TY - GEN
T1 - CALAIS-A Component Analysis Learning Algorithm for Inner Source Development
AU - Kenny, Ronan
AU - Fallon, Enda
AU - Fallon, Sheila
AU - Jacob, Paul
AU - Usher, Damian
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/5/14
Y1 - 2018/5/14
N2 - 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.
AB - 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.
KW - Artificial Neural Networks
KW - Component
KW - Inner Source
KW - Open Source
KW - Software Development
UR - http://www.scopus.com/inward/record.url?scp=85048361515&partnerID=8YFLogxK
U2 - 10.1109/UKSim.2017.28
DO - 10.1109/UKSim.2017.28
M3 - Conference contribution
AN - SCOPUS:85048361515
SN - 9781538627358
T3 - Proceedings - 2017 UKSim-AMSS 19th International Conference on Modelling and Simulation, UKSim 2017
SP - 3
EP - 10
BT - Proceedings - 2017 UKSim-AMSS 19th International Conference on Modelling and Simulation, UKSim 2017
A2 - Jenkins, Glenn
A2 - Orsoni, Alessandra
A2 - Cant, Richard
A2 - Al-Dabass, David
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE UKSim-AMSS International Conference on Modelling and Simulation, UKSim 2017
Y2 - 5 April 2017 through 7 April 2017
ER -