The document discusses data mining of software repositories to improve software quality. It introduces data mining and describes using techniques like classification and clustering on data from version control systems and bug tracking databases. The results section shows error prediction on Eclipse and Firefox projects. Future work includes improving performance and developing new procedures to aid software visualization, change management and other topics.

1. Introduction
Data Mining
And the results are
A vision over the present and the future
Mining Software Repositories
Improving software
Pere Urb´n Bayes
o
Data Management Group
Dept. Arquitectura de Computadors
Universitat Polit`cnica de Catalunya
e
purbon@ac.upc.edu
July of 2009
Pere Urb´n Bayes
o Mining Software Repositories

2. Introduction
Data Mining
And the results are
A vision over the present and the future
Index
Introduction
Data Mining
The results
The future
Pere Urb´n Bayes
o Mining Software Repositories

3. Introduction
Motivations
Data Mining
The Situation
And the results are
Objectives
A vision over the present and the future
The problem
Companies need to own highly available and reliable software.
The software of low quality harms both, clients and producers.
Unfortunately, avoiding defects is a difficult task to undertake.
Project Leaders need to keep an eye inside to many projects.
Software engineer tend not to document software in deep.
The complexity of software projects is growing every day.
Pere Urb´n Bayes
o Mining Software Repositories

4. Introduction
Motivations
Data Mining
The Situation
And the results are
Objectives
A vision over the present and the future
The software development process
Pere Urb´n Bayes
o Mining Software Repositories

5. Introduction
Motivations
Data Mining
The Situation
And the results are
Objectives
A vision over the present and the future
Support tools
Tools used to support software development:
Version Control server.
Bug Tracker server.
Project Management server.
Life cycle management software.
...
This set of tools store a huge amount of information during the
process, Why not to use this information to improve our software?
Pere Urb´n Bayes
o Mining Software Repositories

6. Introduction
Motivations
Data Mining
The Situation
And the results are
Objectives
A vision over the present and the future
Objective and Applications
Objectives:
Analyse the use of data mining technology, to data stored in
support tools, with the aim to improve software quality.
Develop an experimental prototype tool.
Applications:
Reduce the error rate.
Provides a non-exploited source of documentation.
Provide a new source of support tools for IDE’s.
Pere Urb´n Bayes
o Mining Software Repositories

7. Introduction
Data Mining Introduction
And the results are The use of
A vision over the present and the future
Data mining
Type of database analysis that attempts to discover useful patterns
or relationships in a group of data. The analysis uses advanced
statistical methods, such as cluster analysis, and sometimes
employs artificial intelligence or neural network techniques. A
major goal of data mining is to discover previously unknown
relationships among the data, especially when the data come from
different databases.
Pere Urb´n Bayes
o Mining Software Repositories

8. Introduction
Data Mining Introduction
And the results are The use of
A vision over the present and the future
Methods
Types of:
Traditional Data Mining (K-Means, C4.5, Bayesian Networks).
Relational Data Mining (ILP, Markov logic networks,
Relational bayesian methods, Dependency Networks).
Categories:
Clusterers
Classifiers
Associative rules
Network Models.
Pere Urb´n Bayes
o Mining Software Repositories

9. Introduction
Data Mining Introduction
And the results are The use of
A vision over the present and the future
Data mining
Type of database analysis that attempts to discover useful patterns
or relationships in a group of data. The analysis uses advanced
statistical methods, such as cluster analysis, and sometimes
employs artificial intelligence or neural network techniques. A
major goal of data mining is to discover previously unknown
relationships among the data, especially when the data come from
different databases.
Pere Urb´n Bayes
o Mining Software Repositories

10. Introduction
Data Mining Introduction
And the results are The use of
A vision over the present and the future
Issue detection
LOC DefectAppearence2Month RevisionsAuthor
LineAddedIRLAdd ReportedI2Month Revision2Month
LineAddedIRLDel Revision3Month Releases
AlterType DefectAppearence3Month ReportedI1Month
AgeMonths ReportedI3Month ReportedIssues
RevisionAge Revision5Month ReportedI5Month
DefectReleases DefectAppearence5Month
Revision1Month DefectAppearance1Month
Question: Has this file a non detected error. The exact number of
errors can be predicted to.
Pere Urb´n Bayes
o Mining Software Repositories

