Multi-Stakeholder Tensioning Analysis in Requirements Optimisation

1. Multi-Stakeholder Tensioning Analysis in Requirements Optimisation Yuanyuan Zhang CREST Centre University College London

2. Agenda Background Problem Solution Empirical Study Conclusion

3. Requirements Engineering Process • Evolution & Management • Modelling& Analysis Background Problem Solution Empirical Study Conclusion University College London yuanyuan.zhang@cs.ucl.ac.uk

4. Requirements Selection & Optimisation Task Using prioritisation, visualisation, and optimisation techniques helps decision maker to select the optimal or near optimal subset from all possible requirements to be implemented. Background Problem Solution Empirical Study Conclusion University College London yuanyuan.zhang@cs.ucl.ac.uk

5. Goals Problem Background Solution Empirical Study Conclusion University College London yuanyuan.zhang@cs.ucl.ac.uk

6. Search based Requirements Optimisation Use of meta-heuristic algorithms to automate and optimise requirements analysis process • Choose appropriate representation of problem • Define problem specific fitness function (to evaluate potential solutions) • Use search based techniques to lead the search towards optimal points in the solution space Solution Background Problem Empirical Study Conclusion University College London yuanyuan.zhang@cs.ucl.ac.uk

7. Representation Stakeholder: Weight: Requirements: Cost: Solution Background Problem Empirical Study Conclusion University College London yuanyuan.zhang@cs.ucl.ac.uk

8. Representation • Each stakeholder assigns a value to requirements : • Each stakeholder has a subset of requirements that expect to be fulfilled denoted by • The overall score of a given requirement can be calculated by: Solution Background Problem Empirical Study Conclusion University College London yuanyuan.zhang@cs.ucl.ac.uk

9. Data Set Generation & Initialisation Matlab Files .mat .dat Real World Data Sets format process input Requirement.Number Requirement.Value Requirement.Cost Requirement.Dependency Stakeholder.Number Stakeholder.Weight Stakeholder.Subset Requirement-Stakeholder Matrix.Density Random.Distribution 27 Combination Random Data Sets generate initialise format Other Random Data Sets Solution Background Problem Empirical Study Conclusion University College London yuanyuan.zhang@cs.ucl.ac.uk

10. Value/Cost Trade-off Analysis Basic Value/Cost Requirements Selection Process Statistic Analysis ANOVA Analysis Search Algorithms Today/Future Importance NSGA-II Spearman’s Rank Correlation Archive-based NSGA-II Interaction Management Fitness-Invariant Dependency Two-Achieve Fitness-Affecting Dependency Pareto GA Iterate? Single Objective GA Yes Multi-Stakeholder Analysis Tensioning Analysis Greedy* No Random* Fairness Analysis Change? No Yes Data Sets Regeneration * Strictly speaking, these are not search algorithms.

11. Result Representation and Visualisation Results 2D and 3D Pareto Fronts Requirements Subsets for Release Planning Kiviat Diagrams Insight Characteristic of Data Sets Marked Line Charts represent & visualise communicate & feedback Performance of the Algorithms Convergence Diversity Solution Background Problem Empirical Study Conclusion King’s College London yuanyuan.zhang@kcl.ac.uk

12. Visualisation Pareto Optimal Front Solution Background Problem Empirical Study Conclusion University College London yuanyuan.zhang@cs.ucl.ac.uk

13. Fitness Functions The problem is to select a set of requirements that maximise the total value to each stakeholder, which is expressed as a percentage. The model of fitness functions represented as: Maximise subject to Solution Background Problem Empirical Study Conclusion University College London yuanyuan.zhang@cs.ucl.ac.uk

14. Data Sets Used 1. Motorola Data Set: 35 Requirements and 4 Stakeholders 2. Greer and Ruhe Data Set: 20Requirements and 5 Stakeholders Empirical Study Background Problem Solution Conclusion King’s College London yuanyuan.zhang@kcl.ac.uk

15. Data Sets Used 3. 27 Combination Levels of Random Data Sets: the No. of requirements the No. of stakeholders the density of the stakeholder-requirement matrix Empirical Study Background Problem Solution Conclusion King’s College London yuanyuan.zhang@kcl.ac.uk

16. Multi-Stakeholder Kiviat Diagram Sta. 1 100% 75% 50% 25% 0% Sta. 2 Sta. 4 Sta. 3 Empirical Study Background Problem Solution Conclusion University College London yuanyuan.zhang@cs.ucl.ac.uk

17. Multi-Stakeholder Tensioning Analysis Motorola data set 30% Budgetary Resource Constraint 70% Empirical Study Background Problem Solution Conclusion University College London yuanyuan.zhang@cs.ucl.ac.uk

18. Solutions on the Pareto Front

19. Average Solutions

20. Tensions between the Stakeholders’ Satisfaction for Different Budgetary Resource Constraints

21. Multi-Stakeholder Tensioning Analysis Greer and Ruhe data set 30% Budgetary Resource Constraint 70% Empirical Study Background Problem Solution Conclusion University College London yuanyuan.zhang@cs.ucl.ac.uk

22. Algorithms’ Performance Empirical Study Background Problem Solution Conclusion University College London yuanyuan.zhang@cs.ucl.ac.uk

23. Algorithms’ Performance 1 . The diversity of the Two-archive algorithm is significant in most cases 2. The Two-archive and NSGA-II algorithms always have a better convergence than the Random Search 3. The Two-Archive algorithm outperforms NSGA-II and Random Search in terms of convergence in some case Empirical Study Background Problem Solution Conclusion University College London yuanyuan.zhang@cs.ucl.ac.uk

24. Conclusion • Present the concept of multi-stakeholder tensioning in requirements analysis • Treat each stakeholder as a separate objective to maximise the requirement satisfaction • Present the empirical study and statistical analysis • Provide a simple visual method to show the tensioning • http://www.sebase.org/sbse/publications University College London yuanyuan.zhang@cs.ucl.ac.uk