Fuzzy logic for software metric models throughout the development life-cycle

Gray, AR
MacDonell, SG
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Conference Contribution
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IEEE Computer Society Press

One problem faced by managers who are using project management models is the elicitation of numerical inputs. Obtaining these with any degree of confidence early in a project is not always feasible. Related to this difficulty is the risk of precisely specified outputs from models leading to overcommitment. These problems can be seen as the collective failure of software measurements to represent the inherent uncertainties in managers' knowledge of the development products, resources, and processes. It is proposed that fuzzy logic techniques can help to overcome some of these difficulties by representing the imprecision in inputs and outputs, as well as providing a more expert-knowledge based approach to model building. The use of fuzzy logic for project management however should not be the same throughout the development life cycle. Different levels of available information and desired precision suggest that it can be used differently depending on the current phase, although a single model can be used for consistency

Accuracy , Fuzzy logic , Information management , Information science , Knowledge management , Machine learning , Management information systems , Project management , Resource management , Software metrics
Proceedings of the 18th international conference annual meeting of the North American Fuzzy Information Processing Society (NAFIPS'99), pp.258-262
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