• We introduced a fully automated framework that quantifies the relationship between microservice architecture complexity, derived from multiple types of statically detected dependencies, and performance-related quality attributes. • We analyzed five complex benchmark systems, including four structurally distinct releases of the Train-Ticket microservice benchmark and one instance of the DeathStarBench suite. • Regarding the relationship between microservice-architecture complexity and overall performance, our analysis demonstrated that systems with poor structural complexity, reflected in high propagation cost and high Clique ratios, exhibited reduced scalability and supported fewer user requests. • Regarding the relationship between individual service complexity and their performance, our results show that endpoints with higher coupling scores tend to have longer response times. Using Spearman’s rank correlation to assess the correlation between each endpoint’s coupling score and its performance score, we find a statistically significant positive correlation. A microservice architecture is intended to promote modularity and evolvability. In this paper, we present an automated framework for assessing the relationship between microservice architecture complexity and performance-related quality attributes. In this framework, we use PPTAM, a performance testing tool, to evaluate system response time under varying user loads, and DV8, an architecture analysis tool, to assess architectural complexity and the complexity of individual services using coupling scores, propagation cost, and architectural antipatterns derived from various types of dependency relations. Using this approach, we evaluated five benchmark systems, including four releases of a microservice system that share similar functionalities but differ in structural design. The results show that microservice architectures with poor complexity scores also exhibited degraded performance outcomes. This automated framework, for the first time, enables a comprehensive measurement of microservice architecture complexity, formed through multiple types of statically extracted dependencies, and its correlation with dynamically obtained performance metrics.

The microservice architecture structures an application as a collection of small autonomous services, enhancing development practices and maintainability. It impacts system performance and scalability, which are influenced by the architectural design, the system deployment, and usage. In this paper, we present a systematic literature review (SLR) on the assessment of performance and scalability in the microservice architecture, aiming to identify research gaps and assess the current state of knowledge. The review protocol screened 801 studies, selecting 29 primary studies for detailed analysis. We perform comparisons of studies and try to identify the main gaps in current research and suggest areas for further investigation. The main conclusion is that the current research partially addresses the dimensions associated with the performance and scalability qualities of microservice systems. In particular, there is a lack of comparative studies of architectural patterns and styles or comparable assessment models and methods. Therefore, a systematic approach is lacking and the paper reports a set of open research trends to guide researchers in the area of microservice-based software architecture quality assessment.

Companies are nowadays looking for the development of solutions based on public and private clouds capable of interoperating with information sources such as other systems, or devices in an IoT and CPS approach, and subsequently using that information efficiently. However, applying appropriate techniques for requirements engineering and designing logical architectures for that context can be complex. The cloud environments are very dynamic and are difficult to identify, clarify, and systematically manage cloud requirements. The lack of requirements engineering methods for this domain carry risks related to incorrect or unjustified decisions, which result in subjective project developments. This paper presents the use of NIST Cloud Computing Reference Architecture in the eliciting of requirements by employing a new approach (the 2P2S technique), that enables the use of an existing Model-Driven Design method (the 4SRS technique), derive logic architectures for cloud-based solutions, assuring that the system requirements are based on effective client needs.

The creation of smart environments that are adaptive and responsive to the context in which they are being used has been one of the strongest ideas in ambient intelligence, but also one of the areas in which there seems to be a persistent gap between the initial promises and its real achievements. A possible reason why this is so challenging is that context-driven interaction and systems support are highly interdependent, but are rarely designed together. In this paper, we analyse the implications of context-driven interaction to the type of systems support provided by smart environments. The analysis is based on the study of previous work in this area and structured around three main dimensions of interaction with smart environments: physical integration; spontaneous interoperation and application programming abstractions. The contributions of this paper are 2-fold: a characterization of the system design space according to the requirements raised by context-driven interaction and the identification of new alternative paths for the integration of context-driven interaction into smart environments.

Human mobility has been widely studied for a variety of purposes, from urban planning to the study of spread of diseases. These studies depend heavily on large datasets, and recent advances in collaborative sensing and WiFi infrastructures have created new opportunities for generating that data. However, these methods and procedures require the participation of a significant community of users through extended periods of time. In this paper, we address the problem of how to engage people to participate in the data collection process. We have conducted a user study on the utilisation of a mobile collaborative sensing application. We have found that users react positively to campaigns, but it is difficult to keep them participating for long periods of time. We also hypothesise that one must close the loop, rewarding the participants with services based on the collected data, eventually showing that there is added value obtainable from crowd sourcing.