CALAIS-A Component Analysis Learning Algorithm for Inner Source Development

Ronan Kenny, Enda Fallon, Sheila Fallon, Paul Jacob, Damian Usher

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In the ever evolving world of software development, the complexity of products is increasing. This increased complexity is due to the integration of components built using multiple technologies. In this environment, companies are turning to open source software components to reduce software development time. These freely available open source components are often tried and tested by the software development community. Similar to open sourcing, inner sourcing involves the reuse of software components from other sections within large organizations. As with open sourcing, inner sourcing is experiencing a high adoption. Companies such as Philips, PayPal and Ericsson use open source software in an internal capacity to encourage the reuse of components. The challenge for system architects considering inner sourced components is to (a) determine the complexity, reliability, usage and therefore the importance of individual components within an overall product (b) assess the impact and importance of any individual component when components can differ in scale and technology. This work proposes CALAIS-A Component Analysis Learning Algorithm for Inner Source Development. CALAIS is a self-directed artificial neural network which uses historic performance to weigh the relative importance of an individual component within a system architecture. CALAIS operates by analyzing complexity, reliability, and usage. Using CALAIS, system architects can gain a fine grained view of the structural relevance of all system components proposed for inner sourcing. This view can be used to promote the delivery of high quality components within an inner source project.

Original languageEnglish
Title of host publicationProceedings - 2017 UKSim-AMSS 19th International Conference on Modelling and Simulation, UKSim 2017
EditorsGlenn Jenkins, Alessandra Orsoni, Richard Cant, David Al-Dabass
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3-10
Number of pages8
ISBN (Electronic)9781538627358
ISBN (Print)9781538627358
DOIs
Publication statusPublished - 14 May 2018
Event19th IEEE UKSim-AMSS International Conference on Modelling and Simulation, UKSim 2017 - Cambridge, United Kingdom
Duration: 5 Apr 20177 Apr 2017

Publication series

NameProceedings - 2017 UKSim-AMSS 19th International Conference on Modelling and Simulation, UKSim 2017

Conference

Conference19th IEEE UKSim-AMSS International Conference on Modelling and Simulation, UKSim 2017
Country/TerritoryUnited Kingdom
CityCambridge
Period5/04/177/04/17

Keywords

  • Artificial Neural Networks
  • Component
  • Inner Source
  • Open Source
  • Software Development

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