D.3.3 ALGORITHMS FOR INCREMENTAL ... - SecureChange

5 A framework for modeling and reasoning
on goal models evolution
In this section we present an enhanced version of the framework for modelling and
reasoning on evolution of requirements models which was proposed in the previous
version of this deliverable submitted at M24. The framework is based on the idea of
modelling evolution of requirement models in terms of two kinds of evolution rules:
controllable and observable rules. The reasoning is based on the computation of two
quantitative metrics called maximal belief and residual risk that intuitively measure the
usefulness of a design alternative (or a set of elements) after evolution. In fact, the
maximal belief tells whether a design alternative is useful after evolution, while residual
risk quantifies if a design alternative is no longer useful. The framework during the third
year of the project has been applied to goal models. In this section we will present an
optimal algorithm to compute maximal belief and residual risk metrics for goal models.
5.1 Reasoning on goal models evolution
To illustrate the calculation of Max Belief and Residual Risk, let‘s consider the
introduction of the AMAN as example. The critical mission of AMAN is to support
ATCOs to manage the arrival traffic safely and efficiently, e.g. by maintaining an
appropriate separation between aircrafts. The AMAN calculates an optimal arrival
sequence considering many constraints such as flight profiles, aircraft types, airspace
and runway condition, inbound flow rate, as well as meteorological conditions. The final
sequence is approved by ATCOs. Then, the AMAN generates various kinds of
advisories for ATCO to send to pilots e.g. time to lose or gain, top-of-descent, track
extension, while their execution is continuously monitored to react to possible
violations. The sequence and other relevant data are exchanged with adjacent sectors
to improve collaboration and reduce conflicts.
This high level goal of AMAN is refined to many other subgoals, as illustrated by the
hypergraph in Figure 10. The hypergraph consists of nodes (rounded rectangles) that
represent goals to be achieved and circles that represent the AND decomposition of
goals. Here the example only focuses on the sub goal ‘G2- optimal arrival sequence
applied’ and ‘G5- Data exchanged’. The former goal concerns the generation and
application of an optimal arrival sequence with respect to a given situation. The latter
goal is to exchange data for collaborating with other units.
D.3.3 Algorithms for Incremental Requirements Models
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