Combining Multiple Granularity Variability in a Software Product Line Approach for Web Engineering

[Abstract] Context: Web engineering involves managing a high diversity of artifacts implemented in different languages and with different levels of granularity. Technological companies usually implement variable artifacts of Software Product Lines (SPLs) using annotations, being reluctant to adopt hybrid, often complex, approaches combining composition and annotations despite their benefits. Objective: This paper proposes a combined approach to support fine and coarse-grained variability for web artifacts. The proposal allows web developers to continue using annotations to handle fine-grained variability for those artifacts whose variability is very difficult to implement with a composition-based approach, but obtaining the advantages of the composition-based approach for the coarse-grained variable artifacts. Methods: A combined approach based on feature modeling that integrates annotations into a generic composition-based approach. We propose the definition of compositional and annotative variation points with custom-defined semantics, which is resolved by a scaffolding-based derivation engine. The approach is evaluated on a real-world web-based SPL by applying a set of variability metrics, as well as discussing its quality criteria in comparison with annotations, compositional, and combined existing approaches. Results: Our approach effectively handles both fine and coarse-grained variability. The mapping between the feature model and the web artifacts promotes the traceability of the features and the uniformity of the variation points regardless of the granularity of the web artifacts. Conclusions: Using well-known techniques of SPLs from an architectural point of view, such as feature modeling, can improve the design and maintenance of variable web artifacts without the need of introducing complex approaches for implementing the underlying variability.The work of the authors from the Universidad de Málaga is supported by the projects Magic P12-TIC1814 (post-doctoral research grant), MEDEA RTI2018-099213-B-I00 (co-financed by FEDER funds), Rhea P18-FR-1081 (MCI/AEI/FEDER, UE), LEIA UMA18-FEDERIA-157, TASOVA MCIU-AEI TIN2017-90644-REDT and, European Union’s H2020 research and innovation program under grant agreement DAEMON 101017109. The work of the authors from the Universidade da Coruña has been funded by MCIN/AEI/10.13039/501100011033, NextGenerationEU/PRTR, FLATCITY-POC: PDC2021-121239-C31 ; MCIN/AEI/10.13039/501100011033 EXTRACompact: PID2020-114635RB-I00 ; GAIN/Xunta de Galicia/ERDF CEDCOVID: COV20/00604 ; Xunta de Galicia/FEDER-UE GRC: ED431C 2021/53 ; MICIU/FEDER-UE BIZDEVOPSGLOBAL: RTI-2018-098309-B-C32 ; MCIN/AEI/10.13039/501100011033 MAGIST: PID2019-105221RB-C41Junta de Andalucía; P12-TIC-1814Universidad de Málaga; UMA18-FEDERIA-157Xunta de Galicia; COV20/00604Xunta de Galicia; ED431C 2021/53Junta de Andalucía; P18-FR-108

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-2016-0000Mashup user interfaces provides their functionality through the combination of different services. The integration of such services can be solved by using reusable and third-party components. Furthermore, these interfaces must be adapted to user preferences, context changes, user interactions and component availability. Model transformation is a useful mechanism to address this adaptation but normally these operations only focus on the functional requirements. In this sense, quality attributes should be included in the adaptation process to obtain the best adapted mashup user interface. This paper proposes a generic quality-aware transformation process to support the adaptation of software architectures. The transformation process has been applied in ENIA, a geographic information system, by constructing a specific quality model for the adaptation of mashup user interfaces. This model is taken into account for evaluating the different transformation alternatives and choosing the one that maximizes the quality assessments. The approach has been validated by a set of adaptation scenarios that are intended to maximize different quality factors and therefore apply distinct combinations of metrics.Peer ReviewedPostprint (author's final draft

