Developing Conceptual Framework of Software Defect Prediction in Software Testing: The Case of Ethiopian Software Industries

Abstract

Purpose: Software defect prediction is one of the most active research areas in software engineering. As our dependency on software is increasing, software quality is becoming gradually more and more important in present era. Software used almost everywhere and in every tread of life. Software consequences such as fault and failures may diminish the quality of software which leads to customer dissatisfaction. Different points that are related to the current work had been discussed.
 Methodology: This research contains a detailed explanation of the scientific methods and methodologies used for the design of proposed framework. Primary and secondary source of data has been used.
 Findings: Due to the tremendous amount of data generated daily from fields such as business that software has resulted in the generation of tremendous amount of defected data. As the organizations struggle to handle and utilize effectively all the information available in order to provide better products and services and gain competitive advantage, defect separation and the so called “big data” analytics are two fields that currently constitute matter of concern and discussion.
 Unique Contribution to Theory, Practice and Policy: There are different research areas in defect prediction using big data analytics defect segmentations one of the issues. Defect Segmentation is an important component of much organization, and the algorithm designed in this work is expected to be a significant contribution to the field, and mainly to researchers working on various aspects of defect prediction defect segmentation using big data analytics. Therefore, the researchers in the area can use the algorithm or the implemented system for processing segmentation as component in their research, mainly on machine learning applications.