Hidden Markov model based automated fault localization for integration testing

Integration testing is an expensive activity in software testing, especially for fault localization in complex systems. Model-based diagnosis (MBD) provides various benefits in terms of scalability and robustness. In this work, we propose a novel MBD approach for the automated fault localization in integration testing. Our method is based on Hidden Markov Model (HMM) which is an abstraction of system´s component to simulate component´s behaviour. The core of this method is a fault localization algorithm that gives out the set of suspect faulty components and a backward algorithm that calculates the matching degree between the HMM and the real system to evaluate the confidence degree of the localization conclusion. The proposed method is evaluated on a specific test bed and is applied to a simple helicopter control system case study.