Hidden Markov Model Based Automated Fault Localization for Integration Testing

Abstract : 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.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01402565
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Thursday, November 24, 2016 - 6:58:23 PM
Last modification on : Thursday, October 24, 2019 - 2:44:11 PM
Long-term archiving on : Monday, March 20, 2017 - 9:26:09 PM

File

ge_15151.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01402565, version 1
  • OATAO : 15151

Citation

Ning Ge, Shin Nakajima, Marc Pantel. Hidden Markov Model Based Automated Fault Localization for Integration Testing. 4th International Conference on Software Engineering and Service Science (ICSESS 2013), May 2013, Beijing, China. pp. 1-4. ⟨hal-01402565⟩

Share

Metrics

Record views

174

Files downloads

350