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There are three interdependent factors that drive our software development processes: interval, quality and cost. As market Dressures continue to demand new features ever more months. network, consisting of circuit packs, ASICs, software units, and a craft terminal. Total head count for this release was 180 people and the development project lasted for 19 We conclude with lessons learned from the case study and resulting ongoing improvement activities.

A plethora of defect prediction models has been proposed and empirically evaluated, often using standard classification performance measures.In this paper, we explore defect prediction models for a large, multi-release software system from the telecommunications domain. A history of roughly 3 years is analyzed to extract process and static code metrics that are used to build several defect prediction models with Random Forests.The performance of the resulting models is comparable to previously published work. Furthermore, we develop a new evaluation measure based on the comparison to an optimal model.

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