Multiple Software Product Lines: applications and challenges

Trujillo-Tzanahua, Guadalupe Isaura; Juárez-Martínez, Ulises; Aguilar-Lasserre, Alberto A.; Cortés-Verdín, María Karen

doi:10.1007/978-3-319-69341-5_11

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…It is possible that multi product lines could be beneficial (Damiani et al 2019;Holl et al 2012;Trujillo-Tzanahua et al 2018) to help with this representation. A multi product line breaks down a large-scale system into a set of several self-contained and independent product lines.…”

Section: Importance Of Constructs That Support Duplicate Featuresmentioning

confidence: 99%

An empirical investigation of organic software product lines

et al. 2021

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Software product line engineering is a best practice for managing reuse in families of software systems that is increasingly being applied to novel and emerging domains. In this work we investigate the use of software product line engineering in one of these new domains, synthetic biology. In synthetic biology living organisms are programmed to perform new functions or improve existing functions. These programs are designed and constructed using small building blocks made out of DNA. We conjecture that there are families of products that consist of common and variable DNA parts, and we can leverage product line engineering to help synthetic biologists build, evolve, and reuse DNA parts. In this paper we perform an investigation of domain engineering that leverages an open-source repository of more than 45,000 reusable DNA parts. We show the feasibility of these new types of product line models by identifying features and related artifacts in up to 93.5% of products, and that there is indeed both commonality and variability. We then construct feature models for four commonly engineered functions leading to product lines ranging from 10 to 7.5 × 1020 products. In a case study we demonstrate how we can use the feature models to help guide new experimentation in aspects of application engineering. Finally, in an empirical study we demonstrate the effectiveness and efficiency of automated reverse engineering on both complete and incomplete sets of products. In the process of these studies, we highlight key challenges and uncovered limitations of existing SPL techniques and tools which provide a roadmap for making SPL engineering applicable to new and emerging domains.

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Section: Importance Of Constructs That Support Duplicate Featuresmentioning

confidence: 99%

An empirical investigation of organic software product lines

et al. 2021

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“…However, when having multiple users at different levels of the software (e.g., platform developer versus plug-in developer versus end user), a configuration might affect very different modules, developed, maintained, and deployed by different stakeholders, vendors, and customers. Research on multi software product lines has analyzed some isolated aspects of dependent and distributed configuration [29,43], but have not taken into account most aspects of configuration, such as non-functional optimization. But, it is important to fully understand how far-reaching a single configuration is to estimate the impact of configuration choices on the whole software ecosystem.…”

Section: Configuration Complexitymentioning

confidence: 99%

Dimensions of software configuration: on the configuration context in modern software development

Siegmund¹,

Ruckel

Siegmund

2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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“…A first modeling challenge is the situation when more than one product line is involved and these product lines depend on each other. Such sets of related and interdependent product lines are known as a multi product line (MPL) (Holl et al, 2012;Trujillo-Tzanahua et al, 2018). A second modeling challenge, orthogonal to MPLs, is the situation when different product variants from the same product line need to co-exist in the same context and must be interoperable (Damiani et al, 2018a).…”

Section: Introductionmentioning

confidence: 99%