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Quantitative WinWin. A new method for decision support in requirements negotiation

: Ruhe, G.; Eberlein, A.; Pfahl, D.

urn:nbn:de:0011-n-104607 (297 KByte PDF)
MD5 Fingerprint: 8979285fcd1e47389ab465707123c711
Created on: 21.08.2002

Kaiserslautern, 2002, VII, 21 pp. : Ill., Lit.
IESE-Report, 014.02/E
Reportnr.: 014.02/E
Study, Electronic Publication, Report
Fraunhofer IESE ()
requirement negotiation; decision support; quantitative method; analytical hierarchy process; effort estimation; simulation; easy WinWin

Defining, prioritizing, and selecting requirements are problems of tremendous importance. In this paper, a new approach called Quantitative WinWin for decision support in requirements negotiation is studied. The difference to Boehm's WinWin groupware-based negotiation support is the inclusion of quantitative methods as a backbone for better and more objective decisions. Like Boehm's original WinWin, Quantitative WinWin uses an iterative approach, with the aim to increase knowledge about the requirements during each iteration. The novelty of the presented idea is three-fold. Firstly, it uses the Analytical Hierarchy Process for a stepwise determination of the stake-holders' preferences in quantitative terms. Secondly, these results are combined with methods for early effort estimation, in our case using the simulation prototype GENSIM, to evaluate the feasibility of alternative requirements subsets in terms of their related implementation efforts. Thirdly, it reflects the increasing knowledge gained about the requirements during each iteration, in a similar way as it is done in Boehm's spiral model for software development. As main result, quantitative WinWin offers decision support for selecting the most appropriate requirements based on the preferences of the stakeholders, the business value of requirements and a given maximum development effort.

1 Background and Motivation S.1-2
2 Underlying Assumptions and Problem Statement S.3-4
- 2.1 Classes of Stakeholders and Requirements S.3
- 2.2 Problem Statement S.4
3 A Hybrid and Quantitative Approach to Support Requirements Subset Selection S.5-8
- 3.1 Analytic Hierarchy Process S.5-7
- 3.2 Incremental Refinement of Requirements Selection S.7-8
- 3.3 Optimization of Requirements Selection S.8
4 Effort Estimation using "GENSIM" S.9-12
5 Quantitative "WINWIN" - The Overall Algorithm S.13-14
6 Validation of the Approach using "GENSIM" S.15-18
7 Summary and Conclusions S.19
8 Acknowledgements S.19
9 References S.20-23