TY - GEN
T1 - STATS - Software component trend analysis over time series
AU - Kenny, Ronan
AU - Fallon, Enda
AU - Fallon, Sheila
AU - Mannion, Fionnuala
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/8
Y1 - 2018/6/8
N2 - Through initiatives such as open sourcing, software development organizations have embraced component reuse. Inner sourcing, in which organizations reuse internally created components, is gaining interest. Large companies such as Philips, PayPal and Ericsson have embraced inner source initiatives. Software components reuse can significantly reduce software development and testing time. A challenge when reusing components is to gauge their quality and projected reliability over time. Minor component changes can create unforeseen complexities. This work proposes STATS - Software Component Trend Analysis Over Time Series, a self-directed artificial neural network which uses historic performances to predict the performance of inner source components over time. Using time series-based learning instances, STATS can aid in the prediction of component trouble reports based on historic knowledge. This is accomplished through the history of components trouble reports over varying time series. Using STATS, system architects and developers predict the reliability of candidate open source and inner source software components in the medium and long term.
AB - Through initiatives such as open sourcing, software development organizations have embraced component reuse. Inner sourcing, in which organizations reuse internally created components, is gaining interest. Large companies such as Philips, PayPal and Ericsson have embraced inner source initiatives. Software components reuse can significantly reduce software development and testing time. A challenge when reusing components is to gauge their quality and projected reliability over time. Minor component changes can create unforeseen complexities. This work proposes STATS - Software Component Trend Analysis Over Time Series, a self-directed artificial neural network which uses historic performances to predict the performance of inner source components over time. Using time series-based learning instances, STATS can aid in the prediction of component trouble reports based on historic knowledge. This is accomplished through the history of components trouble reports over varying time series. Using STATS, system architects and developers predict the reliability of candidate open source and inner source software components in the medium and long term.
KW - Artificial neural networks
KW - Prediction
KW - Software development
KW - Time series
KW - Trend analysis
UR - http://www.scopus.com/inward/record.url?scp=85049908192&partnerID=8YFLogxK
U2 - 10.1109/ICIRD.2018.8376317
DO - 10.1109/ICIRD.2018.8376317
M3 - Conference contribution
AN - SCOPUS:85049908192
SN - 9781538656969
T3 - 2018 IEEE International Conference on Innovative Research and Development, ICIRD 2018
SP - 1
EP - 6
BT - 2018 IEEE International Conference on Innovative Research and Development, ICIRD 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Innovative Research and Development, ICIRD 2018
Y2 - 11 May 2018 through 12 May 2018
ER -