An Adaptive Filter-Framework for the Quality Improvement of Open-Source Software Analysis

Lehrstuhl Informatik 5 (Information Systems)

Prof. Dr. M. Jarke 1

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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



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Open Source Software Projects

 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



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Open Source Software Analysis for Software Engineering

 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



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Open Source Software Analysis Research Results

Scacchi, “The Future Research in Free/Open Source Software Development”, 2010



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Techniques for Knowledge Mining in

Development Repositories

 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”

 Mine Data not Noise! Cleaning of Artifacts from Communication and

Development Repositories Needed



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Data Cleaning for Knowledge Mining in Development Repositories

 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



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Adaptive-Filtering Approach Cross-Media Mapping

Artifact types  Mail  Comment   Post   ... Cross-media mapping   Assignment of semantic meaning to artifact elements   Extensibility to new data sources   Same filters for different data



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Adaptive-Filtering Approach Filter Nesting

  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

set with more filters of higher complexity



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Adaptive-Filtering Approach Multi-Threading

 Only new data is filtered   Asynchronous processing: filtered data subset is

provided directly to the next analysis task   Synchronous processing: wait till the complete data

set is filtered



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Dataset Reduction and Content Cleaning Filters

 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 – Quotation Filter – Detection of predefined patterns in content



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Artifact Transformation Filters   Filter as analysis task  Modifies artifact attributes   Example:

–  Core-Periphery Filter: Separates core of community from periphery

–  Hierarchical clustering based on power law distribution



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Validation in BioJava, Biopython and BioPerl OSS: Spam Detection

Spam and spammer level in mailing lists of OSS   Significant amount (up to 60%)  Non-monoton  Distortion of dynamics

BioJava



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Mood within project community   Summarized sentiment of project Mails per month   Positive sentiment of spam advertisement   Incorrect sentiment assignment due to quotation

Validation in BioJava, Biopython and BioPerl OSS: Results Distortion

Year 2004, BioJava



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Adaptive Filter-Framework and OSS Analysis

 OSS Analysis for SE –  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 –  Filter nesting –  Filter as analysis method

An Adaptive Filter-Framework for the Quality Improvement of Open-Source Software Analysis

Technology

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