1. Automated Source Code Changes Classification
for Effective Code Review and Analysis
Evgeny G. Knyazev
Senior developer
«Transas Technologies»
Post-graduate student
SPb State University of Information Technologies, Fine Mechanics and Optics
Работа выполняется в рамках работы над кандидатской диссертацией. Результаты апробируются в процессе работы в Транзас Технологии.

2. Source Code Review
Informal source code look-through trying to find different kind problems in it

3. Source Code Review Helps to…
increase code quality
find errors on early stages
know all code of a system
keep an eye on novices work

4. Source Control System and Code Changes Review
Source control system keeps development history
It allows to review only changed code
Change
Request
(Revision X)
Review
Developer

5. Code Change Review Example

6. Changes Review Task Complexity
In large project a lot of changes need to be reviewed
Project
Tortoise SVN
Navi-Manager
KDE
Size,
LOC
~ 200 thousand
~ 250 thousand
~ 4.7 million
Observation Period
~1 month
Changes Count
215 72
11841 (!)

7. New Functionality Implementation
The Solution
Split changes into classes
Choose class for review
New Functionality Implementation
Refactoring
Bugfix
Cosmetics
Code Deletion

8. The solution (2) Automate changes classification Source Control System
Automated
Changes
Classifier
Change
Change
Class
Is This Class
Interesting ?
Developer
Review
Yes

9. Known Code Changes Classification Methods
Changes Comments Classification
“bug”, “fixed” – a bug fix
“implement”, “feature” – new feature implementation
Refactoring Search Using Changes Metrics
Extract parent class (DIT>0 и NOM<0, …)
Move to other class (DIT=0 и NOM<0, …)
Split method (NOM < T, ...)
Difference Search in Semantic Graphs
Build code graph before and after the change
Generate transition script
Search refactoring templates

10. Changes Metrics Clustering Method: Learning Phase
1. Learning Set Preparation
2. Expert Classification of Learning Set
3. Change Metrics Calculation
4. Change Metrics Vectors Clustering
5. Mapping Clusters to Expert Classes

11. Fuzzy Change Metrics Clustering Algorithm

12. Changes Metrics Clustering Method: Changes Classification
1. Changes Metrics Calculation
2. Map Changes to
Nearest Clusters of
Learning Set
3. Computation of Change Class by Cluster-Class Mapping,
Built During Learning

13. Changes Metrics Calculated as subtraction of revisions metrics
∆M = Mr – Mr-1
CC – Cyclomatic Complexity (number of linearly independent paths in execution graph)
CS – number of Classes/Structures
eLOC – Effective Lines of Code (without empty and comment lines)

14. Metrics Calculation and Clustering of Changes from Navi-Manager Project
Revision CC IC
eLOC
Nearest Cluster
Change Comment
16820 -2 +1 -5 1
Vessel objects now merged in one transaction.
16833 -1 2
Deleted an extra commit command.
17026 +4
+18 3
Full format of lat and lon during polling report.
17029 4
Set MessageSource.UpdateTime after processing of each change.
17038
Revert changes from r There’s no need to update time after each message processed.
17107
+12
+89
Implementation of first version of vessel tracks loading from MonServer.

15. Fuzzy Clusters of Revisions Table
Revision / Cluster 1 2 3 4
16820
0,78
0,14
0,00
0,08
16833
0,02
0,79
0,20
17026
0,21
0,32
0,11
0,36
17029
0,03
0,30
0,67
17038
17107
0,1
0,68

16. Method Learning Example
Project: Navi-Manager
Size of Learning Set: 29 changes
Number of Clusters: 4
Cluster
Expert Class 1
Refactoring 2
Code Deletion 3
New Functionality Implementation 4
Bugfix

17. Classification Example
Revision
Nearest Class
Nearest Cluster
Change Comment
16820
Refactor. 1
Vessel objects now merged in one transaction.
16833
Delete Func. 2
Deleted an extra commit command.
17026
New Feature 3
Full format of lat and lon during polling report.
17029
Bugfix 4
Set MessageSource.UpdateTime after processing of each change.
17038
Del. Func.
Revert changes from r There’s no need to update time after each message processed.
17107
Implementation of first version of vessel tracks loading from MonServer.

18. Classification Fuzziness
Change r16833 «Deleted an extra commit command» classified as:
On 2% as refactoring
On 79% as code deletion
On 0% as new functionality implementation
On 20% as bugfix

19. Code Changes Classification in Software Development Process
Dev.Team
Leader
Testing Team
Developer
Project
Manager
Source
Code

20. Changes Control During Important Development Phases
Deny potentially destabilizing changes classes
Dev Phase
Main Dev Cycle
Stop Сode
Code Freeze
Change Class
New Functionality Implementation + –
Code Deletion
Refactoring
Small Bugfixes
Critical Bugfixes

21. Request List of Changes by Class
For Example: request refactorings list done in specific version X
Automated
Source Code Changes
Classifier
Dev Team
Leader
Request Refactorings
in Version X
List of Refactorings
in Version X
List
of Changes
in Version X
Source
Control
System

22. Project Statistics Analysis

23. Achieved Results on Navi-Manager Project
Effectiveness
More than 50% time economy on code review
Development Problems Discover
Too much bugfixes comparing to new feature implementations

24. Automated Changes Classification Tool
Works with Subversion
Low depended from program language
Calculates CC, CS, eLOC metrics
Discovers change classes:
New feature implementation
Code deletion
Refactoring
Cosmetic Changes
Bugfixes*
* Исправление ошибки классифицируется по остаточному принципу: что не классифицируется как любой другой класс, есть исправление ошибки.

25. Future Research Method improvements Refactorings classification
Gustavson-Kessel Clustering
Object and coupling metrics usage
Refactorings classification
Application widening
Usage in development process on constant basis
Adaptability analysis for different types of projects

26. Thank you!
Any questions?