10 A Machine Learning Approach for Identifying Expert Stakeholders

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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.

Technology Education

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

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Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management. The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM). Speakers: Bob Boule Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle. Gopinath Rebala Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.

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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap. The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies. Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques What will you get from this session? 1. Insights into SAP testing best practices 2. Heatmap utilization for testing 3. Optimization of testing processes 4. Demo Topics covered: Execution from the test manager Orchestrator execution result Defect reporting SAP heatmap example with demo Speaker: Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP

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The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development. The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers: State of global ICS asset and network exposure Sectoral targets and attacks as well as the cost of ransom Global APT activity, AI usage, actor and tactic profiles, and implications Rise in volumes of AI-powered cyberattacks Major cyber events in 2024 Malware and malicious payload trends Cyberattack types and targets Vulnerability exploit attempts on CVEs Attacks on counties – USA Expansion of bot farms – how, where, and why In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East Why are attacks on smart factories rising? Cyber risk predictions Axis of attacks – Europe Systemic attacks in the Middle East Download the full report from here: https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/

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10 A Machine Learning Approach for Identifying Expert Stakeholders

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

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