1. A Machine Learning Approach for Identifying Expert Stakeholders Carlos Castro-Herrera Jane Cleland-Huang

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. Indirect Stakeholders Visualize the relationships between topics: Ex. Indirect Stakeholders for the Encryption topic: 9/2/2009 RE09 • Carlos Castro-Herrera • DePaul University 8

9. 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

10. 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

11. 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