Abstract
Risk analysis and management is a critical aspect of the software development process. Various risks are associated with every phase of the software development lifecycle. The early identification of risks in each phase of software development coupled with mitigating plans can help to reduce the cost of the product and increase software quality. This study aims to explore various tools and techniques used in the literature of analyzing and managing risks. Most risk analysis techniques have been applied in the requirement analysis phase, so there is a scarcity of tools supporting automated risk analysis. Accommodating various types of risk factors to predict the software risks reduces the accuracy of the classifier.
Highlights
A software risk is a threat that could have a negative effect on software quality, delay the project or exceed the budget
The purpose of this study is to find the merits and the demerits of the different classification techniques that have been applied for defect prediction
Software fault/risk prediction plays an important role in analyzing software quality and balancing software development cost
Summary
A software risk is a threat that could have a negative effect on software quality, delay the project or exceed the budget. It is necessary to identify various types of risk to a software project during the early phases of Software Development Life Cycle (SDLC). Software quality can be ensured by forecasting the risks that an existing software project can face. For this purpose, we use defect prevention, defect reduction, and defect containment. A critical review showing the usefulness of the various classification techniques on different types of software project datasets has been conducted in this report. Developing an efficient software fault/risk prediction method is a highly demanding challenge. Software fault/risk prediction plays an important role in analyzing software quality and balancing software development cost. And accurate prediction of software risk has become a critical issue to project success. As the demand for good software quality is increasing, increased software size and complexity may lead to increasing software faults which require identification at the early stages of SDLC
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