Model-driven planning and monitoring of long-term software product line evolution

Schubanz, Mathias; Pleuss, Andreas; Pradhan, Ligaj; Botterweck, Goetz; Thurimella, Anil Kumar

doi:10.1145/2430502.2430527

Cited by 15 publications

(7 citation statements)

References 12 publications

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“…Schubanz et al (2013) developed their own specific modeling approach, which is integrated in a prototypical tool chain and focuses on a high level of abstraction, i.e., evolution of development goals and requirements. Their main goal is to provide a tool which is able to manage evolution over several releases (variability in time) as well as the ability to give the developer traces from the requirements to their respective implementation variants (variability in space).…”

Section: Evolution Of Problem Space Variability Modelsmentioning

confidence: 99%

Evolution of software in automated production systems: Challenges and research directions

Vogel‐Heuser

Fay

Schaefer

et al. 2015

Journal of Systems and Software

253

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a b s t r a c tCoping with evolution in automated production systems implies a cross-disciplinary challenge along the system's life-cycle for variant-rich systems of high complexity. The authors from computer science and automation provide an interdisciplinary survey on challenges and state of the art in evolution of automated production systems. Selected challenges are illustrated on the case of a simple pick and place unit. In the first part of the paper, we discuss the development process of automated production systems as well as the different type of evolutions during the system's life-cycle on the case of a pick and place unit. In the second part, we survey the challenges associated with evolution in the different development phases and a couple of cross-cutting areas and review existing approaches addressing the challenges. We close with summarizing future research directions to address the challenges of evolution in automated production systems.

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Section: Evolution Of Problem Space Variability Modelsmentioning

confidence: 99%

Evolution of software in automated production systems: Challenges and research directions

Vogel‐Heuser

Fay

Schaefer

et al. 2015

Journal of Systems and Software

253

View full text Add to dashboard Cite

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“…Even though software evolution is usually related to changes in stakeholders' needs and requirements, the research in goaloriented or intentional evolution is not profuse [23]. Nonetheless, there are some approaches that deal with the evolution of requirements or goals and how this evolution impacts on the system, including the context of SPLs [24][25][26] and MAS [27].…”

Section: Goal-oriented Software Evolutionmentioning

confidence: 99%

“…The work in [25] supports the evolution of the SPL to cope with changes in the stakeholders' requirements. The EvoPL framework supports an integrated model-driven approach to plan the evolution of SPLs in the production of different releases of a product at feature level.…”

Section: Goal-oriented Software Evolutionmentioning

confidence: 99%

“…In conclusion, these approaches [23][24][25][26][27] consider the evolution of the goal models but they do not propagate the changes to the variability model nor the final architectural elements. In fact, no work takes into account both mapping between goal models and variability models and the subsequent automatic updating of that mapping when the requirements evolve.…”

Section: Goal-oriented Software Evolutionmentioning

confidence: 99%

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A goal-driven software product line approach for evolving multi-agent systems in the Internet of Things

Ayala

Amor

Horcas

et al. 2019

Knowledge-Based Systems

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Multi-agent systems have proved to be a suitable technology for developing self-adaptive Internet of Things (IoT) systems, able to make the most appropriate decisions to address unexpected situations. This leads to new opportunities to use multi-agent technologies to develop all kinds of cyberphysical systems, which usually encompass a high diversity of devices (e.g., new home appliances). The heterogeneity of devices and the high diversity of the available technology, demand the explicit modeling of all kinds of variability for ultra-large systems. However, multi-agent systems lack mechanisms to effectively deal with the different degrees of variability present in these kinds of systems. Software Product Line (SPL) technologies, including variability models, have been successfully applied to different domains to explicitly model variability in hardware, system requirements or userintended goals. In addition, current market trends are unpredictable, imposing novel technologies, new requirements and goals that must be incorporated immediately into the running systems without damaging them. In this paper, we combine goal-driven and SPL approaches to develop and drive the evolution of multi-agent systems in the context of cyber-physical systems. We propose an SPL process and an evolution process that define a set of models (iStar 2.0 for goals and CVL models for variability) and algorithms to automatically propagate changes to agents running in multiple heterogeneous devices, each of them with a different configuration. We illustrate the proposal in the context of a home energy management system. Finally, we have tested the scalability and performance of the proposal using randomly generated models. The results show that with our approach it is possible to manage huge iStar models of 10000 elements in seconds.

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“…They provided a feature-oriented project management and system development platform that supports traceability and analyses. There is also some work towards the monitoring, management and planning of the evolution of an SPL, for instance [140], presents a tool to plan and manage the evolution of an SPL focusing on goals and requirements.…”

Section: Software Product Line Evolutionmentioning

confidence: 99%

Automating the variability formalization of a model family by means of common variability language

Font

Ballarín

Haugen

et al. 2015

Proceedings of the 19th International Conference on Software Product Line

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INTRODUCTION Research Questions related to these challenges: Research Question 1: How to identify and formalize the variability present among a set of product models in terms of features realized by model fragments? Research Question 2: How to capitalize on expert domain knowledge to boost the process of feature location? 1.7. Structure of the dissertation 1.7 Structure of the dissertation This dissertation is structured into five parts: Part I The first part is the introduction of the dissertation, later it presents some background and discusses the state of the art.

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Model-driven planning and monitoring of long-term software product line evolution

Cited by 15 publications

References 12 publications

Evolution of software in automated production systems: Challenges and research directions

Evolution of software in automated production systems: Challenges and research directions

A goal-driven software product line approach for evolving multi-agent systems in the Internet of Things

Automating the variability formalization of a model family by means of common variability language

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