The opportunities of open data have been recently recognized among companies in different domains. Digital service providers have increasingly been interested in the possibilities of innovating new ideas and services around open data. Digital service ecosystems provide several advantages for service developers, enabling the service co-innovation and co-creation among ecosystem members utilizing and sharing common assets and knowledge. The utilization of open data in digital services requires new innovation practices, service development models, and a collaboration environment. These can be provided by the ecosystem. However, since open data can be almost anything and originate from different kinds of data sources, the quality of data becomes the key issue. The new challenge for service providers is how to guarantee the quality of open data. In the ecosystems, uncertain data quality poses major challenges. The main contribution of this paper is the concept of the Evolvable Open Data based digital service Ecosystem (EODE), which defines the kinds of knowledge and services that are required for validating open data in digital service ecosystems. Thus, the EODE provides business potential for open data and digital service providers, as well as other actors around open data. The ecosystem capability model, knowledge management models, and the taxonomy of services to support the open data quality certification are described. Data quality certification confirms that the open data is trustworthy and its quality is good enough to be accepted for the usage of the ecosystem’s services. The five-phase open data quality certification process, according to which open data is brought to the ecosystem and certified for the usage of the digital service ecosystem members using the knowledge models and support services of the ecosystem, is also described. The initial experiences of the still ongoing validation steps are summarized, and the concept limitations and future development targets are identified.

Service engineering of digital service ecosystems can be associated with several challenges, such as change and evolution of requirements; gathering of quality requirements and assessment; and uncertainty caused by dynamic nature and unknown deployment environment, composition and users. Therefore, the complexity and dynamics in which these digital services are deployed call for solutions to make them autonomic . Until now there has been no up-to-date review of the scientific literature on the application of the autonomic computing initiative in the digital service ecosystems domain. This article presents a review and comparison of autonomic computing methods in digital service ecosystems from the perspective of service engineering, i.e., requirements engineering and architecting of services. The review is based on systematic queries in four leading scientific databases and Google Scholar, and it is organized in four thematic research areas. A comparison framework has been defined which can be used as a guide for comparing the different methods selected. The goal is to discover which methods are suitable for the service engineering of digital service ecosystems with autonomic computing capabilities, highlight what the shortcomings of the methods are, and identify which research activities need to be conducted in order to overcome these shortcomings. The comparison reveals that none of the existing methods entirely fulfills the requirements that are defined in the comparison framework.

A digital service ecosystem enables value creation and the co-development of services in a value network under a common ecosystem regulation. The ecosystem members are able to focus on their core competences and can strengthen their forces by co-operating; yet remaining able to act independently. However, due to regulated environment, the ecosystem elements—i.e. ecosystem members, capabilities, infrastructure and the existing ecosystem assets—have an influence on digital service engineering, especially in the service requirements engineering phase. The main contribution of this paper is to describe how to specify the requirements of digital services in a digital service ecosystem. To this aim, this paper introduces the basic definitions and elements of the digital service ecosystem, and a scenario-based service requirement engineering (RE) method for the digital service ecosystem. A practical example is given to illustrate the use of the RE method. The collected feedback from the RE method users highlights the user experiences on the advantages and limitations of the proposed method.

This paper describes our joint experience in organizing a Doctoral Symposium co-located with one of the main software engineering conferences. It presents the issues we addressed during the organization of the symposium, as well as during the symposium. This paper is addressed (1) to the organizers of Doctoral Symposiums aiming to provide a recipe with the main ingredients and preparation steps and their related significance in the entire organization process, as well as (2) to the PhD students providing our feedback on the expectations, evaluation, and presentation of their contributions.

End-user programming produces applications that can produce and/or consume data. An end-user can be a software enthusiast or non-programmer. In this paper end-users are understood to be non-programmers that are interested in creating applications for their personal needs and daily tasks. An interesting research question is how the input and output data of end-users’ applications should be represented? What kind of a data model is needed for this data? And how this input and output data can be utilised? Firstly, the data model should be designed for end-users, that is, it is required that the data model is easy to comprehend and utilise by non-programmers. Secondly, the data model should be suitable for SW professionals that make functionalities available for end-user programming. Thirdly, the data model should be designed so that it is possible to provide reusable post-processing components for input/output data represented via this model. This paper discusses these three research questions and outlines a data model, called the Tiles4Data data model that is designed for the above requirements.

