A decision tree logic based recommendation system to select software fault prediction techniques

In this world of emerging applications of software, it is always important to provide a quality assured product to customers. Software Fault Prediction popularly abbreviated as SFP is a major field which helps to provide quality assured products to customers. It helps to recognize modules that are bugfree and bug-prone in a software module. Machine learning techniques for both classification and determination are used for the purpose of software fault prediction. Software Fault Prediction is carried out prior to testing process without executing the source code, instead vital characteristics of software is taken into consideration. This early identification of faults can help software engineers to reduce the risk of system failure. A company does not always prefer to invest more expense on testing and in those situations, software fault prediction can have an upper hand in testing. The software fault prediction model will first train the learning techniques to generate base learners and then apply these base learners to unseen projects. It is always preferred to determine the count of faults rather than classifying each software module as fault-free and fault-prone. All software fault prediction techniques depend on base learners used and also nature of fault dataset. In this paper, the major learning techniques to determine software fault, characteristics of software fault dataset, etc. are discussed.

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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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