An intelligent early warning system for software quality improvement and project management

There are three major problems with software projects: over budget, behind schedule, and poor quality. It is often too late to correct these problems by the time they are detected in failed software projects. In this paper, we discuss design, implementation, and evaluation of an experimental intelligent software early warning system based on fuzzy logic using an integrated set of software metrics. It consists of the following components: software metrics database, risk knowledge base, intelligent risk assessment, and risk tracing. It helps to assess risks associated with the three problems from perspectives of product, process, and organization in the early phases of the software development process. It is capable of aggregating various partial risk assessment results into an overall risk indicator even if they may be conflicting. In addition, it can be used to analyze a risk by identifying its root causes through its risk tracing utility.