Uncertainty and conflicting stakeholders' objectives make many requirements and architecture decisions particularly hard. Quantitative probabilistic models allow software architects to analyse such decisions using stochastic simulation and multi-objective optimisation, but the difficulty of elaborating the models is an obstacle to the wider adoption of such techniques. To reduce this obstacle, this paper presents a novel modelling language and analysis tool, called RADAR, intended to facilitate requirements and architecture decision analysis. The language has relations to quantitative AND/OR goal models used in requirements engineering and to feature models used in software product lines. However, it simplifies such models to a minimum set of language constructs essential for decision analysis. The paper presents RADAR's modelling language, automated support for decision analysis, and evaluates its application to four real-world examples.

Many requirements engineering and software architecture decisions are complicated by uncertainty and multiple conflicting stakeholders objectives. Using quantitative decision models helps clarify these decisions and allows the use of multi-objective simulation optimisation techniques in analysing the impact of decisions on objectives. Existing requirements and architecture decision support methods that use quantitative decision models are limited by the difficulty in elaborating problem-specific decision models and/or lack integrated tool support for automated decision analysis under uncertainty. To address these problems and facilitate requirements and architecture decision analysis, this research proposes a novel modelling language and automated decision analysis technique, implemented in a tool called RADAR. The modelling language is a simplified version of quantitative AND/OR goal models used in requirements engineering and similar to feature models used in software product lines. This research involves developing the RADAR tool and evaluating the tool's applicability, usefulness and scalability on a set of real-world examples.