Software Product Lines modeling improves software development processes by automating system debugging and analysis. The objective of this paper focuses on extending the formal framework SPLA to represent features such as cost objects and comparisons between products in terms of production costs. We illustrate this extension with a practical example by modeling the creation of valid run-lists for Chef, a widely used configuration management tool. Also, we execute our formal specification in a distributed system using SCOOP and we provide strategies to optimize the effort required to compute a SPLA term.
Nowadays, cloud-native software architectures have a significant relevance due to the speed and agility they provide. These properties lead relevant organizations in different industries, like video streaming (Netflix), car-sharing (Uber, Cabify), banking (BBVA, HSBC), and governmental agencies (NASA, FBI, CERN, ESA) to heavily rely on cloud-native software to run their business-critical applications. Additionally, including fault injection actions in the production infrastructure allows companies to have consistent environments, to improve applications dependability against unexpected failures, to provide better user experience, and to improve the overall system quality. Thus, cloud computing technologies allow development teams to rapidly create complex systems and to continuously deploy them, at a global scale.
This work describes Pystol, a novel fault injection platform-represented as aSoftware Product Line-to analyze the effects caused by a wide spectrum of adverse conditions. Pystol is designed to be executed on top of cloud-native environments, either in private or public clouds. The proposed architecture shows a way for representing feature models based on Unified Model Language (in short, UML) component diagrams. Furthermore, we present a thorough empirical study carried out in real-world environments, providing promising results.
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Lowering pollution levels is one of the main principles of Sustainable Development goals dictated by the United Nations. Consequently, developments on noise monitoring contribute in great manner to this purpose, since they give the opportunity to governments and institutions to maintain track on the matter. While developing a software product for this purpose, with the growth in terms of functional and non-functional requirements, elements such as infrastructure, source code and others also scale up. Consequently if there are not good practices to face the new challenges of the software product, it could become more complex to refactor, maintain and scale, causing a decrease on delivery rate and the quality of the product. DevOps is an emerging concept but still hazy, which involves a set of practices that helps organizations to speed up delivery time, improve software quality and collaboration between teams. The aim of this paper is to document the implementation of some DevOps practices such as IaC, continuous integration and deployment, code quality control and collaboration on a noise monitor system to increase the product quality and automation of deployment. The final result is a set of automated pipelines which represents the entire integration and deployment cycle of the software integrated with platforms to improve quality and maintainability of the software components.
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