An Intelligent Early Warning System for Software Quality Improvement and Project Management


One of the main reasons behind unfruitful software development projects is that it is often too late to correctthe problems by the time they are detected. It clearlyindicates the need for early warning about the potentialrisks. In this paper, we discuss an intelligent softwareearly warning system based on fuzzy logic using an integrated set of software metrics. It helps to assess risks associated with being behind schedule, over budget, andpoor quality in software development and maintenancefrom multiple perspectives. It handles incomplete,inaccurate, and imprecise information, and resolveconflicts in an uncertain environment in its software riskassessment using fuzzy linguistic variables, fuzzy sets, andfuzzy inference rules. Process, product, and organizational metrics are collected or computed based on solid software models. The intelligent risk assessment process consists of the following steps: fuzzification of software metrics, rule firing, derivation and aggregation of resulted risk fuzzy sets, and defuzzification of linguistic risk variables.


Computer Science

Keywords and Phrases

Intelligent Early Warning System; Fuzzification; Fuzzy Logic; Project Management; Software Development; Software Quality Improvement

Document Type

Article - Journal

Document Version


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