Simulating mixed agile and plan-based requirements prioritization strategies: proof-of-concept and practical implications

In this paper, we address the efficacy and pragmatics of mixing two primary strategies for requirements prioritization in order to incorporate the benefits of both plan-based (PB) and agile development methods while avoiding their drawbacks. As it is intractable to directly study the performance of strategies on real projects, we conducted a comprehensive empirically based simulation under a variety of conditions of requirements dynamism, project size, and duration. Simulation results suggest that a mixed strategy for requirements prioritization seems to work best in all but cost for typical levels of dynamism on average. Our findings also indicate that, as theorized, PB and agile strategies perform well within opposite extremes of dynamism. However, they do not outperform the mixed strategies even within their home grounds – that is large and complex systems with stable requirements for PB, and small and dynamic projects for agile methods. Given the unknown, unknowable, or variable nature of dynamism and the dramatic differences in effectiveness for agile and PB strategies under extreme development scenarios, a mixed strategy appears to yield the best results overall. We introduce two mixed strategies – simply adding cost–benefit (CB) to the agile approach, and a more sophisticated ‘hybrid’ (HY) approach that modulates development iteration size to maximize the expected CB for each iteration. We propose a step-by-step method to implement this HY strategy. We provide a structured analysis of the benefits and assumptions of agile and PB requirements prioritization methods (e.g., Pareto optimization), and outline a framework for analyzing and assessing the effectiveness of strategies including several new metrics. This research can furthermore serve as a framework for future validation of the proposed mixed strategies using actual software projects.