Abstract: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 syste… Show more
“…Product configuration and derivation [35,37,42,46,47,49,50,53,55,56,57,58,59,60,65,66,68,72,74,75,76,77,80,82,83,89,93,95,98,99,100,103,105,106,107,108,109,110,112,114,116,118,119,120,123,125,126,128,133,134,135,136,…”
Section: Variability Contextmentioning
confidence: 99%
“…Testing and evolution [28,33,37,38,41,43,44,45,48,52,64,67,69,72,73,83,91,92,97,104,105,107,111,113,114,115,117,121,122,124,128,129,130,132,134,135,139,141,143,144,150,152,159,160,161,162,164,165,166,168,170,176,…”
Section: Variability Contextmentioning
confidence: 99%
“…Citations #papers Opinion Paper [66,73,75,144,164,193,248] 7 Philosophical Paper [51,74,99,101,134,137,171,175,196,201,244] 11 Solution Proposal [30,35,44,58,60,65,72,78,79,80,81,93,102,106,140,141,142,145,151,158,162,165,172,176,184,185,191,195,197,198,199,200,203,…”
Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.
“…Product configuration and derivation [35,37,42,46,47,49,50,53,55,56,57,58,59,60,65,66,68,72,74,75,76,77,80,82,83,89,93,95,98,99,100,103,105,106,107,108,109,110,112,114,116,118,119,120,123,125,126,128,133,134,135,136,…”
Section: Variability Contextmentioning
confidence: 99%
“…Testing and evolution [28,33,37,38,41,43,44,45,48,52,64,67,69,72,73,83,91,92,97,104,105,107,111,113,114,115,117,121,122,124,128,129,130,132,134,135,139,141,143,144,150,152,159,160,161,162,164,165,166,168,170,176,…”
Section: Variability Contextmentioning
confidence: 99%
“…Citations #papers Opinion Paper [66,73,75,144,164,193,248] 7 Philosophical Paper [51,74,99,101,134,137,171,175,196,201,244] 11 Solution Proposal [30,35,44,58,60,65,72,78,79,80,81,93,102,106,140,141,142,145,151,158,162,165,172,176,184,185,191,195,197,198,199,200,203,…”
Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.
“…It is worth to mention that the order in which features are processed to create a specific product is directly reflected in its final cost. In a previous work we introduced costs in our formal framework for representing the required effort to include a feature to the product under construction [18]. This cost may represent different aspects of a feature, such as lines of code of a given software component or effort, in human hours, to include a software component into a project, just to name a few, that usually depend on the target of the product line organization.…”
Section: Introductionmentioning
confidence: 99%
“…-A model that uses probabilistic information to determine the probability of having a feature in a specific SPL. In contrast with our previous work [8,18], which mainly focuses in defining an algebraic language to describe Software Product Lines and using a cost model for comparing valid products, this approach is targeted to identify those features that are more frequently used to generate a product. Basically, the idea is to focus on those features with a high probability to be present in the final product and, therefore, reducing the required processing to generate valid products.…”
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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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