10 A Machine Learning Approach for Identifying Expert Stakeholders

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10 A Machine Learning Approach for Identifying Expert Stakeholders

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

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