1. The Organizational Impacts on Software Quality and Defect Estimation
Stephen Lopez-Couto

2. Discussion Topics Introduction Defect Concepts
Definition of a defect
Static vs. Dynamic Discovery
Defect Estimation Methods
Linear Regression
Capture/Recapture AI
Defect Estimation Variables
Further Research
Conclusion

3. Introduction Defect Estimation
The act of “guessing” the number of defects that exist in a current software baseline
Purpose of this paper is to determine the usefulness of metrics that are not explicitly tied to the code to make the estimates

4. Introduction Organizational Elements: Development Team Size
Developer Experience
Institutional Processes
CMMI level for example
Development Tools
Development Schedule
Programming Language
Software Architecture

5. Defect Concepts: What is a Defect?
There is no common definition of a defect
Consider the following:
Program is supposed to add the variables ‘y’ and ‘i’ together and put the value into ‘x’
int i = 5;
int y = 6;
int x = y+1
Print(x);

6. Defect Concepts: What is a Defect?
The programmer introduced a defect
Correct code: int x = y + i
There is no common definition of a defect
Consider the following:
Program is supposed to add the variables ‘y’ and ‘i’ together and put the value into ‘x’
int i = 5;
int y = 6;
int x = y+1
Print(x);

7. Defect Concepts: What is a Defect?
Two things occur before a defect can be detected
Fault – Incorrect value in the internal state of the program
Failure – When a fault is realized as output
int i = 5;
int y = 6;
int x = y+1 // Causes a Fault
Print(x); // Causes a Failure
A Failure may not be detected until well after the fault has occurred!

8. Defect Concepts: Static Vs. Dynamic Discovery
Static – Does not utilize executing code
Software Inspections
Walkthroughs
Complexity Mapping
Dynamic – Utilizes executing, compiled code
Runtime tests
Automated defect discovery tools

9. Defect Estimation Methods
Estimating the number of defects is by no means an exact science
There are lots of different ways to determine an estimate
Most of the methods are highly tied to a specific organization or software baseline
Not good enough for general use
Three general methods will be discussed
Linear Regression
Capture/Recapture
Artificial Intelligence
Bayesian Belief Networks
Neural Networks

10. Defect Estimation Methods Linear Regression
Determines a mathematical expression that relates some number of independent variables (the input metrics) and the dependent variable (defect estimation)
Variable Type: Numeric Only

11. Defect Estimation Methods Capture/Recapture
Some number of defects are seeded into the software at development time. When testing occurs ratio of seeded to unseeded defects found is used to estimate the total number.
Variable Type: N/A. Only track defect counts.

12. Defect Estimation Methods AI: Bayesian Belief Networks
A network that weighs the relationship among different variables is created (using expert knowledge) and processed using Bayesian probabilities to determine the quality of the software.
Variable Type: any

13. Defect Estimation Methods AI: Neural Networks
A multilayer perceptron using the back propagation algorithm is trained on legacy defect data and then provides estimates based on input data.
Variable Type: Numeric Only

14. Defect Estimation Variables

15. Further Research Three main areas
Transformation of non numerical data into a numerical form
Additional estimation methods that do not utilize numeric only data
Determination of common relationships among the input metrics, independent of which method is used

16. Conclusion
Organizational Element Data is useful when paired with the proper estimation method
Limitations of the most common method (linear regression) has led to a general shunning of these metrics
Defect Estimation approaches are not ready for real world use
They are too tied to specific cases

17. Questions
Questions?