Elane - Promise08

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Comparing Negative Binomial and Recursive Partitioning Models for Fault Prediction

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Elane - Promise08

  1. 1. Comparing Negative Binomial and Recursive Partitioning Models for Fault Prediction Elaine Weyuker Thomas Ostrand Robert Bell AT&T Labs - Research
  2. 2. High Level Goal To determine which files of a large industrial software system with multiple releases are particularly likely to be fault-prone.
  3. 3. Why is this Important? <ul><li>Help testers prioritize testing efforts. </li></ul><ul><li>Help developers decide what to rearchitect. </li></ul><ul><li>Help verifiers decide what to verify. </li></ul>
  4. 4. APPROACH <ul><li>Identify properties that are likely to affect fault-proneness, and then build a statistical model to make predictions. In the past we’ve used a Negative Binomial Regression Model. </li></ul>
  5. 5. Past Systems Studied 75% 2.25 years Voice Resp 83% 2 years Provisioning 83% 4 years Inventory 20% Files Period Covered System Type
  6. 6. Can We Do Better? <ul><li>Compare results for three different systems making predictions using the negative binomial regression model and recursive partitioning. </li></ul>
  7. 7. Recursive Partitioning System A, Releases 1-26, cp = 0.01
  8. 11. Percent Faults in 20% Files 67.9% 76.1% System C 84.8% 93.4% System B 76.1% 80.5% System A RP NBR

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