1,721,242 research outputs found
Towards interoperability of i* models using iStarML
No full textGoal-oriented and agent-oriented modelling provides an effective approach to the understanding of distributed information systems that need to operate in open, heterogeneous and evolving environments. Frameworks, firstly introduced more than ten years ago, have been extended along language variants, analysis methods and CASE tools, posing language semantics and tool interoperability issues. Among them, the i* framework is one the most widespread. We focus on i*-based modelling languages and tools and on the problem of supporting model exchange between them. In this paper, we introduce the i* interoperability problem and derive an XML interchange format, called iStarML, as a practical solution to this problem. We first discuss the main requirements for its definition, then we characterise the core concepts of i* and we detail the tags and options of the interchange format. We complete the presentation of iStarML showing some possible applications. Finally, a survey on the i* community perception about iStarML is included for assessment purposes
iStarML: An XML-based Model Interchange Format for i*
No full textThere are several tools currently available in the i* community with different purposes. This situation poses both benefits and difficulties. Benefits, because different groups may be able to share their models and results among their tools, and even connect different tools in order to perform complex processes. Difficulties, because most of these tools differ either in the underlying metamodel of the language, or the format in which they store the models, or in both. To overcome the difficulties and exploit the benefits, we have defined the iStarML model interchange format as a practical solution to this problem. In this paper we present the research line which supports this outcome. We present its motivation, objectives and current outcomes, the expected contributions and finally our on going and future work
Preface
Full text linkThis CEUR volume contains the research proposals accepted for presentation at the 13th International
Doctoral Symposium on Empirical Software Engineering (IDoESE 2015), held in Beijing, China, the 21st
of October 2015, as an event integrated in the Empirical Software Engineering International Week
(ESEIW), which remarkably included the world-leading Empirical Software Engineering and
Measurement conference (ESEM 2015).
The objective of the doctoral symposium is to provide junior researchers with the opportunity to present
their work to the empirical software engineering community and receive valuable feedback from
experienced researchers in that community. The symposium also aims at facilitating the exchange of ideas
among young researchers. To do so, experienced members of the empirical software engineering
community serve as symposium advisors and provide feedback to students presenting their work
Adoption of Free Libre Open Source Software (FLOSS): A Risk Management Perspective2014 IEEE 38th Annual Computer Software and Applications Conference
No full textFree Libre Open Source Software (FLOSS) has become a strategic asset in software development, and open source communities behind FLOSS are a key player in the field. The analysis of open source community dynamics is a key capability in risk management practices focused on the integration of FLOSS in all types of organizations. We are conducting research in developing methodologies for managing risks of FLOSS adoption and deployment in various application domains. This paper is about the ability to systematically capture, filter, analyze, reason about, and build theories upon, the behavior of an open source community in combination with the structured elicitation of expert opinions on potential organizational business risk. The novel methodology presented here blends together qualitative and quantitative information as part of a wider analytics platform. The approach combines big data analytics with automatic scripting of scenarios that permits experts to assess risk indicators and business risks in focused tactical and strategic workshops. These workshops generate data that is used to construct Bayesian networks that map data from community risk drivers into statistical distributions that are feeding the platform risk management dashboard. A special feature of this model is that the dynamics of an open source community are tracked using social network metrics that capture the structure of unstructured chat data. The method is illustrated with a running example based on experience gained in implementing our approach in an academic smart environment setting including Moodbile, a Mobile Learning for Moodle (www.moodbile.org). This example is the first in a series of planned experiences in the domain of smart environments with the ultimate goal of deriving a complete risk model in that field
Mobile technologies to enable users' informed decisions
Full text linkThe significant wide impact of mobile technologies (e.g., smartphones, tablets) and the difficulty of mastering their complexity (due to factors like constant emergence and evolution) pose new challenges to many (if not all) software engineering disciplines. We particularly see these challenges when thinking of average citizens that carry out their daily activities in smart environments where mobile technologies and sensors installed provide many potential advantages to support them. Applications that could enable informed decision-making are currently beyond what software developers can provide. This position paper discusses challenges, and highlights current approaches available in order to support decision-making for thoughtful living. We present an initial version of a comprehensive framework to overcome the challenges identified and analyse which software engineering research lines may help to implement it. A motivating scenario is used to conduct the discussion
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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