Software errors and complexity: an empirical investigation0
Communications of the ACM
Software engineering standards
Software engineering standards
Analyzing Error-Prone System Structure
IEEE Transactions on Software Engineering
Orthogonal Defect Classification-A Concept for In-Process Measurements
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Using the GQM paradigm to investigate influential factors for software process improvement
Journal of Systems and Software
Building Knowledge through Families of Experiments
IEEE Transactions on Software Engineering
Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
Evaluation of a software requirements document by analysis of change data
ICSE '81 Proceedings of the 5th international conference on Software engineering
Lecture notes on empirical software engineering
Lecture notes on empirical software engineering
IEEE Transactions on Software Engineering
Characteristics of multiple-component defects and architectural hotspots: a large system case study
Empirical Software Engineering
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Answering “macro-process” research issues – which require understanding how development processes fit or do not fit in different organizational systems and environments – requires families of related studies. While there are many sources of variation between development contexts, it is not clear a priori what specific variables influence the effectiveness of a process in a given context. These variables can only be discovered opportunistically, by comparing process effects from different environments and analyzing points of difference. In this paper, we illustrate this approach and the conclusions that can be drawn by presenting a family of studies on the subject of software defects and their behaviors – a key phenomenon for understanding macro-process issues. Specifically, we identify common “folklore,” i.e. widely accepted heuristics concerning how defects behave, and then build up a body of knowledge from empirical studies to refine the heuristics with information concerning the conditions under which they do and do not hold.