Extensibility of Enterprise Modelling Languages

Die Arbeit adressiert insgesamt drei Forschungsschwerpunkte. Der erste Schwerpunkt setzt sich mit zu entwickelnden BPMN-Erweiterungen auseinander und stellt deren methodische Implikationen im Rahmen der bestehenden Sprachstandards dar. Dies umfasst zum einen ganz konkrete Spracherweiterungen wie z. B. BPMN4CP, eine BPMN-Erweiterung zur multi-perspektivischen Modellierung von klinischen Behandlungspfaden. Zum anderen betrifft dieser Teil auch modellierungsmethodische Konsequenzen, um parallel sowohl die zugrunde liegende Sprache (d. h. das BPMN-Metamodell) als auch die Methode zur Erweiterungsentwicklung zu verbessern und somit den festgestellten Unzulänglichkeiten zu begegnen. Der zweite Schwerpunkt adressiert die Untersuchung von sprachunabhängigen Fragen der Erweiterbarkeit, welche sich entweder während der Bearbeitung des ersten Teils ergeben haben oder aus dessen Ergebnissen induktiv geschlossen wurden. Der Forschungsschwerpunkt fokussiert dabei insbesondere eine Konsolidierung bestehender Terminologien, die Beschreibung generisch anwendbarer Erweiterungsmechanismen sowie die nutzerorientierte Analyse eines potentiellen Erweiterungsbedarfs. Dieser Teil bereitet somit die Entwicklung einer generischen Erweiterungsmethode grundlegend vor. Hierzu zählt auch die fundamentale Auseinandersetzung mit Unternehmensmodellierungssprachen generell, da nur eine ganzheitliche, widerspruchsfreie und integrierte Sprachdefinition Erweiterungen überhaupt ermöglichen und gelingen lassen kann. Dies betrifft beispielsweise die Spezifikation der intendierten Semantik einer Sprache

Ernst Denert Award for Software Engineering 2019

This open access book provides an overview of the dissertations of the five nominees for the Ernst Denert Award for Software Engineering in 2019. The prize, kindly sponsored by the Gerlind & Ernst Denert Stiftung, is awarded for excellent work within the discipline of Software Engineering, which includes methods, tools and procedures for better and efficient development of high quality software. An essential requirement for the nominated work is its applicability and usability in industrial practice. The book contains five papers describing the works by Sebastian Baltes (U Trier) on Software Developers’Work Habits and Expertise, Timo Greifenberg’s thesis on Artefaktbasierte Analyse modellgetriebener Softwareentwicklungsprojekte, Marco Konersmann’s (U Duisburg-Essen) work on Explicitly Integrated Architecture, Marija Selakovic’s (TU Darmstadt) research about Actionable Program Analyses for Improving Software Performance, and Johannes Späth’s (Paderborn U) thesis on Synchronized Pushdown Systems for Pointer and Data-Flow Analysis – which actually won the award. The chapters describe key findings of the respective works, show their relevance and applicability to practice and industrial software engineering projects, and provide additional information and findings that have only been discovered afterwards, e.g. when applying the results in industry. This way, the book is not only interesting to other researchers, but also to industrial software professionals who would like to learn about the application of state-of-the-art methods in their daily work

A Scalable Design Framework for Variability Management in Large-Scale Software Product Lines

Variability management is one of the major challenges in software product line adoption, since it needs to be efficiently managed at various levels of the software product line development process (e.g., requirement analysis, design, implementation, etc.). One of the main challenges within variability management is the handling and effective visualization of large-scale (industry-size) models, which in many projects, can reach the order of thousands, along with the dependency relationships that exist among them. These have raised many concerns regarding the scalability of current variability management tools and techniques and their lack of industrial adoption. To address the scalability issues, this work employed a combination of quantitative and qualitative research methods to identify the reasons behind the limited scalability of existing variability management tools and techniques. In addition to producing a comprehensive catalogue of existing tools, the outcome form this stage helped understand the major limitations of existing tools. Based on the findings, a novel approach was created for managing variability that employed two main principles for supporting scalability. First, the separation-of-concerns principle was employed by creating multiple views of variability models to alleviate information overload. Second, hyperbolic trees were used to visualise models (compared to Euclidian space trees traditionally used). The result was an approach that can represent models encompassing hundreds of variability points and complex relationships. These concepts were demonstrated by implementing them in an existing variability management tool and using it to model a real-life product line with over a thousand variability points. Finally, in order to assess the work, an evaluation framework was designed based on various established usability assessment best practices and standards. The framework was then used with several case studies to benchmark the performance of this work against other existing tools

