Presented at Software Engineering Conference 2013 in Aachen

1. Layers
An Adaptive Filter-Framework for the
Quality Improvement of Open-Source
Software Analysis
Advanced Community Information Systems (ACIS)
RWTH Aachen University, Germany
Anna Hannemann, Michael Hackstein, Ralf
Klamma, Matthias Jarke
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
1 This slide deck is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

2. Open Source Software Projects
Layers  Community-driven Development
 Voluntary participation
 Communication, project management and
development via Web tools
 Some successful and famous examples
 Smaller niche projects
 A long-tail of unsuccessful projects
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
2

3. Open Source Software Analysis for
Software Engineering
Layers  Understand, model, simulate and organize
community-driven development
 Agile development practices
 Distributed and intercultural practices
 New success factors
 Long-term freely available datasets
 Low cost empirical studies
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
3

4. Open Source Software Analysis
Research Results
Layers
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
4 Scacchi, “The Future Research in Free/Open Source Software Development”, 2010

5. Techniques for Knowledge Mining in
Development Repositories
Layers
 Results
are only as good as data is!
 Remember DNA Phantom?
“A hypothesized unknown female serial killer as a result of
contaminated cotton swabs used for collecting DNA”
 MineData not Noise!
Cleaning of Artifacts from Communication and
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
Development Repositories Needed
5

6. Data Cleaning for Knowledge Mining
in Development Repositories
Layers
 Data-structure independence: variable artifacts types
 Additive filtering: filter only new data
 Filter nesting: sequence of arbitrary order
 Consistent data format: cross-medium analysis
 Consistent and easy-to-use interface
 Extensibility: continuous evolution
 Adaptive database insertion
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
6

7. Adaptive-Filtering Approach
Cross-Media Mapping
Layers
Artifact types
 Mail
 Comment
 Post
 ...
Cross-media mapping
 Assignment of semantic meaning to artifact elements
 Extensibility to new data sources
Lehrstuhl Informatik 5
(Information Systems)  Same filters for different data
Prof. Dr. M. Jarke
7

8. Adaptive-Filtering Approach
Filter Nesting
Layers  Sequence of filters F1, F2, …, FN
 Results in same predefined format
 One filter – one cleaning (analysis) task
 Each filter triggers its predecessor
 Complex filter as a combination of several filters
 Filtering triggered on demand
 Filtering of a subset possible
 Simple filters first and than analysis of reduced data
Lehrstuhl Informatik 5
(Information Systems)
set with more filters of higher complexity
Prof. Dr. M. Jarke
8

9. Adaptive-Filtering Approach
Multi-Threading
Layers
 Only new data is filtered
 Asynchronous processing: filtered data subset is
provided directly to the next analysis task
Lehrstuhl Informatik 5
 Synchronous processing: wait till the complete data
set is filtered
(Information Systems)
Prof. Dr. M. Jarke
9

10. Dataset Reduction and Content
Cleaning Filters
Layers  Dataset Reduction Filter (DRF)
– Reduces amount of artifacts
– Select artifacts, which fulfill certain criteria
– Example
– Spam detection
– Artifact classification based on Bayes Decision Rule
 Content Cleaning Filter (CRF)
– Modifies content of artifacts
– Example
Lehrstuhl Informatik 5
– Quotation Filter
(Information Systems)
Prof. Dr. M. Jarke
10
– Detection of predefined patterns in content

11. Artifact Transformation Filters
Layers  Filter
as analysis task
 Modifies artifact attributes
 Example:
– Core-Periphery Filter: Separates
core of community from periphery
– Hierarchical clustering based on
power law distribution
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
11

12. Validation in BioJava, Biopython and
BioPerl OSS: Spam Detection
Layers
BioJava
Spam and spammer level in mailing lists of OSS
 Significant amount (up to 60%)
 Non-monoton
 Distortion of dynamics
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
12

13. Validation in BioJava, Biopython and
BioPerl OSS: Results Distortion
Layers
Year 2004, BioJava
Mood within project community
 Summarized sentiment of project Mails per month
 Positive sentiment of spam advertisement
 Incorrect sentiment assignment due to quotation
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
13

14. Adaptive Filter-Framework and OSS
Analysis
 OSS Analysis for SE
Layers
– Methods/metrics for knowledge mining in company
communication and development repositories
– Understanding of community-oriented development:
principles, obstacles and advantages
! Data Cleaning: Results are only as good as data is!
 Adaptive Filter-Framework
– Significant noise level in data
– Adaptable for any Web artifact format
Lehrstuhl Informatik 5
– Filter nesting
(Information Systems)
Prof. Dr. M. Jarke
14 – Filter as analysis method