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Eliciting Requirements for Search based Requirements Prioritisation
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Eliciting Requirements for Search based Requirements Prioritisation

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The slides for my SEBASE talk.

The slides for my SEBASE talk.

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  • What is the relationship
  • salience
  • http://research.it.uts.edu.au/re/cgi-bin/resources_srs.cgi
  • What is the relationship
  • What is the relationship

Transcript

  • 1. Eliciting Requirements for Search based Requirements Prioritisation Soo Ling Lim Department of Computer Science University College London
  • 2.  
  • 3. Stakeholders Requirements Multi-Objective
  • 4. Stakeholders Requirements Multi-Objective
  • 5. Guildford, UK © River Wey
  • 6. Software project failure rate FAILURE SUCCESS CHAOS Report, The Standish Group International, Inc.
  • 7. Build a Stake holder Net work
  • 8. Step 1: Find initial stakeholders Users Developers Legislators Decision-makers
  • 9. Step 2: Get recommendations
  • 10. Step 2: Get recommendations <Alice, Director of Estates, 4>
  • 11. Step 3: Draw social network
  • 12. Step 3: Draw social network Alice
  • 13. Step 3: Draw social network Alice Carl Bob
  • 14. Step 3: Draw social network Alice Carl Bob
  • 15. Step 4: Apply social network measures
    • Betweenness
    • Load
    • Closeness
    • PageRank
    • Degree
    • In-degree
    • Out-degree
    S.L. Lim, D. Quercia & A. Finkelstein (2010) StakeNet: Using Social Networks to Analyse the stakeholders of Large-Scale Software Projects. In 32 nd Int. Conf. on Soft. Eng.
  • 16. RALIC: UCL Access Control Project
  • 17. Recommendations
  • 18. Identifying Stakeholders Found them! Omitted external library users Existing Method StakeNet Precision 89% 90% Recall 40% 90%
  • 19. StakeNet vs. Individuals legislators users developers directors managers StakeNet 79% Role Accuracy ( ρ)
  • 20. Stakeholders Requirements Multi-Objective
  • 21. http://research.it.uts.edu.au
  • 22. RALIC requirements Version 1.2
  • 23. RALIC requirements Version 1.2 Who wants what?
  • 24. StakeRare: Recommender Assisted Requirements Elicitation
  • 25. 1. Each stakeholder rates requirements
  • 26. 1. Each stakeholder rates requirements <Alice, R1, 3> <Alice, R2, 2> <Alice, R3, 5> <Alice, R8, -1>
  • 27. 2. Predict stakeholders’ needs (kNN)
  • 28. You want R1, R2, and R3 2. Predict stakeholders’ needs (kNN)
  • 29. 2. Predict stakeholders’ needs (kNN) You want R1, R2, and R3 You may also want R4, R5, and R7
  • 30.  
  • 31. Identifying Requirements
  • 32. Predicting Requirements S.L. Lim & A. Finkelstein (2011) StakeRare: Social Networks and Collaborative Filtering for Large-Scale Requirements Elicitation. IEEE Trans. on Soft. Eng.
  • 33. BUT…In StakeNet and StakeRare, Analyst interviews stakeholders
  • 34. For large projects…
  • 35. www.stakesource.co.uk S.L. Lim, D. Quercia & A. Finkelstein (2010) StakeSource. In 32 nd Int. Conf. on Soft. Eng.
  • 36. StakeSource 2.0 S.L. Lim, D. Damian & A. Finkelstein (2011) StakeSource2.0. In 33 rd Int. Conf. on Soft. Eng.
  • 37. > 50 Trial Requests Since Dec‘09
  • 38. > 10 Projects Used StakeSource
    • IS Division, UCL
    • Bartlett Faculty of Built Environment , UCL
    • British Library
    • National Institute of Informatics, Japan
    • WestGrid, Canada
    • Allestis, Australia
  • 39. Project Networks Edubase (NII, Japan) IRIS (UCL ISD) RIM (British Lib) Healthy Cities (Bartlett) [1] S.L. Lim, F. Ishikawa, D. Damian & A. Finkelstein (2011) Using Web 2.0 to engage Stakeholders: the Good, Bad and the Ugly (submitted). [2] S.L. Lim & P. Bentley (2011) Evolving relationships between social networks and stakeholder involvement in software projects. GECCO’11 SBSE track .
  • 40. Stakeholders Requirements Multi-Objective
  • 41. Treat each set of stakeholder’s ratings as an objective to optimise
  • 42. MOO Algorithms
    • Sum of Weighted Objectives (Single Objective)
    • Non-Dominated Sorting (NDS)
    • NSGA-II
    • PAES
    • Alternative highly multi-objective methods:
      • Weighted Maximum Ranking (WMR)
      • Weighted Average Ranking (WAR)*
      • Sum of Weighted Ratios (SWR)
      • Sum of Weighted Global Ratios (SWGR)
  • 43. Findings
    • Pareto optimal front too large - contains many solutions that are not desirable
    • For ~80 objectives not feasible to visualise or select good solutions
    • Requirements engineers want an optimal solution, not a range of solutions to choose from
    • Clustering? PCA? Alternative methods?
    • What about 100,000 stakeholders?
  • 44. Soo Ling Lim [email_address] © Anne Geddes