Comparing methods to identify defect reports in a change management database

ABSTRACT

A key problem when doing automated fault analysis and fault prediction from information in a software change management database is how to determine which change reports represent software faults. In some change management systems, there is no simple way to distinguish fault reports from changes made to add new functionality or perform routine maintenance. This paper describes a comparison of two methods for classifying change reports for a large software system, and concludes that, for that particular system, the stage of development when the report was initialized is a more accurate indicator of its fault status than the presence of certain keywords in the report's natural language description.

REFERENCES

• 1: Robert M. Bell , Thomas J. Ostrand , Elaine J. Weyuker, Looking for bugs in all the right places, Proceedings of the 2006 international symposium on Software testing and analysis, July 17-20, 2006, Portland, Maine, USA [doi>10.1145/1146238.1146246]
• 2: Thomas J. Ostrand , Elaine J. Weyuker , Robert M. Bell, Predicting the Location and Number of Faults in Large Software Systems, IEEE Transactions on Software Engineering, v.31 n.4, p.340-355, April 2005 [doi>10.1109/TSE.2005.49]
• 3: Thomas J. Ostrand , Elaine J. Weyuker , Robert M. Bell, Automating algorithms for the identification of fault-prone files, Proceedings of the 2007 international symposium on Software testing and analysis, July 09-12, 2007, London, United Kingdom [doi>10.1145/1273463.1273493]
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INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.5 Testing and Debugging

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.8 Metrics

  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

General Terms:
Design, Experimentation, Management, Measurement, Performance, Reliability