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Citation for published version (APA):Mahdavi-Hezavehi, S., Galster, M., & Avgeriou, P. (2013). Variability in quality attributes of service-based software systems: A systematic literature review. Information and Software Technology, 55(2), 320-343. a b s t r a c tContext: Variability is the ability of a software artifact (e.g., a system, component) to be adapted for a specific context, in a preplanned manner. Variability not only affects functionality, but also quality attributes (e.g., security, performance). Service-based software systems consider variability in functionality implicitly by dynamic service composition. However, variability in quality attributes of service-based systems seems insufficiently addressed in current design practices. Objective: We aim at (a) assessing methods for handling variability in quality attributes of service-based systems, (b) collecting evidence about current research that suggests implications for practice, and (c) identifying open problems and areas for improvement. Method: A systematic literature review with an automated search was conducted. The review included studies published between the year 2000 and 2011. We identified 46 relevant studies. Results: Current methods focus on a few quality attributes, in particular performance and availability. Also, most methods use formal techniques. Furthermore, current studies do not provide enough evidence for practitioners to adopt proposed approaches. So far, variability in quality attributes has mainly been studied in laboratory settings rather than in industrial environments. Conclusions: The product line domain as the domain that traditionally deals with variability has only little impact on handling variability in quality attributes. The lack of tool support, the lack of practical research and evidence for the applicability of approaches to handle variability are obstacles for practitioners to adopt methods. Therefore, we suggest studies in industry (e.g., surveys) to collect data on how practitioners handle variability of quality attributes in service-based systems. For example, results of our study help formulate hypotheses and questions for such surveys. Based on needs in practice, new approaches can be proposed.

Handling multiple quality attributes (QAs) in the domain of self-adaptive systems is an understudied research area. One well-known approach to engineer adaptive software systems and fulfill QAs of the system is architecture-based self-adaptation. In order to develop models that capture the required knowledge of the QAs of interest, and to investigate how these models can be employed at runtime to handle multiple quality attributes, we need to first examine current architecture-based self-adaptive methods. ObjectiveIn this paper we review the state-of-the-art of architecture-based methods for handling multiple QAs in self-adaptive systems. We also provide a descriptive analysis of the collected data from the literature. MethodWe conducted a systematic literature review by performing an automatic search on 28 selected venues and books in the domain of self-adaptive systems. As a result, we selected 54 primary studies which we used for data extraction and analysis. ResultsPerformance and cost are the most frequently addressed set of QAs. Current self-adaptive systems dealing with multiple QAs mostly belong to the domain of robotics and web-based systems paradigm. The most widely used mechanisms/models to measure and quantify QAs sets are QA data variables. After QA data variables, utility functions and Markov chain models are the most common models which are also used for decision making process and selection of the best solution in presence of many alternatives. The most widely used tools to deal with multiple QAs are PRISM and IBM's autonomic computing toolkit. KLAPER is the only language that has been specifically developed to deal with quality properties analysis. ConclusionsOur results help researchers to understand the current state of research regarding architecture-based methods for handling multiple QAs in self-adaptive systems, and to identity areas for improvement in the future. To summarize, further research is required to improve existing methods performing tradeoff analysis and preemption, and in particular, new methods may be proposed to make use of models to handle multiple QAs and to enhance and facilitate the tradeoffs analysis and decision making mechanism at runtime.

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.