Software system

Abstract: From the Publisher: Author Biography: Dr. Stephen H. Kan, an ASQC Certified Quality Engineer, is responsible for IBM Rochester's software quality strategy and plans, quality assessment, software measurements, and statistical analysis. He has been the software quality focal point for the software system of the AS/400 computer since its initial release in 1988.

Abstract: Many organizations want to predict the number of defects (faults) in software systems, before they are deployed, to gauge the likely delivered quality and maintenance effort. To help in this numerous software metrics and statistical models have been developed, with a correspondingly large literature. We provide a critical review of this literature and the state-of-the-art. Most of the wide range of prediction models use size and complexity metrics to predict defects. Others are based on testing data, the "quality" of the development process, or take a multivariate approach. The authors of the models have often made heroic contributions to a subject otherwise bereft of empirical studies. However, there are a number of serious theoretical and practical problems in many studies. The models are weak because of their inability to cope with the, as yet, unknown relationship between defects and failures. There are fundamental statistical and data quality problems that undermine model validity. More significantly many prediction models tend to model only part of the underlying problem and seriously misspecify it. To illustrate these points the Goldilock's Conjecture, that there is an optimum module size, is used to show the considerable problems inherent in current defect prediction approaches. Careful and considered analysis of past and new results shows that the conjecture lacks support and that some models are misleading. We recommend holistic models for software defect prediction, using Bayesian belief networks, as alternative approaches to the single-issue models used at present. We also argue for research into a theory of "software decomposition" in order to test hypotheses about defect introduction and help construct a better science of software engineering.

Abstract: Self-adaptive software requires high dependability robustness, adaptability, and availability. The article describes an infrastructure supporting two simultaneous processes in self-adaptive software: system evolution, the consistent application of change over time, and system adaptation, the cycle of detecting changing circumstances and planning and deploying responsive modifications.

Abstract: Linear MW-only ldquodcrdquo network power flow models are in widespread and even increasing use, particularly in congestion-constrained market applications. Many versions of these approximate models are possible. When their MW flows are reasonably correct (and this is by no means assured), they can often offer compelling advantages. Given their considerable importance in today's electric power industry, dc models merit closer scrutiny. This paper attempts such a re-examination.