Information Technology Reference
In-Depth Information
Requirement evolution has also been the subject of significant research [12,
15, 24, 26, 31]. However, to our understanding, most of these works focus on the
issue of management and consistency of requirements. Here, we tackle a more
fundamental question of modeling uncertain evolving requirements in terms of
evolution rules. Our ultimate goal is to support the decision maker in answer-
ing such a question “Given these anticipated evolutions, what is a solution to
implement an evolution-resilient system?”.
This motivates our research in modeling and reasoning on a requirement model
of a system which might evolve sometime in the future. We assume that stake-
holders will know the tentative possible evolutions of the system-to-be, but with
some uncertainty. For example, the Federal Aviation Authority (FAA) document
of the System Wide Information Management (SWIM) for Air Trac Manage-
ment (ATM) lists a number of potential alternatives that subject to other high-
level decisions (
e.g.,
the existence of an organizational agreement for nation-wide
identity management of SWIM users). Such organization-level agreements do not
happen overnight (and may shipwreck at any time) and stakeholders with expe-
rience and high-level positions have a clear visibility of the likely alternatives,
the possible but unlikely solutions, and the politically impossible alternatives.
Our objective is to model the evolution of requirements when it is known to
be possible, but it is unknown whether it would happen: the
known unknown
.
1.1 The Contributions of This Paper
We set up a game-theoretic foundation for modeling and reasoning on evolution-
ary requirement models:
-
A way to model requirement evolutions in terms of two kinds of evolution
rules:
control lable
and
observable
rules that are applicable to many require-
ment engineering models (from problem frames to goal models).
-
A game-theoretic based explanation for probabilities of an observable
evolution.
-
Two quantitative metrics to help the designer in deciding optimal things to
implement for the system-to-be.
This paper is started by a sketch of a case study (
2). To our purpose, we
only focus on requirements of a part of the system-under-study. We distinguish
which requirements are compulsory, and which are optional at design time. Based
on these, we construct simple evolution scenario to illustrate our approach in
subsequent sections,
i.e.
some compulsory requirements become obsoleted, and
some optional ones turn to be mandatory.
Then, we discuss how to model requirement evolution (
§
3) using evolution
rules and probabilities of evolution occurrences. We employ the game-theoretic
interpretation to account for the semantic of probabilities.
We also introduce two quantitative metrics to support reasoning on rule-based
evolutionary requirement models (
§
4). The reasoning is firstly performed on a
simple scenario. Then we show a programmatic way to adapt the technique to
a more complex scenario (
e.g.,
large model, multiple evolutions) (
§
§
5).
