Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction

Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction Norman Fenton Martin Neil, William Marsh, Peter Hearty and Łukasz Radliński, Paul Krause PROMISE 20 May 2007

Overview
Background
The data
Results
Caveats

Background
Predicting reliability
Statistical models
Causal models

Causal model (Bayesian network) Probability of finding defect Testing process effectiveness Testing process quality Testing effort Testing staff experience Quality of documented test cases Testing process well-defined

Background
AID
MODIST

Schematic view of model Existing code base Defect insertion and recovery Testing and rework Design and development Specification and documentation Common influences Scale of new required functionality

How Model was developed
Qualitative and quantitative factors
Expert judgement
Published data
Company data
NOT THE DATA HERE!!!

Example question: “Relevant Experience of Spec & Doc Staff”
Very High : Over 3 years experience in requirements management, and extensive domain knowledge.
High : Over 3 years experience in requirements management, but limited domain knowledge.
Medium : 1-3 years experience in requirements management.
Low : 1-3 three years experience, but no experience in requirements management.
Very Low : Less than one year’s experience, and no previous domain experience.

How projects were selected
Reliable Data
Satisfactory end
Key people available
Breadth
Depth

Defects vs size

Actual versus predicted defects

Caveats
Biased priors
Structural aspects biased
Data accuracy
Projects overly ‘uniform’

Conclusions
No ‘data fitting’
Dataset provided a validation
Good predictions with few of the inputs
Causal model provides genuine support for risk management

Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction

by promise07 on May 20, 2007

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Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction — Presentation Transcript