1. How to Predict More with Less: Defect Prediction Using Machine Learners in an Implicitly Data Starved Domain
Kim Kaminsky Gary D. Boetticher
Department of Computer Science
University of Houston - Clear Lake
Houston, Texas, USA

2. Preamble
The maturing of Software Engineering as a discipline requires a better understanding of the complexity of the software process. Empirical-based modeling is one mechanism for improving the understanding, and thus management of the software process.

3. Data Starvation Issues in Software Engineering
Heavily context dependent
Measure A from Project X  Measure B from Project Y
Unreliable data due to poor processes
Organizations do not share data
Projects are large project estimation data occurs infrequently

4. Implicitly Data Starved Domains
Lots of this
Number of modules
Little of that
Defect counts

5. Equalized Learning
Balance Data by Replicating Sparse Instances [Mizuno99]
300 Instances
of 0 Defects
of 5 Defects
of 9 Defects
3 Colors = 3 Diff. Instances
300 Instances
of 0 Defects
20 Instances/ 5 Defects
10 Instances/9 Defects

6. Genetic Programming Process - 1
Fitness Value =
Model performance
on data.
2 (of many) Chromosomes
Data + A B * - 3 D
888 out of 1000
913 out of 1000

7. Genetic Programming Process - 2
Mutation
2 Chromosomes
Crossover + B - 3 D * A + A B + B - D
3.1 * A - 3 D

8. } NASA KC2 Defect Dataset Equalized produces 3013 samples 379 Unique
tuples }
Output: Defect Count
Input: Product Metrics
(Size, Complexity, Vocabulary)

9. Original versus Equalized Data Experiment Configuration
2000 Characters
1000 Chromosomes
50 Generations Max.
20 Trials

10. Original versus Equalized Data t-test Results

11. Conclusions Equalized learning spawns large datasets
Equalized learning produces better models

12. Future Directions Apply to other NASA datasets
Improve Performance: Distributed GP