Learning and evolution in dynamic software product lines

Sharifloo, Amir Molzam; Metzger, Andreas; Quinton, Clément; Baresi, Luciano; Pohl, Klaus

doi:10.1145/2897053.2897058

Cited by 31 publications

(17 citation statements)

References 35 publications

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“…For instance, we excluded Reference [33], although the topic is relevant for our study, but this is a short paper. Similarly, we excluded Reference [81], since this regular paper does not provide a sufficient level of assessment.…”

Section: Threats To Validitymentioning

confidence: 99%

Applying Machine Learning in Self-adaptive Systems

Gheibi

Weyns

Quin

2020

ACM Trans. Auton. Adapt. Syst.

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Recently, we have been witnessing a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to filtering large sets of possible configurations before analyzing them. While a body of work on the use of machine learning in self-adaptive systems exists, there is currently no systematic overview of this area. Such an overview is important for researchers to understand the state of the art and direct future research efforts. This article reports the results of a systematic literature review that aims at providing such an overview. We focus on self-adaptive systems that are based on a traditional Monitor-Analyze-Plan-Execute (MAPE)-based feedback loop. The research questions are centered on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges in this area. The search resulted in 6,709 papers, of which 109 were retained for data collection. Analysis of the collected data shows that machine learning is mostly used for updating adaptation rules and policies to improve system qualities, and managing resources to better balance qualities and resources. These problems are primarily solved using supervised and interactive learning with classification, regression, and reinforcement learning as the dominant methods. Surprisingly, unsupervised learning that naturally fits automation is only applied in a small number of studies. Key open challenges in this area include the performance of learning, managing the effects of learning, and dealing with more complex types of goals. From the insights derived from this systematic literature review, we outline an initial design process for applying machine learning in self-adaptive systems that are based on MAPE feedback loops.

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Section: Threats To Validitymentioning

confidence: 99%

Applying Machine Learning in Self-adaptive Systems

Gheibi

Weyns

Quin

2020

ACM Trans. Auton. Adapt. Syst.

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“…Existing DSPL approaches are typically based on ad-hoc adaptation mechanisms that need to be maintained manually to keep them consistent with the problem and solution spaces during evolution 16 . Proper evolution support is, however, crucial to guarantee the consistency of the DSPL and of the (potential) adaptations of the running system 17 . Furthermore, existing approaches typically only support one particular variability management approach and are not flexible enough to allow their use in different domains and for different types of systems, using diverse implementation consistency checking solutions to support evolution in a concrete DSPL.…”

Section: Figurementioning

confidence: 99%

Evolution in dynamic software product lines

Quinton

Vierhauser

Rabiser

et al. 2020

J Software Evolu Process

Self Cite

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Many software systems today provide support for adaptation and reconfiguration at runtime, in response to changes in their environment. Such adaptive systems are designed to run continuously and may not be shut down for reconfiguration or maintenance tasks. The variability of such systems has to be explicitly managed, together with mechanisms that control their runtime adaptation and reconfiguration. Dynamic software product lines (DSPLs) can help to achieve this. However, dealing with evolution is particularly challenging in a DSPL, as changes made at runtime can easily lead to inconsistencies. This paper describes the challenges of evolving DSPLs using an example cyber-physical system for home automation. We discuss the shortcomings of existing work and present a reference architecture to support DSPL evolution. To demonstrate its feasibility and flexibility, we implemented the proposed reference architecture for two different DSPLs: the aforementioned cyber-physical system, which uses feature models to describe its variability, and a runtime monitoring infrastructure, which is based on decision models. To assess the industrial applicability of our approach, we also implemented the reference architecture for a real-world DSPL, an automation software system for injection molding machines. Our results provide evidence on the flexibility, performance and industrial applicability of our approach.

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“…The authors of [22] argue that it is not reasonable to anticipate all relevant context changes during design-time and therefore propose a model that combines learning of adaptation rules with evolution of the configuration space. In the approach of the present paper, it is assumed that the available features are known at design-time.…”

Section: A Related Workmentioning

confidence: 99%

Supervisory Control for Dynamic Feature Configuration in Product Lines

Reniers

Thuijsman

2020

2020 Forum for Specification and Design Languages (FDL)

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Learning and evolution in dynamic software product lines

Cited by 31 publications

References 35 publications

Applying Machine Learning in Self-adaptive Systems

Applying Machine Learning in Self-adaptive Systems

Evolution in dynamic software product lines

Supervisory Control for Dynamic Feature Configuration in Product Lines

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