Analysis and Prediction of Mandelbugs in an Industrial Software System

Abstract

Mandelbugs are faults that are triggered by complex conditions, such as interaction with hardware and other software, and timing or ordering of events. These faults are considerably difficult to detect with traditional testing techniques, since it can be challenging to control their complex triggering conditions in a testing environment. Therefore, it is necessary to adopt specific verification and/or fault-tolerance strategies for dealing with them in a cost-effective way. In this paper, we investigate how to predict the location of Mandelbugs in complex software systems, in order to focus V&V activities and fault tolerance mechanisms in those modules where Mandelbugs are most likely present. In the context of an industrial complex software system, we empirically analyze Mandelbugs, and investigate an approach for Mandelbug prediction based on a set of novel software complexity metrics. Results show that Mandelbugs account for a noticeable share of faults, and that the proposed approach can predict Mandelbug-prone modules with greater accuracy than the sole adoption of traditional software metrics.