This document presents a machine learning approach to identify expert stakeholders for requirements elicitation. It analyzes contributions from students to identify direct stakeholders who contributed to specific topics, indirect stakeholders who contributed to related topics, and inferred stakeholders with similar contribution patterns. The approach clusters topics and calculates metrics to identify relevant stakeholders. It provides a proof of concept for using data mining to analyze stakeholders' contributions and identify potential experts for requirements elicitation.

1. A Machine Learning Approach for Identifying Expert StakeholdersCarlos Castro-HerreraJane Cleland-Huang

2. OutlineIntroduction & MotivationCase StudyIdentifying Expert StakeholdersDirect stakeholdersIndirect stakeholdersInferred stakeholdersConclusions and Future work9/2/20092RE09 • Carlos Castro-Herrera • DePaul University

3. Introduction & MotivationRequirements Elicitation:Representative group of stakeholdersProactively engagedDiscovery and Analysis of the requirementsOrganizations adopting online collaborative toolsHow to identify Subject Matter Experts?Novel technique that automatically analyzes contributions and interests to identify relevant stakeholdersMachine learning techniques organize contributions into topicsIdentifies three classes of stakeholders: Direct, Indirect and Inferred.Can be combined into a single ranking.9/2/20093RE09 • Carlos Castro-Herrera • DePaul University

4. Case StudyStudent Dataset36 Masters students:Requirements Engineering Class366 Feature requestsAmazon-like web portalPurchasing and Selling of school books9/2/20094RE09 • Carlos Castro-Herrera • DePaul University

5. Direct StakeholdersMade specific contributions to a topicTopics can be identified:Manually AutomaticallyAutomatically:Contributions are pre-processed:Determine ‘ideal’ amount of topics: Can’s Cover CoefficientConsensus Clustering using Spherical K-Means:Co-Association Matrix: captures the proximity of the needsHierarchical Agglomerative Clustering over the final matrix9/2/2009RE09 • Carlos Castro-Herrera • DePaul University5

6. Direct StakeholdersEx. Encryption Topic:28 Additional topics (purchases, used books, shopping cart)Contribution Metrics:Topic Contribution: % or requirements in the topic contributed by a stakeholderTopic SpecializationInverse of the number of topics a stakeholder is associated with9/2/2009RE09 • Carlos Castro-Herrera • DePaul University6

7. Indirect StakeholdersMade contributions to a related topicSimilarity between topics:Vector Space Model using tf-idfCosine similarity metricSimilarity scores for the Encryption topic:Interest of stakeholders in the target topic can be weighted by the similarity of the related topics:9/2/2009RE09 • Carlos Castro-Herrera • DePaul University7

8. Indirect StakeholdersVisualize the relationships between topics:Ex. Indirect Stakeholders for the Encryption topic:9/2/2009RE09 • Carlos Castro-Herrera • DePaul University8

9. Inferred StakeholdersDirect and Indirect are based on content.Inferred stakeholders based on behavior profilesCollaborative Recommender Systems:9/2/2009RE09 • Carlos Castro-Herrera • DePaul University9Infer interest based on the behavior of a stakeholders with similar contribution patterns

10. Inferred StakeholdersNeighbors 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):9/2/2009RE09 • Carlos Castro-Herrera • DePaul University10

11. Conclusions and Future WorkPresented a novel technique (proof of concept)Uses data mining Analyze stakeholders’ contributionsIndentify potential experts (key stakeholders) for a topicPotential uses: Identify stakeholders that:Should participate in a new featureCan bring in new perspectives to a stagnant discussionWill be impacted by a changeFuture work:More sophisticated modelMore rigorous evaluation9/2/200911RE09 • Carlos Castro-Herrera • DePaul University