Smart Spaces embedded into our surroundings are dynamic in nature; variable types and amount of services and applications are provided by a set of interoperable devices and systems that are joining and leaving a space every now and then. Thus, the structure, functionality and quality properties of the space are changing at every turn. In order to understand, what devices/systems there are in the space, what applications and services the space provides and what is the quality level of these services, application developers and smart space users need to have an assistant that helps them in figuring out these things. On this account, we use a visualization tool IQVis for illustrating how the run-time properties of a smart space are changing in time. Application developers can use the tool for seeing how an application behaves in a real situation. A user of a smart space can use the quality information provided by the IQVis tool, e.g., on the screen of his/her mobile phone, for adapting his/her profile/actions. The use of IQVis will be exemplified by a smart greenhouse scenario, a case study implemented in laboratory settings.

The design of applications that may benefit from the data gathered from the surrounding physical environment represents a challenging and promising research field. Because of the heterogeneity of such systems interoperability is becoming more difficult to achieve at the service, information and device levels. In this paper, we report on the first attempt to design an interoperability platform by integrating a “Semantic Web” information sharing infrastructure, namely Smart-M3 (Multi-part, Multi-device and Multi-vendor), within the OSGi (Open Service Gateway initiative) framework, enhancing the information level interoperability of the former with the service level interoperability of the latter. Such an interoperability platform can grant the unanticipated evolution of existing Information and Communication Technology (ICT) systems by providing a generic and simple solution to develop what are generally called “Smart Space Applications”. As proof of concept a maintenance scenario will be discussed and detailed.

Any of our living environments can form a smart space that provides services and applications for the users according to their situations, the computing and communication facilities of the environment and the surroundings of the user. The ability to take into account the context of the user, and the digital and physical environment makes a space smart. This paper introduces a set of new capabilities that are required for achieving context-awareness in smart spaces. First, sensor data from the environment is acquired and aggregated. Second, the context data is interpreted and represented and possibly enhanced with external information. Thereafter, context reasoning is possible. Finally, situation based context reasoning is made by application agents that exploit domain ontologies for tuning context information to the situation in hand. The use of the introduced context-awareness concept is exemplified by an emergency scenario of a smart city.

Model-driven development (MDD) aims to increase productivity in software development and manage the complexity of software by utilizing modelling to shift from programming in solution-space to modelling in problem-space. Another approach to increase productivity and experience significant cost savings is to utilize open source (OS) components in software development. This paper presents the state of the art of how to combine MDD methods with OS software development. The approach is called open models software development which is expected to combine the benefits of both worlds. The fundamental idea of open models development is to apply community-driven collaborative MDD. In this paper, open models development is exemplified by constructing resource availability service (RAS). RAS is a web service for providing availability information about resources such as services, content, users and terminals. This paper shows that Open Models development is feasible from technical point of view although there are still great challenges in tooling. In addition, more empirical research is needed in studying attractiveness of open models.

Reliability-aware software architecture development has recently been gaining growing attention among software architects. This paper tackles the issue by introducing an ontology-based approach, called OntoArch, which is characterized by: 1) integration of software reliability engineering and software architecture design; 2) targeting reliability-aware software architecture development; and 3) OntoArch ontology in the context of software architecture design and software reliability engineering. The OntoArch approach is validated by applying the OntoArch approach to the development of a PIR (Personal Information Repository) system architecture.

The Quality-driven Architecture Design and quality Analysis (QADA ) methodology uses quality requirements as a driving force when selecting software structures. In QADA, the family architecture contains qualitative quality requirements as architectural style(s) and patterns and the quantitative quality requirements as the properties of individual architectural elements. Our tool chain covers all the phases of QADA and contributes to software family engineering by providing methods to select an appropriate approach for family architecture (Stylebase), to capture and map requirements to the architecture (Protégé, QPE, TOPCASED), to evaluate the maturity and quality of the architecture (Stylebase, RAP), a technique to represent variation points in the family architecture (QPE, TOPCASED), and a testing approach that can test final software components, ensure the achieved quality level, and produce feedback for the architect (ComponentBee). These tools work under the Eclipse platform and utilise free open source components.

Service composition enables service users to develop Web applications by composing services via Internet. Traditional service description is function-centred, but lack of composition information, which hinders service composition with respect to service contexts. In this paper, we examine service composition information (i.e. service contexts and service dependency). The context- aware service management platform that we developed provides support for service users to compose services by managing service dependency.

Current service-oriented architecture (SOA) focuses on service composition for application development, i.e. application composition, which is perceived as volatile (i.e. "compose one time and use one time."). This paper focuses on service composition information (i.e. service dependency) management and dependency-aware service composition for service development (i.e. service composition). This paper explores service composition and service dependency by proposing an extended SOA model, including: 1) establishing a dependency-aware service-oriented architecture (DSOA) that specifies dependency-aware service interactions, i.e. service publication, discovery, composition and binding; 2) developing an upper service dependency ontology for non-volatile DSOA service composition; and 3) demonstrating the validation of DSOA service composition by implementing a DSOA service manager.

