The validation of software metrics has received much interest over the years due to a desire to promote metrics which are both well-founded theoretically and have also been shown empirically to reflect our intuition and be practically useful. In this paper we describe how we used automatic refactoring to investigate the changes which occur to a number of metrics as software evolves. We use the concept of software volatility to quantify our comparison of the metrics.

Requirements engineers need to be confident that enough requirements analysis has been done before a project can move forward. In the context of KAOS, this information can be derived from the soundness of the refinements: sound refinements indicate that the requirements in the goal-graph are mature enough or good enough for implementation. We can estimate how close we are to `good enough' requirements using the judgments of experts and other data from the goals. We apply Toulmin's model of argumentation to evaluate how sound refinements are. We then implement the resulting argumentation model using Bayesian Belief Networks and provide a semi-automated way aided by Natural Language Processing techniques to carry out the proposed evaluation. We have performed an initial validation on our work using a small case-study involving an electronic document management system.

Multi-objective decisions problems are ubiquitous in requirements engineering. A common approach to solve them is to apply search-based techniques to generate a set of non-dominated solutions, formally known as the Pareto front, that characterizes all solutions for which no other solution performs better on all objectives simultaneously. Analysing the shape of the Pareto front helps decision makers understand the solution space and possible tradeoffs among the conflicting objectives. Interpreting the optimal solutions, however, remains a significant challenge. It is in particular difficult to identify whether solutions that have similar levels of goals attainment correspond to minor variants within a same design or to very different designs involving completely different sets of decisions. Our goal is to help decision makers identify groups of strongly related solutions in a Pareto front so that they can understand more easily the range of design choices, identify areas where strongly different solutions achieve similar levels of objectives, and decide first between major groups of solutions before deciding for a particular variant within the chosen group. The benefits of the approach are illustrated on a small example and validated on a larger independently-produced example representative of industrial problems.