11. Introduction
Data Mining Introduction
And the results are The use of
A vision over the present and the future
Another types of objectives
Predict bugs related to a software developer.
Prediction of bugs in software components.
This techniques could be used in different topics:
Software understanding.
Software evolution.
Software visualization.
Change propagation.
Impact analysis.
Software complexity.
Fault prediction.
Pere Urb´n Bayes
o Mining Software Repositories

12. Introduction
Data Mining Error prediction
And the results are Software
A vision over the present and the future
Error prediction
Eclipse Project Firefox Project
Correctly classified 94.65% 94.822%
Statistics Kappa 0.893 0.8883
Precision 0.9465 0.9482
Recall 0.945 0.949
AUC ROC 0.9682 0.9808
Eclipse-Firefox Firefox-Eclipse
Correctly classified 82.0065% 87.975%
Statistics Kappa 0.5976 0.7595
Precision 0.818 0.894
Recall 0.82 0.88
AUC ROC 0.805 0.83
Pere Urb´n Bayes
o Mining Software Repositories

13. Introduction
Data Mining Error prediction
And the results are Software
A vision over the present and the future
The end App
Pere Urb´n Bayes
o Mining Software Repositories

14. Introduction
Data Mining Software libraries
And the results are An envision
A vision over the present and the future
The Prototype
Software being used:
Programming: JAVA
Database: MySQL and MonetDB.
Data Mining: Weka 3.6 and Proximity 4.3
XML: Apache Xerces 2.9.1
SVN, CVS : svnkit 1.3.0, for CVS netbeans-cvs lib and a
custom rcs file parser.
Presentation: Prefuse Visualization Toolkit and Weka
Drawing facilities.
Pere Urb´n Bayes
o Mining Software Repositories

15. Introduction
Data Mining Software libraries
And the results are An envision
A vision over the present and the future
Could python give use the same?
Machine Learning:
Orange: With 1.0 this lib has many interesting and useful
methods, Classification, Regression and Clustering. The most
similar to Weka.
PyML: Only has classifier facilities.
Shogun: Only for Support Vector Machines.
RPy: An interface to R.
Databases:
The most important relational databases are available via
DB-API.
ZODB: Zope Object Database.
Metakit: An embedded database with a not defined paradigm.
Pygr: Python graph database framework for bioinformatics.
Pere Urb´n Bayes
o Mining Software Repositories

16. Introduction
Data Mining Software libraries
And the results are An envision
A vision over the present and the future
Could python give use the same?
Presentation:
Graph Drawing: NetworkX, with nice result. There are some
other but they look incomplete.
GUI: PyQT, wxWindows, pyGTK. It’s your taste XD!.
SVN, CVS processing:
SVN: pysvn - Python interface to Subversion.
CVS: It seams nothing is available.
GIT: PyGit - Pythonic git bindings targeted towards
porcelains.
XML Processing could be done using built-in support and with any
SAX or DOM parser.
Pere Urb´n Bayes
o Mining Software Repositories

17. Introduction
Data Mining Software libraries
And the results are An envision
A vision over the present and the future
The future
Known issues:
Data preprocessing performance.
Database performance, is the relational model valid?
Dynamic procedure addition.
The Todo List:
Develop new procedures over different related topics, like
software visualization, change support, etc.
Develop a more mature software. Python could help in some
parts. This software must be easily extensible.
Improve the hole process performance.
Pere Urb´n Bayes
o Mining Software Repositories

18. Introduction
Data Mining Software libraries
And the results are An envision
A vision over the present and the future
The end
Question?
Pere Urb´n Bayes
o
Data Management Group
Dept. Arquitectura de Computadors
Universitat Polit`cnica de Catalunya
e
purbon@ac.upc.edu
Pere Urb´n Bayes
o Mining Software Repositories