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Bibliographic Item (1.0)

ZimmermannWeibgerberDiehlZeller05
  1. Thomas Zimmermann & Peter Weibgerber & Stephan Diehl & Andreas Zeller
  2. Mining Version Histories to Guide Software Changes
  3. IEEE Trans Software Engineering V31n6(Jun 2005)pp429-526
  4. =EMPIRICAL EVOLUTION OPEN SOURCE TOOL ROSE CVS ECLIPSE GCC GIMP JBOSS JEDIT KO FFICE POSTGRES PYTHON
  5. ROSE::Eclipse_plugin= See http://www.st.cs.uni-sb.de/softevo/
  6. By analyzing data collected from version histories can predict what else is likely to be changed if one location is changed. Top 3 predictions cover correct predictions 79% of the time.
  7. Detects undocumented couplings.
  8. May help prevent incomplete/inconsistent changes. Few false alarms.
  9. Fine grain prediction works best on stable systems. But even unstable systems the files can be predicted,
  10. Works best in maintenance.

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