Early aspects: aspect-oriented requirements engineering and architecture design

This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications

A Systematic Classification and Analysis of NFRs

The main agenda of Requirements Engineering (RE) is the development of tools, techniques and languages for the elicitation, specification, negotiation, and validation of software requirements. However, this development has traditionally been focused on functional requirements (FRs), rather than non-functional requirements (NFRs). Consequently, NFR approaches developed over the years have been fragmental and there is a lack of clear understanding of the positions of these approaches in the RE process. This paper provides a systematic classification and analysis of 89 NFR approaches

Goal-Oriented Requirements Engineering: State of the Art and Research Trend

The Goal-Oriented Requirements Engineering (GORE) is one approach that is widely used for the early stages of software development. This method continues to develop in the last three decades. In this paper, a literature study is conducted to determine the GORE state of the art. The study begins with a Systematic Literature Review (SLR) was conducted to determine the research trend in the last five years. This study reviewed 126 papers published from 2016 to 2020.  The research continues with the author's search for scientific articles about GORE. There are 26 authors who actively publish GORE research results. Twenty-six authors were grouped into seven groups based on their relation or co-authoring scientific articles. An in-depth study of each group resulted in a holistic mapping of GORE research.  Based on the analysis, it is known that most research focuses on improving GORE for an automated and reliable RE process, developing new models/frameworks/methods originating from GORE, and implementing GORE for the RE process. This paper contributes to a holistic mapping of the GORE approach. Through this study, it is known the various studies that are being carried out and research opportunities to increase automation in the entire RE process

The Reassessment of Preferences of Non-Functional Requirements for Better Informed Decision-making in Self-Adaptation

Decision-making requires the quantification and trade-off of multiple non-functional requirements (NFRs) and the analysis of costs and benefits between alternative solutions. Different techniques have been used to specify utility preferences for NFRs and decision-making strategies of self-adaptive systems (SAS). These preferences are defined during design-time. It is well known that correctly identifying the weight of the NFRs is a major difficulty. In this paper we present initial results of a novel approach that provides a set of criteria to re-assess NFRs preferences given new evidence found at runtime using dynamic decision networks (DDNs). The approach use both conditional probabilities provided by DDNs and the concept of Bayesian surprise. The results show that our approach supports better informed decisions under uncertainty by identifying new situations where the current SAS preferences may need to be re-evaluated to improve the levels of satisfaction of NFRs

Managing Requirement Volatility in an Ontology-Driven Clinical LIMS Using Category Theory. International Journal of Telemedicine and Applications

Requirement volatility is an issue in software engineering in general, and in Web-based clinical applications in particular, which often originates from an incomplete knowledge of the domain of interest. With advances in the health science, many features and functionalities need to be added to, or removed from, existing software applications in the biomedical domain. At the same time, the increasing complexity of biomedical systems makes them more difficult to understand, and consequently it is more difficult to define their requirements, which contributes considerably to their volatility. In this paper, we present a novel agent-based approach for analyzing and managing volatile and dynamic requirements in an ontology-driven laboratory information management system (LIMS) designed for Web-based case reporting in medical mycology. The proposed framework is empowered with ontologies and formalized using category theory to provide a deep and common understanding of the functional and nonfunctional requirement hierarchies and their interrelations, and to trace the effects of a change on the conceptual framework.Comment: 36 Pages, 16 Figure

A survey on preferences of quality attributes in the decision-making for self-adaptive systems:The bad, the good and the ugly

Different techniques have been used to specify preferences for quality attributes and decision-making strategies of self-adaptive systems (SAS). These preferences are defined during requirement specification and design time. Further, it is well known that correctly identifying the preferences associated with the quality attributes is a major difficulty. This is exacerbated in the case of SAS, as the preferences defined at design time may not apply to contexts found at runtime. This paper aims at making an exploration of the research landscape that have addressed decision-making and quality attribute preferences specification for selfadaptation, in order to identify new techniques that can improve the current state-of-the-art of decision-making to support self-adaptation. In this paper we (1) review different techniques that support decisionmaking for self-adaptation and identify limitations with respect to the identification of preferences and weights (i.e. the research gap), (2) identify existing solutions that deal with current limitations

Requirements-aware models to support better informed decision-making for self-adaptation using partially observable Markov decision processes

A self-adaptive system (SAS) is a system that can adapt its behaviour in re- sponse to environmental fluctuations at runtime and its own changes. Therefore, the decision-making process of a SAS is challenged by the underlying uncertainty. In this dissertation, the author focuses on the kind of uncertainty associated with the satisficement levels of non-functional requirements (NFRs) given a set of design decisions reflected on a SAS configuration. Specifically, the focus of this work is on the specification and runtime handling of the uncertainty related to the levels of satisficement of the NFRs when new evidence is collected, and that may create the need of adaptation based on the reconfiguration of the system. Specifically, this dissertation presents two approaches that address decision-making in SASs in the face of uncertainty. First, we present RE-STORM, an approach to support decision- making under uncertainty, which uses the current satisficement level of the NFRs in a SAS and the required trade-offs, to therefore guide its self-adaptation. Second, we describe ARRoW, an approach for the automatic reassessment and update of initial preferences in a SAS based on the current satisficement levels of its NFRs. We eval- uate our proposals using a case study, a Remote Data Mirroring (RDM) network. Other cases have been used as well in different publications. The results show that under uncertain environments, which may have not been foreseen in advance, it is feasible that: (a) a SAS reassess the preferences assigned to certain configurations and, (b) reconfigure itself at runtime in response to adverse conditions, in order to keep satisficing its requirements