Software Fault Localisation via Probabilistic Modelling

Probabilistic modelling of the size effect on ductility of a C-Mn steel

Journal de Physique IV (Proceedings) ◽

10.1051/jp4:1998808 ◽

1998 ◽

Vol 08 (PR8) ◽

pp. Pr8-63-Pr8-70

Author(s):

S. Carassou ◽

M. Soilleux ◽

B. Marini

Keyword(s):

Size Effect ◽

Probabilistic Modelling ◽

Mn Steel

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Modeling software fault detection and fault correction processes with incomplete test data

Industrial Electronics and Engineering ◽

10.2495/iciee140461 ◽

2014 ◽

Author(s):

Yu Liu ◽

Duo Li ◽

Chao Guo

Keyword(s):

Fault Detection ◽

Test Data ◽

Modeling Software ◽

Software Fault

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The Effectiveness of the Fused Weighted Filter Feature Selection Method to Improve Software Fault Prediction

Journal of Communications Technology Electronics and Computer Science ◽

10.22385/jctecs.v8i0.96 ◽

2016 ◽

Vol 8 ◽

pp. 5 ◽

Cited By ~ 1

Author(s):

Fatemeh Alighardashi ◽

Mohammad Ali Zare Chahooki

Keyword(s):

Feature Selection ◽

Feature Selection Method ◽

Selection Method ◽

Machine Learning Algorithms ◽

Fault Prediction ◽

Filter Method ◽

Selection Methods ◽

Software Projects ◽

Software Fault Prediction ◽

Software Fault

Improving the software product quality before releasing by periodic tests is one of the most expensive activities in software projects. Due to limited resources to modules test in software projects, it is important to identify fault-prone modules and use the test sources for fault prediction in these modules. Software fault predictors based on machine learning algorithms, are effective tools for identifying fault-prone modules. Extensive studies are being done in this field to find the connection between features of software modules, and their fault-prone. Some of features in predictive algorithms are ineffective and reduce the accuracy of prediction process. So, feature selection methods to increase performance of prediction models in fault-prone modules are widely used. In this study, we proposed a feature selection method for effective selection of features, by using combination of filter feature selection methods. In the proposed filter method, the combination of several filter feature selection methods presented as fused weighed filter method. Then, the proposed method caused convergence rate of feature selection as well as the accuracy improvement. The obtained results on NASA and PROMISE with ten datasets, indicates the effectiveness of proposed method in improvement of accuracy and convergence of software fault prediction.

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