Machine Learning and Applications, Fourth International Conference on (2011)
Honolulu, Hawaii USA
Dec. 18, 2011 to Dec. 21, 2011
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICMLA.2011.69
Multi-threaded applications are commonplace in today's software landscape. Pushing the boundaries of concurrency and parallelism, programmers are maximizing performance demanded by stakeholders. However, multi-threaded programs are challenging to test and debug. Prone to their own set of unique faults, such as race conditions, testers need to turn to automated validation tools for assistance. This paper's main contribution is a new algorithm called multi-stage novelty filtering (MSNF) that can aid in the discovery of software faults. MSNF stresses minimal configuration, no domain specific data preprocessing or software metrics. The MSNF approach is based on a multi-layered support vector machine scheme. After experimentation with the MSNF algorithm, we observed promising results in terms of precision. However, MSNF relies on multiple iterations (i.e., stages). Here, we propose four different strategies for estimating the number of the requested stages.
software testing, back-box testing, fault detection, machine learning, unsupervised support vector machines
Dragan Gaševic, John Cuzzola, Ebrahim Bagheri, "Fault Detection through Sequential Filtering of Novelty Patterns", Machine Learning and Applications, Fourth International Conference on, vol. 01, no. , pp. 217-222, 2011, doi:10.1109/ICMLA.2011.69