Using empirical studies to mitigate symbol overload in iStar extensions

UID/CEC/04516/2019Modelling languages are frequently extended to include new constructs to be used together with the original syntax. New constructs may be proposed by adding textual information, such as UML stereotypes, or by creating new graphical representations. Thus, these new symbols need to be expressive and proposed in a careful way to increase the extension’s adoption. A method to create symbols for the original constructs of a modelling language was proposed and has been used to create the symbols when a new modelling language is designed. We argue this method can be used to recommend new symbols for the extension’s constructs. However, it is necessary to make some adjustments since the new symbols will be used with the existing constructs of the modelling language original syntax. In this paper, we analyse the usage of this adapted method to propose symbols to mitigate the occurrence of overloaded symbols in the existing iStar extensions. We analysed the existing iStar extensions in an SLR and identified the occurrence of symbol overload among the existing constructs. We identified a set of fifteen overloaded symbols in existing iStar extensions. We used these concepts with symbol overload in a multi-stage experiment that involved users in the visual notation design process. The study involved 262 participants, and its results revealed that most of the new graphical representations were better than those proposed by the extensions, with regard to semantic transparency. Thus, the new representations can be used to mitigate this kind of conflict in iStar extensions. Our results suggest that next extension efforts should consider user-generated notation design techniques in order to increase the semantic transparency.authorsversionpublishe

An extension of iStar for Machine Learning requirements by following the PRISE methodology

The rise of Artificial Intelligence (AI) and Deep Learning has led to Machine Learning (ML) becoming a common practice in academia and enterprise. However, a successful ML project requires deep domain knowledge as well as expertise in a plethora of algorithms and data processing techniques. This leads to a stronger dependency and need for communication between developers and stakeholders where numerous requirements come into play. More specifically, in addition to functional requirements such as the output of the model (e.g. classification, clustering or regression), ML projects need to pay special attention to a number of non-functional and quality aspects particular to ML. These include explainability, noise robustness or equity among others. Failure to identify and consider these aspects will lead to inadequate algorithm selection and the failure of the project. In this sense, capturing ML requirements becomes critical. Unfortunately, there is currently an absence of ML requirements modeling approaches. Therefore, in this paper we present the first i* extension for capturing ML requirements and apply it to two real-world projects. Our study covers two main objectives for ML requirements: (i) allows domain experts to specify objectives and quality aspects to be met by the ML solution, and (ii) facilitates the selection and justification of the most adequate ML approaches. Our case studies show that our work enables better ML algorithm selection, preprocessing implementation tailored to each algorithm, and aids in identifying missing data. In addition, they also demonstrate the flexibility of our study to adapt to different domains.This work has been co-funded by the AETHER-UA project (PID2020-112540RB-C43), a smart data holistic approach for context-aware data analytics: smarter machine learning for business modeling and analytics, funded by the Spanish Ministry of Science and Innovation. And the BALLADEER (PROMETEO/2021/088) project, a Big Data analytical platform for the diagnosis and treatment of Attention Deficit Hyperactivity Disorder (ADHD) featuring extended reality, funded by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana). A. Reina-Reina (I-PI 13/20) hold Industrial PhD Grants co-funded by the University of Alicante and the Lucentia Lab Spin-off Company

Towards iStarML 2.0: Closing gaps from evolved requirements

iStarML is an XML-based format for enabling interoperability among i* tools. Its main design focus was to support data interchange even when involved tools implement different i* variants. In this paper we analyse required changes to the format from two main sources (i) the evolution of i* into a consistent and clear set of core concepts expressed in the new iStar 2.0 specification and (ii) recurrent necessities due to a wide use of i* modelling. In order to address these requirements, we propose new XML elements to be considered in a new version of iStarML: iStarML2.0Peer ReviewedPostprint (published version

Use of a i*extension for Machine Learning: a real case study

Capturing requirements in machine learning projects is a challenging task. It requires domain knowledge as well as experience in the machine learning field. The i* framework is a popular high abstraction-layer requirements capturing tool. However, the use of i* directly in the machine learning field (ML) is unfeasible due to it cannot capture all the restrictions and relationships of ML elements. In previous works we have extended i* to better capture machine learning requirements. In this paper, we apply the i* for machine learning extension to a real machine learning case study, in the context of a project focused on the diagnosis and treatment of Attention-Deficit/Hyperactivity Disorder (ADHD). The results show that the use of the i* for machine learning extension provides insights about the correct path to follow, aiding in the definition and selection of machine learning solutions that better fulfill the project requirements. Moreover, it facilitates faster development of the machine learning solution in a more structured way, avoiding errors and making the application of i* an effective tool for managing machine learning requirements

