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.

about the past and future might say, "In an era of rapid change, those who repeat the past are condemned to a bleak future. " (Think about the dinosaurs, and think carefully about software engineering maturity models that emphasize repeatability.) This paper also tries to identify some

that are tightly integrated into their approach to scientific research. Their software practices share many features of both agile and open source projects, in that they rely on self-organisation of the teams, extensive use of informal communication channels, and developers who are also users and domain experts

in outcomes between siblings who did and did not attend the program. We will explore evidence that spillovers are important below. We begin with the probability that a child completed high school. About three-quarters of the sample of respondents completed high school. The first column is based on OLS

to purchase a new tool – skills for change management become important. Unfortunately, too few of the soft-ware-intensive organizations are proactive about managing change. A recent article by Boehm and Basili shows that poor soft-ware engineering practices are still a major contributor to software defects [1

Algebraic algorithms deal with numbers, vectors, matrices, polynomials, for-mal power series, exponential and differential polynomials, rational functions, algebraic sets, curves and surfaces. In this vast area, manipulation with matri-ces and polynomials is fundamental for modern computations in Sciences and

is connected to everything else, but this is not a productive insight. A more practical approach would, instead, read carefully through the determinants of adaptive capacity to recognize that the local manifestations of macro-scale determinants of adaptive capacity are their most critical characteristics

. This trend dramatically increased the demands on software engineers. Programs and systems became complex and almost impossible to fully understand. The sinking cost and the abundance of computing resources inevitably reduced the care for good design. Quality seemed extravagant, a loser in the race for profit

to enable the submis-sion of higher-quality reports, and presented techniques to sort, categorize and leverage issues for software engineering needs. Who, however, cares about filing issues? What kind of issues are reported in issue trackers? What kind of correlation exist between issue reporting

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