RE-PREF: Support for REassessment of PREFerences of Non-functional Requirements for Better Decision-Making in Self-Adaptive Systems

Modelling and reasoning with prioritization of non-functional requirements (NFRs) is a research field that needs more attention. We demonstrate RE-PREF, an approach that supports the modelling of NFRs and their preferences, and discovery of possible scenarios where badly chosen preferences can either make the runtime system miss or suggest unnecessary adaptations that may degrade the behavior of a self-adaptive system (SAS). Specifically, we showcase how RE-PREF is used in a remote data mirroring (RDM) system. The model of NFRs and the analysis of their preferences are enabled by using dynamic decision network (DDNs) and Bayesian Surprise.

[1]  Mary Shaw,et al.  A Design Space for Self-Adaptive Systems , 2010, Software Engineering for Self-Adaptive Systems.

[2]  S. Kullback,et al.  Information Theory and Statistics , 1959 .

[3]  Nelly Bencomo,et al.  Relaxing claims: coping with uncertainty while evaluating assumptions at run time , 2012, MODELS'12.

[4]  Peter Norvig,et al.  Artificial intelligence - a modern approach: the intelligent agent book , 1995, Prentice Hall series in artificial intelligence.

[5]  Javier Poncela,et al.  Wired/Wireless Internet Communications , 2015, Lecture Notes in Computer Science.

[6]  Nelly Bencomo,et al.  Supporting Decision-Making for Self-Adaptive Systems: From Goal Models to Dynamic Decision Networks , 2013, REFSQ.

[7]  Nelly Bencomo,et al.  Requirements-Aware Systems: A Research Agenda for RE for Self-adaptive Systems , 2010, 2010 18th IEEE International Requirements Engineering Conference.

[8]  Nelly Bencomo,et al.  A world full of surprises: bayesian theory of surprise to quantify degrees of uncertainty , 2014, ICSE Companion.

[9]  Nelly Bencomo,et al.  Self-Explanation in Adaptive Systems , 2012, 2012 IEEE 17th International Conference on Engineering of Complex Computer Systems.