One of the main reasons behind unfruitful software development projects is that it is often too late to correct the problems by the time they are detected. It clearly indicates the need for early warning about the potential risks. In this paper, we discuss an intelligent software early 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, and poor quality in software development and maintenance from multiple perspectives. It handles incomplete, inaccurate, and imprecise information, and resolve conflicts in an uncertain environment in its software risk assessment using fuzzy linguistic variables, fuzzy sets, and fuzzy 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.
X. F. Liu et al., "An Intelligent Early Warning System for Software Quality Improvement and Project Management," Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, 2003, Institute of Electrical and Electronics Engineers (IEEE), Jan 2003.
The definitive version is available at https://doi.org/10.1109/TAI.2003.1250167
Keywords and Phrases
Fuzzy Inference; Fuzzy Linguistic Variables; Fuzzy Logic; Fuzzy Sets; Intelligent Software Early Warning System; Intelligent Warning System; Project Management; Risk Defuzzification; Risk Management; Risk Variables; Software Development; Software Maintenance; Software Management; Software Metrics; Software Process Improvement; Software Quality; Software Quality Improvement; Solid Software Model
International Standard Serial Number (ISSN)
Article - Conference proceedings
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