Managing the Complexity of Large Free and Open Source Package-Based Software Distributions

Mancinelli, Fabio; Boender, Jaap; Cosmo, Roberto Di; Vouillon, Jérôme; Durak, Berke; Leroy, Xavier; Treinen, Ralf

doi:10.1109/ase.2006.49

Cited by 113 publications

(118 citation statements)

References 10 publications

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“…For further details on the modeling languages and manifests, we refer to [2,48,49] (eCos and Linux kernel), [50] (Debian), [51] (Eclipse), and [52] (Android), in addition to our within-case write-ups (cf., Section 3.4). Grouping and Categorization.…”

Section: Variability Representationmentioning

confidence: 99%

Variability mechanisms in software ecosystems

Berger

Pfeiffer

Tartler

et al. 2014

Information and Software Technology

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Context: Software ecosystems are increasingly popular for their economic, strategic, and technical advantages. Application platforms such as Android or iOS allow users to highly customize a system by selecting desired functionality from a large variety of assets. This customization is achieved using variability mechanisms. Objective: Variability mechanisms are well-researched in the context of software product lines. Although software ecosystems are often seen as conceptual successors, the technology that sustains their success and growth is much less understood. Our objective is to improve the empirical understanding of variability mechanisms used in successful software ecosystems. Method: We analyze five ecosystems, ranging from the Linux kernel through Eclipse to Android. A qualitative analysis identifies and characterizes variability mechanisms together with their organizational context. This analysis leads to a conceptual framework that unifies ecosystem-specific aspects using a common terminology. A quantitative analysis investigates scales, growth rates, and-most importantly-dependency structures of the ecosystems. Results: In all the studied ecosystems, we identify rich dependency languages and variability descriptions that declare many direct and indirect dependencies. Indirect dependencies to abstract capabilities, as opposed to concrete variability units, are used predominantly in fast-growing ecosystems. We also find that variability models-while providing systemwide abstractions over code-work best in centralized variability management and are, thus, absent in ecosystems with large free markets. These latter ecosystems tend to emphasize maintaining capabilities and common vocabularies, dynamic discovery, and binding with strong encapsulation of contributions, together with uniform distribution channels. Conclusion:The use of specialized mechanisms in software ecosystems with large free markets, as opposed to software product lines, calls for recognition of a new discipline-variability encouragement.

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Section: Variability Representationmentioning

confidence: 99%

Variability mechanisms in software ecosystems

Berger

Pfeiffer

Tartler

et al. 2014

Information and Software Technology

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“…Each phase of the upgrade process can fail. Dependency resolution can fail either because the user request is unsatisfiable (e.g., user error or inconsistent distributions [8]) or because the meta-installer is unable to find a solution. Completenessthe guarantee that a solution will be found whenever one exists-is a desirable metainstaller property unfortunately missing in most meta-installers, with too few claimed exceptions [9].…”

Section: Upgradesmentioning

confidence: 99%

A Model Driven Approach to Upgrade Package-Based Software Systems

Cicchetti

Ruscio

Pierantonio

et al. 2010

Communications in Computer and Information Science

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Abstract. Complex software systems are often based on the abstraction of package, brought to popularity by Free and Open Source Software (FOSS) distributions. While helpful as an encapsulation layer, packages do not solve all problems of deployment, and more generally of management, of large software collections. In particular upgrades, which can affect several packages at once due to interpackage dependencies, often fail and do not hold good transactional properties. This paper shows how to apply model driven techniques to describe and manage software upgrades of FOSS distributions. It is discussed how to model static and dynamic aspects of package upgrades-the latter being the more challenging to deal with-in order to be able to predict common causes of upgrade failures and undo residual effects of failed or undesired upgrades.

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“…And finally, there are two different types of dependencies, conjunctive (the 'normal' kind, which can only be satisfied in one way) and disjunctive (where a dependency may be satisfied by one out of a list of packages). For formal definitions of these concepts, please refer to [7].…”

Section: Background and Definitionsmentioning

confidence: 99%

Small World Characteristics of FLOSS Distributions

Boender

Fernandes²

2014

Software Engineering and Formal Methods

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Abstract. Over the years, Free/Libre Open Source Software (FLOSS) distributions have become more and more complex and recent versions contain tens of thousands of packages. This has made it impossible to do quality control by hand. Instead, distribution editors must look to automated methods to ensure the quality of their distributions. In the present paper, we present some insights into the general structure of FLOSS distributions. We notably show that such distributions have the characteristics of a small world network: there are only a few important packages, and many less important packages. Identifying the important packages can help editors focus their efforts on parts of the distribution where errors will have important consequences.

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Managing the Complexity of Large Free and Open Source Package-Based Software Distributions

Cited by 113 publications

References 10 publications

Variability mechanisms in software ecosystems

Variability mechanisms in software ecosystems

A Model Driven Approach to Upgrade Package-Based Software Systems

Small World Characteristics of FLOSS Distributions

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