Int. J. of Business Intelligence and Data Mining   »   2018 Vol.13, No.1/2/3

 

 

Title: An empirical approach for complexity reduction and fault prediction for software quality attribute

 

Authors: Rajkumar; Viji; S. Duraisamy

 

Addresses:
Department of Computer Science and Engineering, SVS College of Engineering, Coimbatore, Tamil Nadu, India
Department of Computer Science and Engineering, SVS College of Engineering, Coimbatore, Tamil Nadu, India
Department of Computer Applications, Sri Krishna College of Engineering and Technology, Coimbatore, India

 

Abstract: Designing the high-quality software is a difficult one due to the high complexity and fault prone class. To reduce the complexity and predict the fault-prone class in the object orient software design proposed a new empirical approach. This proposed approach concentrates more on to increase the software quality in the object oriented programming structures. This technique will collect the dataset and metric values from CK-based metrics. And then complexity will be calculated based on the weighted approach. The fault prediction will be done, based on the low usage of the dataset and high complexity dataset. This helps to increase the software quality. In simulation section, the proposed approach has performed and analysed the parameters such as accuracy, fairness, recall, prediction rate and efficiency. The experimental results have shown that the proposed approach increases the prediction rate, accuracy and efficiency.

 

Keywords: complexity reduction; fault prediction; software design; software quality; CK-based metrics.

 

DOI: 10.1504/IJBIDM.2017.10004682

 

Int. J. of Business Intelligence and Data Mining, 2018 Vol.13, No.1/2/3, pp.177 - 187

 

Available online: 03 Nov 2017

 

 

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