Techniques and Practices for Software Architecture Work in Agile Software Development

Since the publishing of Agile Manifesto in 2001, the agile software development has taken the world by storm. Agile software development does not emphasize the importance of software architecture, even though software architecture is often seen as a key factor for achieving the quality goals set for the software system. It has been even said that agile software development and software architecture are a clash of two cultures.In many software projects there is no need to consider software architecture anymore. For example, when designing a mobile application, the ecosystem forces the developer to use certain architecture style provided by the platform. In web development ready-made frameworks and project templates are available offering complete software architecture designs for the application developer.There are still domains and systems where careful architecture design is needed. When developing complex systems or systems with a long lifecycle, diligent software architecture design is a key to avoid massive rework during the development. It cannot be a coincidence that companies developing these kinds of systems struggle with agile software development the most.On the one hand, the goal of this thesis was to study software architecture practices used in agile software development in the industry. On the other hand, the goal was to develop new methods and techniques to support incremental software architecture working practices which can be aligned with agile methods such as Scrum. The study applied case study, interviews and design science as the main research methods.The results show that there are four main ways to carry out software architecture work while using agile methods. Basing on this result, models for aligning software architec-ture knowledge management were developed. These models can be used as guidelines for selecting the appropriate software architecture practices in an organization.As a part of the research work, an architecture knowledge repository was developed for sharing the knowledge in agile projects and for automatic software architecture document generation. Additionally, the results of this study show that by taking a decision-centric approach to software architecture evaluation, the evaluation method can be lightweight enough to make incremental evaluation a viable option. Similarly, existing software architecture evaluation methods can be boosted to fit agile software development by utilizing domain knowledge

Explicitly Integrated Architecture - An Approach for Integrating Software Architecture Model Information with Program Code

Software-Architekturspezifikationen und -Implementierungen sind zwei Sichtweisen auf Softwarearchitektur. Sie beschreiben gemeinsame Aspekte, wie z.B. die Existenz und Verbindung von Komponenten. Die Spezifikation fügt Informationen zum Design, zur Kommunikation und zur Analyse hinzu. Die Implementierung beschreibt stattdessen zusätzlich Details für ein ausführbares System. Die Konsistenz zwischen diesen Darstellungen manuell zu verwalten, ist schwierig und fehleranfällig. Diese Arbeit stellt einen Ansatz vor, der Informationen der Architekturspezifikation vollständig in die Implementierung integriert, sodass die Spezifikation als eigenständiges Artefakt nicht mehr notwendig ist. Das Tool Codeling extrahiert die integrierte Architekturspezifikation in unterschiedlichen Sprachen aus dem Code und propagiert Änderungen in dieser Spezifikation automatisch an den Code zurück.Specifications and implementations are both viewpoints upon software architecture. Besides common aspects, the specification adds information for design, communication, or analysis, while the implementation adds details for an executable system instead. Managing the consistency between these representations manually is difficult and error-prone. This thesis presents an approach, that completely integrates architecture specifications with the implementation, so that separate specification artifacts are not necessary anymore. The tool Codeling extracts integrated architecture specifications in multiple languages from code, and automatically propagates changes in these specifications back to the code

