Information Technology Reference
In-Depth Information
alternative requirements can be specified in RELAX, stating thereby how achievement
conditions for the original requirement can be relaxed. This mechanism can be consid-
ered a particular way to implement the Relegate relation, that is the RELAX mecha-
nism obtains a straightforward interpretation in the language we used here. There can
be other ways to handle uncertainty and relaxation of requirements, and our aim in this
paper was to remain independent of particular approaches.
7
Conclusions and Future Work
We argued in this paper for a general formulation of the runtime requirements adapta-
tion problem, using recent work on the revised general requirements problem and its
core ontology. Taking into account our work on continuous adaptive RE in CARE, and
the types of adaptation defined in CARE, in this paper, we proposed to make explicit
the dynamic parts in requirements representation and formulated the runtime require-
ments adaptation problem. In particular, two key concepts help managing runtime re-
quirements changes, namely the concept of
context
and
resource
, while the
relegation
and
influence
relations capture changes at runtime. The proposed
runtime requirements
adaptation problem
is envisioned as dynamic RE problem for adaptive systems i.e. re-
formulating the requirements problem when changes occur that a SAS can detect, and
then solving the changed problem at runtime.
Ongoing work aims at exploiting these formal definitions of concepts and relations
into a more concrete modeling and analysis language. The concept of requirements
database
Δ
introduced in this paper provides premise to define operations (e.g. adding,
removing, substituting requirements) that a SAS may perform over
Δ
to update its own
specification at runtime thus help realizing continuous adaptive RE (see CARE [17,18]).
Moreover, the application at runtime of automated reasoning (such as in AI planning)
and decision-making techniques (e.g., Analytic Hierarchy Process) may be relevant for
the engineering and running of SAS. They require further investigation.
References
1. Ali, R., Dalpiaz, F., Giorgini, P.: Location-based software modeling and analysis: Tropos-
based approach. In: Li, Q., Spaccapietra, S., Yu, E., Olive, A. (eds.) ER 2008. LNCS,
vol. 5231, pp. 169-182. Springer, Heidelberg (2008)
2. Baresi, L., Pasquale, L., Spoletini, P.: Fuzzy goals for requirements-driven adaptation. In:
18th IEEE Int. Requirements Eng. Conf., Sydney, Australia, pp. 125-134 (2010)
3. Brun, Y., Serugendo, G.D.M., Gacek, C., Giese, H.M., Kienle, H., Litoiu, M., Muller, H.A.,
Pezze, M., Shaw, M.: Engineering self-adaptive systems through feedback loops. Software
Engineering for Self-Adaptive Systems 5525, 48-70 (2009)
4. Cheng, B.H.C., Sawyer, P., Bencomo, N., Whittle, J.: A goal-based modeling approach to
develop requirements of an adaptive system with environmental uncertainty. In: Schurr, A.,
Selic, B. (eds.) MODELS 2009. LNCS, vol. 5795, pp. 468-483. Springer, Heidelberg (2009)
5. Cheng, B.H., de Lemos, R., Giese, H., Inverardi, P., Magee, J.: Software Engineering for Self-
Adaptive Systems: A Research Roadmap. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inver-
ardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 5525,
pp. 1-26. Springer, Heidelberg (2009)
