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10 A Machine Learning Approach for Identifying Expert Stakeholders
 

10 A Machine Learning Approach for Identifying Expert Stakeholders

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    10 A Machine Learning Approach for Identifying Expert Stakeholders 10 A Machine Learning Approach for Identifying Expert Stakeholders Presentation Transcript

    • A Machine Learning Approach for Identifying Expert Stakeholders
      Carlos Castro-Herrera
      Jane Cleland-Huang
    • Outline
      Introduction & Motivation
      Case Study
      Identifying Expert Stakeholders
      Direct stakeholders
      Indirect stakeholders
      Inferred stakeholders
      Conclusions and Future work
      9/2/2009
      2
      RE09 • Carlos Castro-Herrera • DePaul University
    • Introduction & Motivation
      Requirements Elicitation:
      Representative group of stakeholders
      Proactively engaged
      Discovery and Analysis of the requirements
      Organizations adopting online collaborative tools
      How to identify Subject Matter Experts?
      Novel technique that automatically analyzes contributions and interests to identify relevant stakeholders
      Machine learning techniques organize contributions into topics
      Identifies three classes of stakeholders:
      Direct, Indirect and Inferred.
      Can be combined into a single ranking.
      9/2/2009
      3
      RE09 • Carlos Castro-Herrera • DePaul University
    • Case Study
      Student Dataset
      36 Masters students:
      Requirements Engineering Class
      366 Feature requests
      Amazon-like web portal
      Purchasing and Selling of school books
      9/2/2009
      4
      RE09 • Carlos Castro-Herrera • DePaul University
    • Direct Stakeholders
      Made specific contributions to a topic
      Topics can be identified:
      Manually
      Automatically
      Automatically:
      Contributions are pre-processed:
      Determine ‘ideal’ amount of topics:
      Can’s Cover Coefficient
      Consensus Clustering using Spherical K-Means:
      Co-Association Matrix: captures the proximity of the needs
      Hierarchical Agglomerative Clustering over the final matrix
      9/2/2009
      RE09 • Carlos Castro-Herrera • DePaul University
      5
    • Direct Stakeholders
      Ex. Encryption Topic:
      28 Additional topics (purchases, used books, shopping cart)
      Contribution Metrics:
      Topic Contribution:
      % or requirements in the topic contributed by a stakeholder
      Topic Specialization
      Inverse of the number of topics a stakeholder is associated with
      9/2/2009
      RE09 • Carlos Castro-Herrera • DePaul University
      6
    • Indirect Stakeholders
      Made contributions to a related topic
      Similarity between topics:
      Vector Space Model using tf-idf
      Cosine similarity metric
      Similarity scores for the Encryption topic:
      Interest of stakeholders in the target topic can be weighted by the similarity of the related topics:
      9/2/2009
      RE09 • Carlos Castro-Herrera • DePaul University
      7
    • Indirect Stakeholders
      Visualize the relationships between topics:
      Ex. Indirect Stakeholders for the Encryption topic:
      9/2/2009
      RE09 • Carlos Castro-Herrera • DePaul University
      8
    • Inferred Stakeholders
      Direct and Indirect are based on content.
      Inferred stakeholders based on behavior profiles
      Collaborative Recommender Systems:
      9/2/2009
      RE09 • Carlos Castro-Herrera • DePaul University
      9
      Infer interest based on the behavior of a stakeholders with similar contribution patterns
    • Inferred Stakeholders
      Neighbors are calculated using a similarity function:
      A prediction score for a particular item is calculated using the function:
      Ex. Top Recommendations for the Encryption topic (inferred stakeholders):
      9/2/2009
      RE09 • Carlos Castro-Herrera • DePaul University
      10
    • Conclusions and Future Work
      Presented a novel technique (proof of concept)
      Uses data mining
      Analyze stakeholders’ contributions
      Indentify potential experts (key stakeholders) for a topic
      Potential uses: Identify stakeholders that:
      Should participate in a new feature
      Can bring in new perspectives to a stagnant discussion
      Will be impacted by a change
      Future work:
      More sophisticated model
      More rigorous evaluation
      9/2/2009
      11
      RE09 • Carlos Castro-Herrera • DePaul University