Earticle

초록

영어

Software plays a significant role in technological and economic development due to its utmost importance in day to day activities. A sequence of rigorous activities under certain constraints is followed to come up with reliable software. Various measures are taken during the process of software development to ensure high quality software. One such method is software module fault prediction for quality assurance to discover defects in the software prior to testing. It aids in predicting the software module faults earlier in the development of the software which predicts fault prone modules so that these can be given special attention to avoid any future risk which eventually curbs the testing along with maintenance cost and effort. The literature survey uncovers many findings that had never been focused like dimensionality reduction and feature selection based on individual feature importance which leads to increase in time complexity and chances of false information. This paper addresses these issues and proposes a supervised machine learning based software module fault prediction technique by implementing Convolutional Neural Network (CNN) as classifier model. Feature selection methods used are InfoGain and Correlation. The results obtained are compared with the existing method HySOM (SOM Clustering with Artificial Neural Network Classification) by considering three different feature sets (Fifteen features, Eighteen features and Twenty one features) of PC1 dataset from NASA. The comparative analysis is performed on the basis of accuracy, precision, recall and F1-measure. The results clearly show better performance of the proposed CNN based technique than HySOM. This paper will contribute towards improvement of quality assurance models utilized for software fault prediction by automating this process using machine learning which enhances True Positive Rate and reduces the detection error. This in turn will help project managers, testers and developers to locate and keep track of fault prone modules so that final software is more accurate, consistent and reliable without consuming much of the testing and maintenance resources.

목차

Abstract
 1. Introduction
 2. Literature Survey
 3. Proposed Methodology
 4. Results and Analysis
 5. Conclusion
 References

키워드

저자

• Rupali Sharma [ Department of Computer Science and Engineering, DAV Institute of Engineering and Technology, India ] corresponding Author
• Parveen Kakkar [ Department of Computer Science and Engineering, DAV Institute of Engineering and Technology, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605