The Value of Exact Analysis in Requirements Selection

Uncertainty is characterised by incomplete understanding. It is inevitable in the early phase of requirements engineering, and can lead to unsound requirement decisions. Inappropriate requirement choices may result in products that fail to satisfy stakeholders' needs, and might cause loss of re...

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Bibliographic Details
Published inIEEE transactions on software engineering Vol. 43; no. 6; pp. 580 - 596
Main Authors Lingbo Li, Harman, Mark, Fan Wu, Yuanyuan Zhang
Format Journal Article
LanguageEnglish
Published New York IEEE 01.06.2017
IEEE Computer Society
Subjects
Decisions
exact multi-objective optimisation
next release problem
Optimization
Requirements analysis
Revenue
Robustness
simulation optimisation
Software
Software algorithms
Software engineering
Solvers
Stakeholders
Uncertainty
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Summary:Uncertainty is characterised by incomplete understanding. It is inevitable in the early phase of requirements engineering, and can lead to unsound requirement decisions. Inappropriate requirement choices may result in products that fail to satisfy stakeholders' needs, and might cause loss of revenue. To overcome uncertainty, requirements engineering decision support needs uncertainty management. In this research, we develop a decision support framework METRO for the Next Release Problem (NRP) to manage algorithmic uncertainty and requirements uncertainty. An exact NRP solver (NSGDP) lies at the heart of METRO. NSGDP's exactness eliminates interference caused by approximate existing NRP solvers. We apply NSGDP to three NRP instances, derived from a real world NRP instance, RALIC, and compare with NSGA-II, a widely-used approximate (inexact) technique. We find the randomness of NSGA-II results in decision makers missing up to 99.95 percent of the optimal solutions and obtaining up to 36.48 percent inexact requirement selection decisions. The chance of getting an inexact decision using existing approximate approaches is negatively correlated with the implementation cost of a requirement (Spearman r up to -0.72). Compared to the inexact existing approach, NSGDP saves 15.21 percent lost revenue, on average, for the RALIC dataset.
ISSN:0098-5589
1939-3520
DOI:10.1109/TSE.2016.2615100