Comparing Negative Binomial and Recursive Partitioning Models for Fault Prediction

Elaine Weyuker

Thomas Ostrand

Robert Bell

AT&T Labs - Research

To determine which files of a large industrial software system with multiple releases are particularly likely to befault-prone.

High Level Goal

Why is this Important?

• Help testers prioritize testing efforts.

• Help developers decide what to rearchitect.

• Help verifiers decide what to verify.

APPROACH

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.

Past Systems Studied

System Type Period Covered 20% Files

Inventory 4 years 83%

Provisioning 2 years 83%

Voice Resp 2.25 years 75%

Can We Do Better?

Compare results for three different systems making predictions using the negative binomial regression model and recursive partitioning.

Recursive PartitioningSystem A, Releases 1-26, cp = 0.01

Percent Faults in 20% Files

NBR RP

System A 80.5% 76.1%

System B 93.4% 84.8%

System C 76.1% 67.9%