Supporting decision-making in software engineering with process simulation and empirical studies

Decision-making is a complex and important task in software engineering. The current state-of-the-practice is rather non-systematic as it typically relies upon personal judgment and experience without using explicit models. Empirical studies can help but they are costly to conduct and, to some extent, context dependent. Typically it is not efficient or even possible to conduct empirical studies for a large number of context parameter variations. Process simulation offers decision support as well, but currently suffers from a lack of empirical knowledge on the determinants of underlying system dynamics. In this paper we present an assessment of empirical knowledge and simulation techniques for the area of quality assurance planning. There is a strong interdependency between process simulation and empirical models for decision-making in this area: (a) profound empirical knowledge enables process simulation to support decision-making, and (b) the analysis of simulation results can point out situations and factors for which conducting empirical studies would be most worthwhile. This paper discusses critically some of the most important challenges for decision-making in the area of quality assurance planning.