Silverio Martínez-Fernández

Practical experiences in designing and conducting empirical studies in industry-academia collaboration

More and more, software engineering researchers are motivated to solve real problems that bring value to industry. An example is the industry-academia collaboration described in this paper among everis, an IT consulting firm, and the GESSI research group at UPC. The goal of this paper is twofold: to evaluate the success of the collaboration, and to report the experience with conducting empirical studies in the industry and lessons learnt. We evaluated our collaboration with an existing model for technology transfer, and performed a focus group discussion to identify challenges we have faced. After initialization and alignment of the collaboration, a high maturity level has been achieved: we have obtained the first results in form of proposed solutions, scientific publications, and pilots run in real projects. In spite of this positive progress, further initiatives need to be undertaken in the last phases of the collaboration to achieve high degrees of maturity in deployment impact, industry benefit and innovativeness. Evaluating the collaboration has been positive, since we identified the next steps to be taken to achieve a high degree of technology transfer and innovation dissemination. We think it is a needed step in industry-academia collaborations in order to improve their success.

Benefits and drawbacks of reference architectures

Reference architectures (RA) have been studied to create a consistent notion of what constitutes them as well as their benefits and drawbacks. However, few empirical studies have been conducted to provide evidence that support the claims made. To increase this evidence, this paper investigates the actual industrial practice of using RAs. The study consists of a survey with 28 stakeholders from everis, a multinational consulting company based in Spain. We report the findings and contextualize them with previous research.

REARM: A Reuse-Based Economic Model for Software Reference Architectures

To remain competitive, organizations are challenged to make informed and feasible value-driven design decisions in order to ensure the quality of their software systems. However, there is a lack of support for evaluating the economic impact of these decisions with regard to software reference architectures. This damages the communication among architects and management, which can result in poor decisions. This paper aims at ameliorating this problem by presenting a pragmatic preliminary economic model to perform cost-benefit analysis on the adoption of software reference architectures as a key asset for optimizing architectural decision-making. The model is based on existing value-based metrics and economics-driven models used in other areas. A preliminary validation based on a retrospective study showed the ability of the model to support a cost-benefit analysis presented to the management of an IT consulting company. This validation involved a cost-benefit analysis related to reuse and maintenance; other qualities will be integrated as our research progresses.

Artifacts of software reference architectures: a case study

Context: Software reference architectures (SRA) have emerged as an approach to systematically reuse architectural knowledge and software elements in the development of software systems. Over the last years, research has been conducted to uncover the artifacts that SRAs provide in order to build software systems. However, empirical studies have not focused on providing industrial evidence about such artifacts. Aim: This paper investigates which artifacts constitute an SRA, how SRAs are designed, the potential reuse of SRAs artifacts, and how they are used in practice. Method: The study consists of a case study made in collaboration with a multinational consulting company that designs SRAs for diverse client organizations. A total of nine European client organizations that use an SRA participated in the study. We analyzed available documentation and contacted 28 practitioners. Results: In the nine analyzed projects, we observed that the artifacts that constitute an SRA are mainly software elements, guidelines and documentation. The design and implementation of SRAs are influenced by the reuse of artifacts from previous software system development and experiences, and the reuse of an SRA across different business domains may be possible when they are platform-oriented. Regarding SRAs usage, we observed that conformance checking is seldom performed. Conclusions: This study reports artifacts of SRAs as stated by practitioners in order to help software architects and scientists in the inception, design, and application of SRAs.

QuPreSS: A service-oriented framework for predictive services quality assessment

Nowadays there are lots of predictive services for several domains such as stock market and bookmakers. The value delivered by these services relies on the quality of their predictions. This paper presents QuPreSS, a general framework which measures predictive service quality and guides the selection of the most accurate predictive service. To do so, services are monitored and their predictions are compared over time by means of forecast verification with observations. A systematic literature review was performed to design a service-oriented framework architecture that fits into the current body of knowledge. The service-oriented nature of the framework makes it extensible and interoperable, being able to integrate existing services regardless their heterogeneity of platforms and languages. Finally, we also present an instantiation of the generic framework architecture for the weather forecast domain, freely available at http://gessi.lsi.upc. edu/qupress/.

Data-Driven Requirements Engineering in Agile Projects: The Q-Rapids Approach

Requirements identification, specification and management are key activities in the software development process. In the last years, many approaches to these activities have emerged, based on the exploitation of huge amounts of data gathered from software repositories and system usage. The Q-Rapids project proposes the collection and analysis of such data and its consolidation into a set of strategic indicators as product quality, time to market and team productivity. These indicators are visualized through a dashboard designed to support decision-makers. In this paper, we present the ongoing research undertaken in this project. We use the concept of blocking situation to exemplify the Q-Rapids approach.

Towards guidelines for building a business case and gathering evidence of software reference architectures in industry

BackgroundSoftware reference architectures are becoming widely adopted by organizations that need to support the design and maintenance of software applications of a shared domain. For organizations that plan to adopt this architecture-centric approach, it becomes fundamental to know the return on investment and to understand how software reference architectures are designed, maintained, and used. Unfortunately, there is little evidence-based support to help organizations with these challenges.MethodsWe have conducted action research in an industry-academia collaboration between the GESSI research group and everis, a multinational IT consulting firm based in Spain.ResultsThe results from such collaboration are being packaged in order to create guidelines that could be used in similar contexts as the one of everis. The main result of this paper is the construction of empirically-grounded guidelines that support organizations to decide on the adoption of software reference architectures and to gather evidence to improve RA-related practices.ConclusionsThe created guidelines could be used by other organizations outside of our industry-academia collaboration. With this goal in mind, we describe the guidelines in detail for their use.

Towards an ontology for strategic decision making: The case of quality in rapid software development projects

Strategic decision making is the process of selecting a logical and informed choice from the alternative options based on key strategic indicators determining the success of a specific organization strategy. To support this process and provide a common underlying language, in this work, we present an empirically-grounded ontology to support different strategic decision-making processes and extend the ontology to cover the context of managing quality in Rapid Software Development projects. We illustrate the complete ontology with an example.

Q-Rapids Tool Prototype: Supporting Decision-Makers in Managing Quality in Rapid Software Development

Software quality is an essential competitive factor for the success of software companies today. Increasing the software quality levels of software products and services requires an adequate integration of quality requirements (QRs) in the software life-cycle, which is still scarcely supported in current rapid software development (RSD) approaches. One of the goals of the Q-Rapids (Quality-aware Rapid Software Development) method is providing tool support to decision-makers for QR management in RSD. The Q-Rapids method is based on gathering data from several and heterogeneous sources, to be aggregated into quality-related strategic indicators (e.g., customer satisfaction, product quality) and presented to decision-makers using a highly informative dashboard. The current release of Q-Rapids Tool provides four sets of functionality: (1) data gathering from source tools (e.g. GitLab, Jira, SonarQube, and Jenkins), (2) aggregation of data into three levels of abstraction (metrics, product/process factors, and strategic indicators), (3) visualization of the aggregated data, and (4) navigation through the aggregated data. The tool has been evaluated by four European companies that follow RSD processes.

Quality-aware Architectural Model Transformations in Adaptive Mashups User Interfaces

The final publication is available at IOS Press through http://dx.doi.org/10.3233/FI-2016-0000