Model-Based Engineering of Collaborative Embedded Systems

This Open Access book presents the results of the "Collaborative Embedded Systems" (CrESt) project, aimed at adapting and complementing the methodology underlying modeling techniques developed to cope with the challenges of the dynamic structures of collaborative embedded systems (CESs) based on the SPES development methodology. In order to manage the high complexity of the individual systems and the dynamically formed interaction structures at runtime, advanced and powerful development methods are required that extend the current state of the art in the development of embedded systems and cyber-physical systems. The methodological contributions of the project support the effective and efficient development of CESs in dynamic and uncertain contexts, with special emphasis on the reliability and variability of individual systems and the creation of networks of such systems at runtime. The project was funded by the German Federal Ministry of Education and Research (BMBF), and the case studies are therefore selected from areas that are highly relevant for Germany’s economy (automotive, industrial production, power generation, and robotics). It also supports the digitalization of complex and transformable industrial plants in the context of the German government's "Industry 4.0" initiative, and the project results provide a solid foundation for implementing the German government's high-tech strategy "Innovations for Germany" in the coming years

Modelling, Reverse Engineering, and Learning Software Variability

The society expects software to deliver the right functionality, in a short amount of time and with fewer resources, in every possible circumstance whatever are the hardware, the operating systems, the compilers, or the data fed as input. For fitting such a diversity of needs, it is common that software comes in many variants and is highly configurable through configuration options, runtime parameters, conditional compilation directives, menu preferences, configuration files, plugins, etc. As there is no one-size-fits-all solution, software variability ("the ability of a software system or artifact to be efficiently extended, changed, customized or configured for use in a particular context") has been studied the last two decades and is a discipline of its own. Though highly desirable, software variability also introduces an enormous complexity due to the combinatorial explosion of possible variants. For example, the Linux kernel has 15000+ options and most of them can have 3 values: "yes", "no", or "module". Variability is challenging for maintaining, verifying, and configuring software systems (Web applications, Web browsers, video tools, etc.). It is also a source of opportunities to better understand a domain, create reusable artefacts, deploy performance-wise optimal systems, or find specialized solutions to many kinds of problems. In many scenarios, a model of variability is either beneficial or mandatory to explore, observe, and reason about the space of possible variants. For instance, without a variability model, it is impossible to establish a sampling strategy that would satisfy the constraints among options and meet coverage or testing criteria. I address a central question in this HDR manuscript: How to model software variability? I detail several contributions related to modelling, reverse engineering, and learning software variability. I first contribute to support the persons in charge of manually specifying feature models, the de facto standard for modeling variability. I develop an algebra together with a language for supporting the composition, decomposition, diff, refactoring, and reasoning of feature models. I further establish the syntactic and semantic relationships between feature models and product comparison matrices, a large class of tabular data. I then empirically investigate how these feature models can be used to test in the large configurable systems with different sampling strategies. Along this effort, I report on the attempts and lessons learned when defining the "right" variability language. From a reverse engineering perspective, I contribute to synthesize variability information into models and from various kinds of artefacts. I develop foundations and methods for reverse engineering feature models from satisfiability formulae, product comparison matrices, dependencies files and architectural information, and from Web configurators. I also report on the degree of automation and show that the involvement of developers and domain experts is beneficial to obtain high-quality models. Thirdly, I contribute to learning constraints and non-functional properties (performance) of a variability-intensive system. I describe a systematic process "sampling, measuring, learning" that aims to enforce or augment a variability model, capturing variability knowledge that domain experts can hardly express. I show that supervised, statistical machine learning can be used to synthesize rules or build prediction models in an accurate and interpretable way. This process can even be applied to huge configuration space, such as the Linux kernel one. Despite a wide applicability and observed benefits, I show that each individual line of contributions has limitations. I defend the following answer: a supervised, iterative process (1) based on the combination of reverse engineering, modelling, and learning techniques; (2) capable of integrating multiple variability information (eg expert knowledge, legacy artefacts, dynamic observations). Finally, this work opens different perspectives related to so-called deep software variability, security, smart build of configurations, and (threats to) science