Seamless Variability Management With the Virtual Platform

Customization is a general trend in software engineering, demanding systems that support variable stakeholder requirements. Two opposing strategies are commonly used to create variants: software clone & own and software configuration with an integrated platform. Organizations often start with the former, which is cheap, agile, and supports quick innovation, but does not scale. The latter scales by establishing an integrated platform that shares software assets between variants, but requires high up-front investments or risky migration processes. So, could we have a method that allows an easy transition or even combine the benefits of both strategies? We propose a method and tool that supports a truly incremental development of variant-rich systems, exploiting a spectrum between both opposing strategies. We design, formalize, and prototype the variability-management framework virtual platform. It bridges clone & own and platform-oriented development. Relying on programming-language-independent conceptual structures representing software assets, it offers operators for engineering and evolving a system, comprising: traditional, asset-oriented operators and novel, feature-oriented operators for incrementally adopting concepts of an integrated platform. The operators record meta-data that is exploited by other operators to support the transition. Among others, they eliminate expensive feature-location effort or the need to trace clones. Our evaluation simulates the evolution of a real-world, clone-based system, measuring its costs and benefits.Comment: 13 pages, 10 figures; accepted for publication at the 43rd International Conference on Software Engineering (ICSE 2021), main technical trac

Consistent View-Based Management of Variability in Space and Time

Systeme entwickeln sich schnell weiter und existieren in verschiedenen Variationen, um unterschiedliche und sich ändernde Anforderungen erfüllen zu können. Das führt zu aufeinanderfolgenden Revisionen (Variabilität in Zeit) und zeitgleich existierenden Produktvarianten (Variabilität in Raum). Redundanzen und Abhängigkeiten zwischen unterschiedlichen Produkten über mehrere Revisionen hinweg sowie heterogene Typen von Artefakten führen schnell zu Inkonsistenzen während der Evolution eines variablen Systems. Die Bewältigung der Komplexität sowie eine einheitliche und konsistente Verwaltung beider Variabilitätsdimensionen sind wesentliche Herausforderungen, um große und langlebige Systeme erfolgreich entwickeln zu können. Variabilität in Raum wird primär in der Softwareproduktlinienentwicklung betrachtet, während Variabilität in Zeit im Softwarekonfigurationsmanagement untersucht wird. Konsistenzerhaltung zwischen heterogenen Artefakttypen und sichtbasierte Softwareentwicklung sind zentrale Forschungsthemen in modellgetriebener Softwareentwicklung. Die Isolation der drei angrenzenden Disziplinen hat zu einer Vielzahl von Ansätzen und Werkzeugen aus den unterschiedlichen Bereichen geführt, was die Definition eines gemeinsamen Verständnisses erschwert und die Gefahr redundanter Forschung und Entwicklung birgt. Werkzeuge aus den verschiedenen Disziplinen sind oftmals nicht ausreichend integriert und führen zu einer heterogenen Werkzeuglandschaft sowie hohem manuellen Aufwand während der Evolution eines variablen Systems, was wiederum der Systemqualität schadet und zu höheren Wartungskosten führt. Basierend auf dem aktuellen Stand der Forschung in den genannten Disziplinen werden in dieser Dissertation drei Kernbeiträge vorgestellt, um den Umgang mit der Komplexität während der Evolution variabler Systeme zu unterstützten. Das unifizierte konzeptionelle Modell dokumentiert und unifiziert Konzepte und Relationen für den gleichzeitigen Umgang mit Variabilität in Raum und Zeit basierend auf einer Vielzahl ausgewählter Ansätze und Werkzeuge aus der Softwareproduktlinienentwicklung und dem Softwarekonfigurationsmanagement. Über die bloße Kombination vorhandener Konzepte hinaus beschreibt das unifizierte konzeptionelle Modell neue Möglichkeiten, beide Variabilitätsdimensionen zueinander in Beziehung zu setzen. Die unifizierten Operationen verwenden das unifizierte konzeptionelle Modell als Datenstruktur und stellen die Basis für operative Verwaltung von Variabilität in Raum und Zeit dar. Die unifizierten Operationen werden basierend auf einer Analyse diverser Ansätze konzipiert, welche verschiedene Modalitäten und Paradigmen verfolgen. Während die unifizierten Operationen die Funktionalität von analysierten Werkzeugen abdecken, ermöglichen sie den gleichzeitigen Umgang mit beiden Variabilitätsdimensionen. Der unifizierte Ansatz basiert auf den vorhergehenden Beiträgen und erweitert diese um Konsistenzerhaltung. Zu diesem Zweck wurden Typen von variabilitätsspezifischen Inkonsistenzen identifiziert, die während der Evolution variabler heterogener Systeme auftreten können. Der unifizierte Ansatz ermöglicht automatisierte Konsistenzerhaltung für eine ausgewählte Teilmenge der identifizierten Inkonsistenztypen. Jeder Kernbeitrag wurde empirisch evaluiert. Zur Evaluierung des unifizierten konzeptionellen Modells und der unifizierten Operationen wurden Expertenbefragungen durchgeführt, Metriken zur Bewertung der Angemessenheit einer Unifizierung definiert und angewendet, sowie beispielhafte Anwendungen demonstriert. Die funktionale Eignung des unifizierten Ansatzes wurde mittels zweier Realweltfallstudien evaluiert: Die häufig verwendete ArgoUML-SPL, die auf ArgoUML basiert, einem UML-Modellierungswerkzeug, sowie MobileMedia, eine mobile Applikation für Medienverwaltung. Der unifizierte Ansatz ist mit dem Eclipse Modeling Framework (EMF) und dem Vitruvius Ansatz implementiert. Die Kernbeiträge dieser Arbeit erweitern das vorhandene Wissen hinsichtlich der uniformen Verwaltung von Variabilität in Raum und Zeit und verbinden diese mit automatisierter Konsistenzerhaltung für variable Systeme bestehend aus heterogenen Artefakttypen

