1. FUZZY CLUSTERING OF SOFTWARE METRICS Paper by Scott Dick and Abraham Kandel Presentation by Craig Castaneda

2. Metrics, Quality, and Resources  Developers monitor various attributes of the development process through metrics in an effort to improve the quality of the final product.  The metrics can be used to guide the allocation of additional development resources to “high-risk” modules.

3. Metrics and the Problems with Metrics  Metrics quantifies some aspect of the source code.  This may be some count of how many things there are, or may be a measure of density.  Unfortunately all of these metrics are strongly correlated not only to quality, but each other as well, known as multicollinearity.

4. Multicolinearity  Multicolinearity arises as a result of the variables not being independent of each other. This means they are not ORTHOGONAL.  When variables are orthogonal to each other, a point in the solution space, can be written uniquely.  When variables are non-orthogonal, the same spot can be written in more than 1 way. Because of this, linear regression fails to produce an answer.

5. Other Problems  While there is data for small modules with small numbers of errors, there really isn’t any sample data when it comes to large modules with large numbers of errors.  This means that the analysis is only a valid predictor over a limited range.

7. Lets get fuzzy  In binary logic, things are one thing or another “Raise your hand if you’re a boy”  But what if you asked a group of kids, “Do you like school?”  We need a bit of logic that allows for being a little of both sides, and a way to describe how much in either direction.

8. Fuzzy in groups  In set and group theory, usually an item can belong to a group or not.  Fuzzy groups allows an item to belong to multiple sets, with an indicator of how strongly it belongs in that set.

9. How is it used here  While we don’t know a software’s future failure rate, we do know the metrics involved in creating the module.  These metrics can be used to define a module in feature space, and we can assign a “How much of a problem will this be” number to it.

10. How is it used part 2  By doing this with all of the modules in the software, we can rank which modules are likely to cause the most problems.  Using a pareto analysis, we can assign additional resources appropriately.

11. A look at the data – MIS Dataset  390 total Modules  11 Software Metrics per Module  Number of changes to each module were also recorded.  Assumption that number of changes represents number of failures, but unverified.

12. A look at the data – ProcSoft  Structured Programming  Contains 422 Modules  11 Metrics  No data on change metrics

13. A look at the data – OOSoft  Object Oriented  562 Methods  11 Metrics  Same functionality as ProcSoft  No data on change metrics

14. A look at the method  Fuzzy C-Clustering requires the number of clusters to be determined beforehand  They ran the data for 2 through 10 clusters  Analyzed after which number of clusters would be the best to use.

15. Results – Before we look  Cluster Compactness and Separation  How distinct, each cluster is from the other clusters, and how close the data points are to each epicenter. We want this high.  Fuzzy Separation index  A measure of how fuzzy the line between clusters, we want this low.  Avg SSE – Average Predicted Error  A measure of our predicted error as a %, we want this low

16. MIS – Data Set

17. Results 2 – Before we look  Mean – Average = Total changes / modules  Median – Middle number when ordered by magnitude  If mean < median – The low end was farther away from the median than the high end.

18. MIS – Data Set 2

19. MIS – Data Set 3

20. ProcSoft

21. ProcSoft 2

22. OOSoft

23. OOSoft 2

24. Conclusions  The technique selects modules for additional work that would not normally be found by ranking individual modules. That’s a good thing.  The nature of the technique is repeatable when the programs came from the same paradigm (procedural).  Programs from the Object-oriented paradigm did not fit the pattern established by the procedural programs, this could be due to paradigm itself, and metrics more suited to the paradigm may be needed.