A framework for characterization and analysis of software system scalability
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Prioritisation mechanisms to support incremental development of agent systems
International Journal of Agent-Oriented Software Engineering
Tool-supported requirements prioritization: Comparing the AHP and CBRank methods
Information and Software Technology
i*-prefer: optimizing requirements elicitation process based on actor preferences
Proceedings of the 2009 ACM symposium on Applied Computing
Proceedings of the eighteenth international symposium on Software testing and analysis
Prioritizing Legal Requirements
RELAW '09 Proceedings of the 2009 Second International Workshop on Requirements Engineering and Law
Using an SMT solver for interactive requirements prioritization
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
A more expressive softgoal conceptualization for quality requirements analysis
ER'06 Proceedings of the 25th international conference on Conceptual Modeling
Interactive requirements prioritization using a genetic algorithm
Information and Software Technology
PWWM: a personal web workflow methodology
The Personal Web
Uncertainty handling in goal-driven self-optimization - Limiting the negative effect on adaptation
Journal of Systems and Software
A systematic literature review of software requirements prioritization research
Information and Software Technology
Hi-index | 0.00 |
Case-based driven approaches to requirements prioritization proved to be much more effective than first-principle methods in being tailored to a specific problem, that is they take advantage of the implicit knowledge that is available, given a problem representation. In these approaches, first principle prioritization criteria are replaced by a pairwise preference elicitation process. Nevertheless case-based approaches, using the Analytic Hierarchy Process (AHP) technique, become impractical when the size of the collection of requirements is greater than about twenty since the elicitation effort grows as the square of the number of requirements. We adopt a case-based framework for requirements prioritization, called Case-Based Ranking, which exploits machine learning techniques to overcome the scalability problem. This method reduces the acquisition effort by combining human preference elicitation and automatic preference approximation. Our goal in this paper is to describe the framework in details and to present empirical evaluations which aim at showing its effectiveness in overcoming the scalability problem. The results prove that on average our approach outperforms AHP with respect to the trade-off between expert elicitation effort and the requirement prioritization accuracy.