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Automating and Evaluating Probabilistic Cause-Effect Diagrams to Improve Defect Causal Analysis

  • Marcos Kalinowski
  • Emilia Mendes
  • Guilherme H. Travassos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6759)

Abstract

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.

Keywords

Bayesian Networks Defect Causal Analysis Defect Prevention Defect Prevention-based Process Improvement DPPI Probabilistic Cause-Effect Diagrams Product Focused Process Improvement 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marcos Kalinowski
    • 1
    • 2
  • Emilia Mendes
    • 1
    • 3
  • Guilherme H. Travassos
    • 1
  1. 1.COPPE/UFRJ – Federal University of Rio de JaneiroRio de JaneiroBrazil
  2. 2.UVA-RJ – Veiga de Almeida UniversityRio de JaneiroBrazil
  3. 3.Computer Science DepartmentThe University of AucklandAucklandNew Zealand

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