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Published January 1, 2010 | Version v1
Journal article Open

Defect prediction from static code features: current results, limitations, new approaches

Creators

  • 1. W Virginia Univ, Morgantown, WV 26506 USA
  • 2. Univ Oulu, Oulu, Finland
  • 3. Bogazici Univ, Istandbul, Turkey

Description

Building quality software is expensive and software quality assurance (QA) budgets are limited. Data miners can learn defect predictors from static code features which can be used to control QA resources; e.g. to focus on the parts of the code predicted to be more defective.

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