Modeling and verification of Functional and Non-Functional Requirements of ambient Self-Adaptive Systems

The analysis of self-adaptive systems (SAS) requirements involves addressing uncertainty from several sources. Despite advances in requirements for SAS, uncertainty remains an extremely difficult challenge. In this paper, we propose REFAS, a framework to model the requirements of self-adaptive software systems. Our aim with REFAS is to address and reduce uncertainty and to provide a language with sufficient power of expression to specify the different aspects of self-adaptive systems, relative to functional and non-functional requirements. The REFAS modeling language includes concepts closely related to these kind of requirements and their fulfillment, such as context variables, claims, and soft dependencies. Specifically, the paper´s contribution is twofold. First, REFAS supports different viewpoints and concerns related to requirements modeling, with key associations between them. Moreover, the modeler can define additional models and views by exploiting the REFAS meta-modeling capability, in order to capture additional aspects contributing to reduce uncertainty. Second, REFAS promotes in-depth analysis of all of the modeled concerns with aggregation and association capabilities, especially with context variables. Furthermore, we also define a process that enforces modeling requirements, considering different aspects of uncertainty. We demonstrate the applicability of REFAS by using the VariaMos software tool, which implements the REFAS meta-model, views, and process.

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Non-functional Requirements Trade-Off in Self-Adaptive Systems

2018 4th International Workshop on Requirements Engineering for Self-Adaptive, Collaborative, and Cyber Physical Systems (RESACS) ◽

10.1109/resacs.2018.00007 ◽

2018 ◽

Author(s):

Ahmed Abdo Ali Saeed ◽

Seok-Won Lee

Keyword(s):

Adaptive Systems ◽

Functional Requirements ◽

Trade Off ◽

Self Adaptive

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

2016 IEEE 24th International Requirements Engineering Conference (RE) ◽

10.1109/re.2016.29 ◽

2016 ◽

Cited By ~ 1

Author(s):

Luis H. Garcia Paucar ◽

Nelly Bencomo

Keyword(s):

Decision Making ◽

Adaptive Systems ◽

Functional Requirements ◽

Self Adaptive

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PERSA: A Requirements Specification Process for Self-Adaptive Systems Based on Fuzzy Logic and NFR-Framework

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488517500064 ◽

2017 ◽

Vol 25 (01) ◽

pp. 145-178 ◽

Cited By ~ 2

Author(s):

João Dionisio Paraiba ◽

Luiz Eduardo G. Martins

Keyword(s):

Fuzzy Logic ◽

Case Studies ◽

Adaptive Systems ◽

Automated System ◽

Human Intervention ◽

Functional Requirements ◽

Early Assessment ◽

Wide Variability ◽

Theoretical Proposal ◽

Self Adaptive

Self-Adaptive Systems are able to change their behavior at runtime according to the environment where they are. This study presents an approach to specify the requirements for self-adaptive systems based on the concepts of Fuzzy Logic, which deals with factors such as ambiguity, uncertainties and vague information on the solution of problems; and NFR-Framework, which deals with the non-functional requirements which, very often, vaguely and full of uncertainties present themselves. Adaptive systems consist of (functional and non-functional) requirements, which hold the capacity to modify themselves during the runtime with little or no human intervention at all. Requirements that carry out the feature of wide variability are called adaptive requirements. PERSA (acronym from “Processo de Especificação de Requisitos Adaptativos”, in Portuguese) is reported in this work, using the Fuzzy Logic and the NFR-Framework as a basis, since both offers resources to manage uncertainties, an inherent attribute of self-adaptive systems. This process aims the approach of specification of adaptive requirements in a systematic way providing a guide to support requirements engineers. PERSA Process is settled in three mains phases subdivided into several steps. Two case studies were developed to validate it: the first deals with an automated system to prepare steaks, which needs to adapt to its several types; the second relates to a system for automation for canine diets, which must be adapted to different breeds of dogs according to their size, weight and classification. The case studies provide a first approach of the use and benefits of PERSA Process. In these studied the theoretical proposal was evaluated and discussed in order to establish the degree of understanding, the clarity of activities and the necessary adjustments to improve the proposed achievements, thus obtaining a satisfactory though early assessment which answers the purpose of specifying the requirements for self-adaptive systems.

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Uncertainty in Self-adaptive Systems: A Research Community Perspective

ACM Transactions on Autonomous and Adaptive Systems ◽

10.1145/3487921 ◽

2020 ◽

Vol 15 (4) ◽

pp. 1-36

Author(s):

Sara M. Hezavehi ◽

Danny Weyns ◽

Paris Avgeriou ◽

Radu Calinescu ◽

Raffaela Mirandola ◽

...

Keyword(s):

Adaptive Systems ◽

Future Research ◽

Functional Requirements ◽

Second Stage ◽

Uncertainty Sources ◽

Uncertainty Handling ◽

Reference Process ◽

Key Characteristics ◽

The Impact ◽

Self Adaptive

One of the primary drivers for self-adaptation is ensuring that systems achieve their goals regardless of the uncertainties they face during operation. Nevertheless, the concept of uncertainty in self-adaptive systems is still insufficiently understood. Several taxonomies of uncertainty have been proposed, and a substantial body of work exists on methods to tame uncertainty. Yet, these taxonomies and methods do not fully convey the research community’s perception on what constitutes uncertainty in self-adaptive systems and on the key characteristics of the approaches needed to tackle uncertainty. To understand this perception and learn from it, we conducted a survey comprising two complementary stages in which we collected the views of 54 and 51 participants, respectively. In the first stage, we focused on current research and development, exploring how the concept of uncertainty is understood in the community and how uncertainty is currently handled in the engineering of self-adaptive systems. In the second stage, we focused on directions for future research to identify potential approaches to dealing with unanticipated changes and other open challenges in handling uncertainty in self-adaptive systems. The key findings of the first stage are: (a) an overview of uncertainty sources considered in self-adaptive systems, (b) an overview of existing methods used to tackle uncertainty in concrete applications, (c) insights into the impact of uncertainty on non-functional requirements, (d) insights into different opinions in the perception of uncertainty within the community and the need for standardised uncertainty-handling processes to facilitate uncertainty management in self-adaptive systems. The key findings of the second stage are: (a) the insight that over 70% of the participants believe that self-adaptive systems can be engineered to cope with unanticipated change, (b) a set of potential approaches for dealing with unanticipated change, (c) a set of open challenges in mitigating uncertainty in self-adaptive systems, in particular in those with safety-critical requirements. From these findings, we outline an initial reference process to manage uncertainty in self-adaptive systems. We anticipate that the insights on uncertainty obtained from the community and our proposed reference process will inspire valuable future research on self-adaptive systems.

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