TY - GEN
T1 - Application of computational intelligence for Source Code classification
AU - Alvares, Marcos
AU - Marwala, Tshilidzi
AU - De Lima Neto, Fernando Buarque
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/16
Y1 - 2014/9/16
N2 - Multi-language Source Code Management systems have been largely used to collaboratively manage software development projects. These systems represent a fundamental step in order to fully use communication enhancements by producing concrete value on the way people collaborate to produce more reliable computational systems. These systems evaluate results of analyses in order to organise and optimise source code. These analyses are strongly dependent on technologies (i.e. framework, programming language, libraries) each of them with their own characteristics and syntactic structure. To overcome such limitation, source code classification is an essential preprocessing step to identify which analyses should be evaluated. This paper introduces a new approach for generating content-based classifiers by using Evolutionary Algorithms. Experiments were performed on real world source code collected from more than 200 different open source projects. Results show us that our approach can be successfully used for creating more accurate source code classifiers. The resulting classifier is also expansible and flexible to new classification scenarios (opening perspectives for new technologies).
AB - Multi-language Source Code Management systems have been largely used to collaboratively manage software development projects. These systems represent a fundamental step in order to fully use communication enhancements by producing concrete value on the way people collaborate to produce more reliable computational systems. These systems evaluate results of analyses in order to organise and optimise source code. These analyses are strongly dependent on technologies (i.e. framework, programming language, libraries) each of them with their own characteristics and syntactic structure. To overcome such limitation, source code classification is an essential preprocessing step to identify which analyses should be evaluated. This paper introduces a new approach for generating content-based classifiers by using Evolutionary Algorithms. Experiments were performed on real world source code collected from more than 200 different open source projects. Results show us that our approach can be successfully used for creating more accurate source code classifiers. The resulting classifier is also expansible and flexible to new classification scenarios (opening perspectives for new technologies).
UR - http://www.scopus.com/inward/record.url?scp=84908584389&partnerID=8YFLogxK
U2 - 10.1109/CEC.2014.6900300
DO - 10.1109/CEC.2014.6900300
M3 - Conference contribution
AN - SCOPUS:84908584389
T3 - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
SP - 895
EP - 902
BT - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Y2 - 6 July 2014 through 11 July 2014
ER -