Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
A statistical method for controlling software defect detection process
Computers and Industrial Engineering
Defect-Causal Analysis Drives Down Error Rates
IEEE Software
Developing techniques for using software documents: a series of empirical studies
Developing techniques for using software documents: a series of empirical studies
Experiences with defect prevention
IBM Systems Journal
A Computational Framework for Supporting Software Inspections
Proceedings of the 19th IEEE international conference on Automated software engineering
Towards a Defect Prevention Based Process Improvement Approach
SEAA '08 Proceedings of the 2008 34th Euromicro Conference Software Engineering and Advanced Applications
Design and code inspections to reduce errors in program development
IBM Systems Journal
Applying DPPI: a defect causal analysis approach using bayesian networks
PROFES'10 Proceedings of the 11th international conference on Product-Focused Software Process Improvement
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Defect causal analysis (DCA) has shown itself an efficient means to obtain product-focused software process improvement. A DCA approach, called DPPI, was assembled based on guidance acquired through systematic reviews and feedback from experts in the field. To our knowledge, DPPI represents an innovative approach integrating cause-effect learning mechanisms (Bayesian networks) into DCA meetings, by using probabilistic cause-effect diagrams. The experience of applying DPPI to a real Web-based software project showed its feasibility and provided insights into the requirements for tool support. Moreover, it was possible to observe that DPPI's Bayesian diagnostic inference predicted the main defect causes efficiently, motivating further investigation. This paper describes (i) the framework built to support the application of DPPI and automate the generation of the probabilistic cause-effect diagrams, and (ii) the results of an experimental study aiming at investigating the benefits of using DPPI's probabilistic cause-effect diagrams during DCA meetings.