Learning from Our Mistakes with Defect Causal Analysis

  • Authors:

  • David N. Card

  • Affiliations:

  • -

  • Venue:
  • IEEE Software
  • Year:
  • 1998

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Abstract

Defect causal analysis offers a simple, low-cost method for systematically improving the quality of software produced by a team, project, or organization. DCA takes advantage of one of the most widely available types of quality information-the software problem report. This information drives a team-based technique for defect causal analysis. The analysis leads to process changes that help prevent defects and ensure their early detection. Although some approaches to quality improvement involve exhaustive defect classification schemes or complex mathematical models, the approach I present relies on basic techniques that can be implemented readily by the typical software organization. The DCA process was developed at IBM 1 ; I adapted it for Computer Sciences Corporation 2 and other organizations. Three key principles drive the DCA approach. ý Reduce defects to improve quality. Although there are many different ideas about what quality is or which "-ility" is more important-reliability, portability, or what-ever- we can all probably agree that a product with many defects lacks quality, however you define it. Thus, we can improve software quality by focusing on the prevention and early detection of defects, a readily measurable software attribute.