Experiences with criticality predictions in software development

Abstract

Cost-effective software project management has the serious need to focus resources on those areas with highest criticality. The paper focuses on two areas important for practical application of criticality-based predictions in real projects, namely the selection of a classification technique and the use of the results in directing management decisions. The first part is comprehensively comparing and evaluating five common classification techniques (Pareto classification, classification trees, factor-based discriminant analysis, fuzzy classification, neural networks) for identifying critical components. Results from a current large-scale switching project are included to show practical benefits. Knowing which technique should be applied the second area gains even more attention: What are the impacts for practical project management within given resource and time constraints? Several selection criteria based on the results of a combined criticality and history analysis are provided together with potential decisions.