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Abstract-Several service-based systems quality assurance proposals 147 that aggregate monitoring and analysis facilities can be found 148 in the literature. To conduct the search of the related work, we 149 have revised the most relevant conferences and journals in the 150 area, selecting those papers that were scoped in the field of 151 SLA monitoring and analysis. Furthermore, we have increased 152 the results by adding relevant papers obtained from experts in 153 the field. Table I summarizes the results of this study. 154We have examined the selected papers under the four factors 155 described in the introduction. The first three factors fall into 156 the functionality of the proposed solution, whereas the fourth 157 factor falls into its architecture. 158Functionality. Considering the three factors for functionality 159 identified in the introduction, we focus on the following issues: 160 (1) Which SLAs are supported, (2a) How the information 161 to configure the monitor is specified, (2b) How the QoS 162 monitoring result is specified and (3) How the violations are 163 explained. 164Architecture. The issues arising from this factor are: (4a) 165 Which architectural elements are needed and (4b) How the 166 architectural elements are structured. 167We analyse these issues below:

Context: Quality of Service (QoS) is a major issue in various web service related activities. Quality models have been proposed as the engineering artefact to provide a common framework of understanding for QoS, by defining the quality factors that apply to web service usage. Objective: The goal of this study is to evaluate the current state of the art of the proposed quality models for web services, specifically: (1) which are these proposals and how are they related; (2) what are their structural characteristics; (3) what quality factors are the most and least addressed; and (4) what are their most consolidated definitions. Method: We have conducted a systematic mapping by defining a robust protocol that combines automatic and manual searches from different sources. We used a rigorous method to elicitate the keywords from the research questions and a selection criteria to retrieve the final papers to evaluate. We have adopted the ISO/IEC 25010 standard to articulate our analysis. Results: We have evaluated 47 different quality models from 65 papers that fulfilled the selection criteria. By analyzing in depth these quality models, we have: 1) distributed the proposals along the time dimension and identified their relationships; 2) analysed their size (visualizing the number of nodes and levels) and definition coverage (as indicator of quality of the proposals); 3) quantified the coverage of the different ISO/IEC 25010 quality factors by the proposals; 4) identified the quality factors that appeared in at least 30% of the surveyed proposals and provided the most consolidated definitions for them. Conclusions: We believe that this panoramic view on the anatomy of the quality models for web services may be a good reference for prospective researchers and practitioners in the field and especially may help avoiding the definition of new proposals that do not align with current research.

AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-, speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state-of-the-art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.

Software evolution ensures that software systems in use stay up to date and provide value for end-users. However, it is challenging for requirements engineers to continuously elicit needs for systems used by heterogeneous end-users who are out of organisational reach. Objective: We aim at supporting continuous requirements elicitation by combining user feedback and usage monitoring. Online feedback mechanisms enable end-users to remotely communicate problems, experiences, and opinions, while monitoring provides valuable information about runtime events. It is argued that bringing both information sources together can help requirements engineers to understand end-user needs better. Method/Tool: We present FAME, a framework for the combined and simultaneous collection of feedback and monitoring data in web and mobile contexts to support continuous requirements elicitation. In addition to a detailed discussion of our technical solution, we present the first evidence that FAME can be successfully introduced in real-world contexts. Therefore, we deployed FAME in a web application of a German small and medium-sized enterprise (SME) to collect user feedback and usage data. Results/Conclusion: Our results suggest that FAME not only can be successfully used in industrial environments but that bringing feedback and monitoring data together helps the SME to improve their understanding of end-user needs, ultimately supporting continuous requirements elicitation.

Context: Rapid software development (RSD) refers to the organizational capability to develop, release, and learn from software in rapid cycles without compromising its quality. To achieve RSD, it is essential to understand and manage software quality along the software lifecycle. Problem: Despite the numerous information sources related to product quality, there is a lack of mechanisms for supporting continuous quality management throughout the whole RSD process. Principal ideas/results: We propose Q-Rapids, a data-driven, qualityaware RSD methodology in which quality and functional requirements are managed together. Quality Requirements are incrementally elicited and refined based on data gathered at both development time and runtime. Project, development, and runtime data is aggregated into quality-related key indicators to support decision makers in steering future development cycles. Contributions: Q-Rapids aims to increase software quality through continuous data gathering and analysis, as well as continuous management of quality requirements.

Quality Requirements (QRs) are difficult to manage in agile software development. Given the pressure to deploy fast, quality concerns are often sacrificed for the sake of richer functionality. Besides, artefacts as user stories are not particularly well-suited for representing QRs. In this exploratory paper, we envisage a data-driven method, called Q-Rapids, to QR elicitation, assessment and documentation in agile software development. Q-Rapids proposes: 1) The collection and analysis of design and runtime data in order to raise quality alerts; 2) The suggestion of candidate QRs to address these alerts; 3) A strategic analysis of the impact of such requirements by visualizing their effect on a set of indicators rendered in a dashboard; 4) The documentation of the requirements (if finally accepted) in the backlog. The approach is illustrated with scenarios evaluated through a questionnaire by experts from a telecom company.

Cloud Computing enables the construction and the provisioning of virtualized service-based applications in a simple and cost effective outsourcing to dynamic service environments. Cloud Federations envisage a distributed, heterogeneous environment consisting of various cloud infrastructures by aggregating different IaaS provider capabilities coming from both the commercial and the academic area. In this paper, we introduce a federated cloud management solution that operates the federation through utilizing cloudbrokers for various IaaS providers. In order to enable an enhanced provider selection and inter-cloud service executions, an integrated monitoring approach is proposed which is capable of measuring the availability and reliability of the provisioned services in different providers. To this end, a minimal metric monitoring service has been designed and used together with a service monitoring solution to measure cloud performance. The transparent and cost effective operation on commercial clouds and the capability to simultaneously monitor both private and public clouds were the major design goals of this integrated cloud monitoring approach. Finally, the evaluation of our proposed solution is pre-