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

Consistent View-Based Management of Variability in Space and Time

Developing variable systems faces many challenges. Dependencies between interrelated artifacts within a product variant, such as code or diagrams, across product variants and across their revisions quickly lead to inconsistencies during evolution. This work provides a unification of common concepts and operations for variability management, identifies variability-related inconsistencies and presents an approach for view-based consistency preservation of variable systems

Towards Automatic Parsing of Structured Visual Content through the Use of Synthetic Data

Structured Visual Content (SVC) such as graphs, flow charts, or the like are used by authors to illustrate various concepts. While such depictions allow the average reader to better understand the contents, images containing SVCs are typically not machine-readable. This, in turn, not only hinders automated knowledge aggregation, but also the perception of displayed in-formation for visually impaired people. In this work, we propose a synthetic dataset, containing SVCs in the form of images as well as ground truths. We show the usage of this dataset by an application that automatically extracts a graph representation from an SVC image. This is done by training a model via common supervised learning methods. As there currently exist no large-scale public datasets for the detailed analysis of SVC, we propose the Synthetic SVC (SSVC) dataset comprising 12,000 images with respective bounding box annotations and detailed graph representations. Our dataset enables the development of strong models for the interpretation of SVCs while skipping the time-consuming dense data annotation. We evaluate our model on both synthetic and manually annotated data and show the transferability of synthetic to real via various metrics, given the presented application. Here, we evaluate that this proof of concept is possible to some extend and lay down a solid baseline for this task. We discuss the limitations of our approach for further improvements. Our utilized metrics can be used as a tool for future comparisons in this domain. To enable further research on this task, the dataset is publicly available at https://bit.ly/3jN1pJ

A conceptual model for unifying variability in space and time: Rationale, validation, and illustrative applications

With the increasing demand for customized systems and rapidly evolving technology, software engineering faces many challenges. A particular challenge is the development and maintenance of systems that are highly variable both in space (concurrent variations of the system at one point in time) and time (sequential variations of the system, due to its evolution). Recent research aims to address this challenge by managing variability in space and time simultaneously. However, this research originates from two different areas, software product line engineering and software configuration management, resulting in non-uniform terminologies and a varying understanding of concepts. These problems hamper the communication and understanding of involved concepts, as well as the development of techniques that unify variability in space and time. To tackle these problems, we performed an iterative, expert-driven analysis of existing tools from both research areas to derive a conceptual model that integrates and unifies concepts of both dimensions of variability. In this article, we first explain the construction process and present the resulting conceptual model. We validate the model and discuss its coverage and granularity with respect to established concepts of variability in space and time. Furthermore, we perform a formal concept analysis to discuss the commonalities and differences among the tools we considered. Finally, we show illustrative applications to explain how the conceptual model can be used in practice to derive conforming tools. The conceptual model unifies concepts and relations used in software product line engineering and software configuration management, provides a unified terminology and common ground for researchers and developers for comparing their works, clarifies communication, and prevents redundant developments

Consistent View-Based Management of Variability in Space and Time

Developing variable systems faces many challenges. Dependencies between interrelated artifacts within a product variant, such as code or diagrams, across product variants and across their revisions quickly lead to inconsistencies during evolution. This work provides a unification of common concepts and operations for variability management, identifies variability-related inconsistencies and presents an approach for view-based consistency preservation of variable systems

Proceedings of the ECCS 2005 satellite workshop: embracing complexity in design - Paris 17 November 2005

Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr). Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr)