatively short amount of time, before the program is actually deployed. The vectors are constructed from the decision making expressions or in other words predicates, appearing within the program text. These patterns are further applied, after the program deployment, to estimate the probability of program failure symptoms, early before the program crashes. Our experiments with bug prediction in Siemens software, demonstrate the ability of our proposed technique to predict errors before they can cause any damages. © 2008 Springer-Verlag.