Quality-aware service delivery has been receiving increasing attention in both software architecture and service management. Our approach values software and service quality assurance, ranging from quality assessment in software architecting to quality matching in service discovery. This paper proposes an integrated QoS-aware service management method, which examines the 'service as a software' development and the 'software as a service' management against QoS requirements. Moreover, we design an integrated QoS-aware service management infrastructure which promises complete software and service workflow management coupled with a QoS ontology development. We develop the QoS property ontology from the viewpoints of technical quality and managerial quality information management.

Semantic Web services integrate the meaningful content of the semantic Web with the business logic of Web services and thus enable industries and individuals to build, access, deploy and execute services and transactions independently over Internet. This paper surveys semantic Web services from the viewpoints of Web service architectures, service engineering, service description languages, Web service building tools, and also presents a case study

Semantic QoS aware Web services incorporating the emerging Web services in the QoS aware system development are promoting service oriented software engineering (SOSE). To identify the steps toward semantic quality of service (QoS) aware Web services, this paper examines previous studies related to semantic QoS aware Web services, including QoS aware Web service architectures, QoS classification, QoS ontology, QoS specification languages, and Web service creation tools. Moreover, a case study is presented to discuss the gaps between our current quality driven software development approach and the semantic QoS aware Web services

Abstract A base station, as a network element, has become an increasingly software‐intensive system. Digital signal processing (DSP) software is hard real‐time software that is a part of the software system needed in a base station. This article reports practical experiences related to organizing the development of embedded software in the telecommunication industry, at Nokia Networks. The article introduces the main factors influencing the development of DSP software and also compares the evolutionary process under study with both selected organizational models for a software product line and a multistage model for the software life cycle. We believe it is vitally important to formulate the organization according to the software architecture, and it is essential to have a dedicated development organization with long‐term responsibility for the software. History shows that without long‐term responsibility, there is no software reuse. In this paper we introduce a new organizational model for product line development. This new hybrid model clarifies long‐term responsibilities in large software organizations with hundreds of staff members and formulates the organization according to the software architecture. Our case needs a couple more constraints to keep it in the evolution stage of the software life cycle. Thus, we extend the evolution phase in the multistage model to make it relevant for embedded, hard real‐time software. Copyright © 2006 John Wiley & Sons, Ltd.

Wireless services are software-based services that exploit distribution infrastructure embedded in our everyday life as various communication and computing technologies. Service architecture defines concepts and principles to develop and maintain services to obtain the quality issues with minimum cost and faster time-to-market. In order to boost the development of wireless services, more effective means of using existing architectural know-how and artifacts are required. Our contribution is the architectural knowledge base that introduces three cornerstones: the service taxonomy, reference service architecture, and basic services that alt together provide an efficient means of creating added value with wireless services. The service taxonomy assists in identifying the required functional and quality properties of services and the constraints of the underlying technology platforms. The reference architecture realizes the required properties, based on a selected set of architectural styles and patterns, and provides a skeleton upon which a new end-user service can be developed faster and more easily by using partially ready-made solutions, and furthermore, to keep the architectural knowledge base evolving at the same time. The architectural knowledge base has been validated in several research projects with industrial companies.

Ubiquitous computing embeds computer technology in our everyday environment, providing a human with information services and applications through any device over different kinds of networks. Ubiquitous computing can be seen as a prerequisite for pervasive computing that emphasizes mobile data access and the mechanisms needed for supporting a community of nomadic users. Ubiquitous software is the software required in ubiquitous computing environments. This paper surveys the challenges and state-of-the-art software technologies applicable to ubiquitous computing environments. Ubiquitous wireless world systems trigger a set of requirements, e.g. interoperability, adaptability and mobility, for ubiquitous system and software technologies. The main challenges of ubiquitous software are a uniform and adaptive middleware technology, iteroperability of services and networks, and the enabling technologies required in their development. Futhermore guaranteeing secure transactions between service providers, content providers and users is essential in worldwide pervasive computing environments. Although standards, reference architectures and generic software technologies provide the basis for future ubiquitous software development, new kinds of micro architectures and software technologies, and development methods are needed.

The purpose of the architecture evaluation of a software system is to analyze the architecture to identify potential risks and to verify that the quality requirements have been addressed in the design. This survey shows the state of the research at this moment, in this domain, by presenting and discussing eight of the most representative architecture analysis methods. The selection of the studied methods tries to cover as many particular views of objective reflections as possible to be derived from the general goal. The role of the discussion is to offer guidelines related to the use of the most suitable method for an architecture assessment process. We will concentrate on discovering similarities and differences between these eight available methods by making classifications, comparisons and appropriateness studies.