A Sustainability Catalogue for Software Modelling

Sustainable development is the development that meets the needs of the present without compromising the needs of our future generations. It covers five different dimensions: environmental, economic, social, technical, and individual. Such dimensions are also of interest for software. For example, memory and power efficiency have an impact on the environmental dimension, the reduction of costs in software development and evolution relates to the economic dimension, the use of software for general improvement of people’s lives affects the social dimension, the software’s ability to cooperate with other systems impacts the technical dimension, and the improvement of well-being of individuals relates to the individual dimension. These various dimensions and their properties impact on each other and on the base requirements of a system. Therefore, well-informed design decisions require improved support to reason on such intra- and inter-relationships and impacts, early in development. The objective of this dissertation is to propose a catalog of sustainability requirements for later reuse during the software development process. The envisioned solution involves using requirement engineering activities to address sustainability in the early stages of the software development. The first step towards a solution was to perform a (agile) systematic mapping study in order to gain a complete and profound knowledge about the existing sustainability and requirement engineering techniques. This study was the base of our work. Our final artifact is a sustainability catalogue. This catalogue addresses four out of the five dimensions of sustainability, as well as their qualities and relationships. We did not treat the individual dimension, for sake of simplicity and time constraints, although we consider that some of its properties are included in the social dimension. The catalogue was developed using the iStar framework, and it was implemented in the piStar Tool. Such catalogue offers a generic approach that can be instantiated for particular application domains, and for any combination of dimensions. Hence, this work will contribute to the field of sustainable software development

A goal-oriented method for FAIRification planning

The FAIR Principles provide guidance on how to improve the Findability, Accessibility, Interoperability, and Reusability of digital resources. Since the publication of the principles in 2016, several workflows have been proposed to support the process of making data FAIR (FAIRification). However, to respect the uniqueness of different communities, both the principles and the available workflows have been deliberately designed to remain agnostic in terms of standards, tools, and related implementation choices. Consequently, FAIRification needs to be properly planned in advance, and implementation details must be discussed with stakeholders and aligned with FAIRification objectives. To support this, this paper describes a method for identifying and refining FAIRification objectives. Leveraging on best practices and techniques from requirements and ontology engineering, the method aims at incrementally elaborating the most obvious aspects of the domain (e.g. the initial set of elements to be collected) into complex and comprehensive objectives. The definition of clear objectives enables stakeholders to communicate effectively and make informed implementation decisions, such as defining achievement criteria for distinct principles and identifying relevant metadata to be collected.</p

Agile Quality Requirements Elaboration:A Proposal and Evaluation

The increasing success and user satisfaction of agile methods’ application in their original context (eg small co-located teams), motivated large organizations to utilize agile methods to deal with the rapidly changing markets and the distributed global workforce. Several studies have reported a variety of quality requirements (QRs) challenges in large-scale distributed agile (LSDA) context, so a recent empirical study has identified 15 QRs challenges in LSDA projects. This paper proposes an approach based on the concept of goal documentation to deal with the 15 QRs challenges reported previously. Our proposal, the Agile Quality Requirements Elaboration (AQRE) approach, introduces a new organizational role and a two-step process to elaborate high-level goals (s) into epics and user stories alongside QRs. The fitness and the usefulness of AQRE are evaluated by using a focus group with eight practitioners in the IT department of a large Dutch government organization. The evaluation indicated that 12 of the 15 QRs challenges could be mitigated by the AQRE. Our main contribution is two-fold,(i) we proposed a solution approach to deal with QRs challenges in LSDA context, and (ii) our evaluation provided empirical evidence about its usefulness in realworld context

Challenges for Model-Driven Development of Strategically Aligned Information Systems

[EN] Model-Driven Development (MDD) has been proposed as an alternative to the traditional development of information systems, given its ability to integrate different stakeholders into the information system engineering process. Currently, longtime researched MDD methods and modern no-code and low-code platforms support the generation of the working code of the information system and services. However, in today's continuously changing environment, organisations need to align the information systems and services with the business structure, strategy, and processes they support. This article shows the design challenges for integrating business strategy information into a model-driven development method. We applied a set of mechanism experiments on an MDD method composed of three modelling frameworks with demonstrated semantic consistency, that covers the organisational, business process, and information system levels to identify information loss and transformation coverage issues that prevent the generation of information systems and services that are strategically aligned. The challenges were discussed with experts, confirming the relevance of avoiding the overlapping between the strategic and business process concepts, providing organisational-level constructs to express strategic ends and means, and considering the organisational structure in the modular design of business process and information systems and services.This work was supported in part by the Spanish State Research Agency and the Generalitat Valenciana under Project MICIN/AEI/10.13039/501100011033, Project GV/2021/072, and Project INNEST/2021/57 by Agencia Valenciana de Innovacion (AVI); in part by the European Regional Development Fund (ERDF), the European Union Next Generation, and Plan de Recuperacion, Transformacion y Resiliencia (PRTR); and in part by the National Agency for Research and Development (ANID)/Scholarship Program/Doctorado Becas Chile under Grant 2020-72210494.Noel-Lopez, R.; Panach, JI.; Pastor López, O. (2022). Challenges for Model-Driven Development of Strategically Aligned Information Systems. IEEE Access. 10:38237-38253. https://doi.org/10.1109/ACCESS.2022.316222538237382531

Formalizing enrichment mechanisms for bibliographic ontologies in the Semantic Web

This paper presents an analysis of current limitations to the reuse of bibliographic data in the Semantic Web and a research proposal towards solutions to overcome them. The limitations identified derive from the insufficient convergence between existing bibliographic ontologies and the principles and techniques of linked open data (LOD); lack of a common conceptual framework for a diversity of standards often used together; reduced use of links to external vocabularies and absence of Semantic Web mechanisms to formalize relationships between vocabularies, as well as limitations of Semantic Web languages for the requirements of bibliographic data interoperability. A proposal is advanced to investigate the hypothesis of creating a reference model and specifying a superontology to overcome the misalignments found, as well as the use of SHACL (Shapes Constraint Language) to solve current limitations of RDF languages.info:eu-repo/semantics/acceptedVersio