A Regression Analysis Based Model for DEffect Learning and Prediction in Software Development | [Mehran University Research Journal of Engineering and Technology • 2021]

Author(s):

1. Mashooque Ahmed Memon: Department of Computer Science, Benazir Bhutto Shaheed University,Layari, Karachi, Sindh,Pakistan

2. Mujeeb-ur-Rehman Maree Baloch: Institute of Mathematics and Computer Science, University of Sindh,Jamshoro, Sindh,Pakistan

3. Muniba Memon: Department of Information Technology, Quaid-e-Awam University of Engineering,Science and Technology,Nawabshah, Sindh, Pakistan.

4. Syed Hyder Abbas Musavi: Department of Electronic Engineering, Mehran University of Engineering and Technology, Shaheed Zulfiqar Ali Bhutto Campus, Khairpur Mir’s, Sindh, Pakistan.

Abstract:

The development of software undergoes multiple regression phases to deliver quality software. Therefore, to minimize the development effort, time and cost it is very important to understand the probable defects associated with the designed modules. It is possible that occurrence of a range of defects may impact the designed modules which need to be predicted in advance to have a close inter-association with the depended modules. Most of the existing defect prediction classifier mechanisms are derived from the past project data learning, but it is not sufficient for new project defect predicting as the new design may have a different kind of parameters and constraints. This paper recommends Regression Analysis (RA) based defect learning and prediction Defect Prediction (RA-DP) mechanism to support the defective or non-defective prediction for quality software development. The RA-DP approach provides two methods to perform this prediction analysis. It initially presents an association learning through RA to construct the regression rules from the learned knowledge required for the defect prediction. The constructed regression rules are used for defect prediction and analysis. To measure the performance of the RA-DP a regression experimental evaluation is performed over the defect-prone PROMISE dataset from NASA project. The outcome of the results is analyzed through measuring the prediction Accuracy, Sensitivity and Specificity to demonstrate the improvisation and effectiveness of the proposal in comparison to a few existing classifiers.

Page(s): 617-629

DOI: DOI not available

Published: Journal: Mehran University Research Journal of Engineering and Technology, Volume: 40, Issue: 3, Year: 2021

Keywords:

regression analysis , Association Learning , Software Defect Prediction

References:

References are not available for this document.

Citations

Citations are not available for this document.

Citations

Downloads

Views