Performance-Aware Approach for Software Risk Management Using Random Forest Algorithm

• Published in: International journal of software innovation Vol. 9; no. 1; pp. 12 - 19
• Main Authors: Aggarwal, Alankrita, Dhindsa, Kanwalvir Singh, Suri, P K
• Format: Journal Article
• Language: English
• Published: Mount Pleasant IGI Global 01.01.2021
• Subjects: Algorithms; Decision trees; Prediction models; Quality assurance; Quality control; Risk management; Soft computing; Software packages; Software quality; Source code
• ISSN: 2166-7160; 2166-7179
• DOI: 10.4018/IJSI.2021010102

Software quality assurance and related methodologies are quite prominent before actual launching the application so that any type of issues can be resolved at prior notifications. The process of software evaluation is one of the key tasks that are addressed by the quality assurance teams so that the risks in the software suite can be identified and can be removed with prior notifications. Different types of metrics can be used in defect prediction model and widely used metrics are source code and process metrics. The focus of this research manuscript is to develop a narrative architecture and design for software risk management using soft computing in integration with the proposed approach of random forest approach is expected to have the effectual results on multiple parameters with the flavor of multiple decision trees. The proposed approach is integrated with the framework of meta-heuristics with random forest in different substances and elements to produce a new substance.