This document describes an adaptive filter framework for improving the quality of open-source software analysis. It discusses how open-source software projects are community-driven and use web tools for communication and development. It also describes how analyzing these projects can provide insights but the results depend on the quality of the data. The adaptive filter framework allows filtering artifacts from communication and development repositories in a structured way to clean the data and improve analysis results. It features filters that can reduce datasets, clean content, and transform artifacts for cross-medium analysis. The framework was validated on biology-related open-source projects where it helped reduce spam and its distorting effects.

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
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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
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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
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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
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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
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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
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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
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– 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
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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