1. Quality Assessment based on Attribute Series of Software Evolution Paper Presentation for CISC 864 Lionel Marks

2. What is this paper about? Defect Prediction Defect Prediction Uses CVS data to analyze characteristics such as: Uses CVS data to analyze characteristics such as: Number of lines added for bug fixes Number of lines added for bug fixes Number of co-changed files Number of co-changed files Number of modifications without a commit message Number of modifications without a commit message Then they took the analysis further Then they took the analysis further

3. The “further” analysis Took “value series” of evolution attributes Took “value series” of evolution attributes These are relative measures These are relative measures For example, for the number of lines of code deleted for bug fixes For example, for the number of lines of code deleted for bug fixes The “value series” version would be: Number of lines deleted for bug fixes/Number of lines deleted (any type) The “value series” version would be: Number of lines deleted for bug fixes/Number of lines deleted (any type)

4. Examples of Evolution Attributes Lines Added/Deleted Lines Added/Deleted Number of Changes Number of Changes Number of Authors Number of Authors Co-Changed Files Co-Changed Files Co-Changed New Files Co-Changed New Files Number of files that were created together with a change to the investigated file Number of files that were created together with a change to the investigated file

5. Examples of Corresponding Value Series Lines Added: Lines Added: Lines added within a day/Total lines of code until this day Lines added within a day/Total lines of code until this day Number of Changes Number of Changes Number of Changes within a day/Total number of changes in the history file until this day Number of Changes within a day/Total number of changes in the history file until this day Number of Authors Number of Authors Number of authors within a day/ Number of changes within this day (Interesting!) Number of authors within a day/ Number of changes within this day (Interesting!) Co-Changed New Files Co-Changed New Files Number of newly introduced files that are co-changed files/number of co-changed files Number of newly introduced files that are co-changed files/number of co-changed files

6. Validation Distance Equation – sum of squares of actual minus estimated Distance Equation – sum of squares of actual minus estimated y is the actual value y is the actual value w is the weight w is the weight a is the attribute a is the attribute Over k instances Over k instances

7. Correlation Coefficient p bar is an average of the predicted values p bar is an average of the predicted values a bar is an average of the actual values a bar is an average of the actual values Value of 1 = perfect correlation Value of 1 = perfect correlation Value of 0 = no correlation Value of 0 = no correlation Value of -1 = inverse correlation Value of -1 = inverse correlation

8. Mean Absolute Error Value of 0 means perfect data Value of 0 means perfect data Value greater than zero shows the error in the data averaged out over the number of points in the set Value greater than zero shows the error in the data averaged out over the number of points in the set

9. Mean Squared Error Instead of using absolute value bars, squares are used to emphasize error more when there are large deviations Instead of using absolute value bars, squares are used to emphasize error more when there are large deviations Still averaged out over the number of points in the data set Still averaged out over the number of points in the data set

10. Results Comm. had a lot of errors in their system Comm. had a lot of errors in their system Authors best indicator overall Authors best indicator overall TLinesAdd a solid metric as well – TLinesAdd a solid metric as well – # of lines added in all co- changed files/#of couplings # of lines added in all co- changed files/#of couplings

11. Likes and Dislikes of This Paper Likes Likes The different series measures were interesting The different series measures were interesting Very impressed with the high correlation found in Authors and Commit Messages Very impressed with the high correlation found in Authors and Commit Messages Nicely related to course work Nicely related to course work Dislikes Dislikes Found the reading bland Found the reading bland Prefer more support for decisions for series attributes, would have liked more discussion on how they decided upon their denominators Prefer more support for decisions for series attributes, would have liked more discussion on how they decided upon their denominators Did not find many unique points to the paper Did not find many unique points to the paper