Predicting class libraries interface evolution: an investigation into machine learning approaches

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Seventh Asia-Pacific Software Engineering Conference (APSEC'00)

Singapore

December 05-December 08

ISBN: 0-7695-0915-0

	ASCII Text	x
	H.A. Sahraoui, A.M. Boukadoum, H. Lounis, F. Etheve, "Predicting class libraries interface evolution: an investigation into machine learning approaches," Asia-Pacific Software Engineering Conference, pp. 456, Seventh Asia-Pacific Software Engineering Conference (APSEC'00), 2000.

	BibTex	x
	@article{ 10.1109/APSEC.2000.896734, author = {H.A. Sahraoui and A.M. Boukadoum and H. Lounis and F. Etheve}, title = {Predicting class libraries interface evolution: an investigation into machine learning approaches}, journal ={Asia-Pacific Software Engineering Conference}, volume = {0}, year = {2000}, issn = {1530-1362}, pages = {456}, doi = {http://doi.ieeecomputersociety.org/10.1109/APSEC.2000.896734}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Asia-Pacific Software Engineering Conference TI - Predicting class libraries interface evolution: an investigation into machine learning approaches SN - 1530-1362 SP EP A1 - H.A. Sahraoui, A1 - A.M. Boukadoum, A1 - H. Lounis, A1 - F. Etheve, PY - 2000 KW - software libraries; learning (artificial intelligence); decision trees; software reusability; software metrics; software quality; object-oriented programming; user interfaces; fuzzy logic; pattern classification; Bayes methods; management of change; software development management; machine learning; class library interface evolution prediction; object-oriented system evolution management; reusable class libraries; user interface; version compatibility; symptomatic instability detection; fuzzy logic-based approach; structural metrics; stability indicators; commercial C++ class libraries; Bayesian classifiers; top down induction of decision trees VL - 0 JA - Asia-Pacific Software Engineering Conference ER -

H.A. Sahraoui, Montreal Univ., Que., Canada

A.M. Boukadoum, Montreal Univ., Que., Canada

H. Lounis, Montreal Univ., Que., Canada

F. Etheve, Montreal Univ., Que., Canada

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/APSEC.2000.896734

Managing the evolution of an OO system constitutes a complex and resource-consuming task. This is particularly true for reusable class libraries since the user interface must be preserved for version compatibility. Thus, the symptomatic detection of potential instabilities during the design phase of such libraries may help avoid later problems. This paper introduces a fuzzy logic-based approach for evaluating the stability of a reusable class library interface, using structural metrics as stability indicators. To evaluate this new approach, we conducted a preliminary study on a set of commercial C++ class libraries. The obtained results are very promising when compared to those of two classical machine learning approaches, top down induction of decision trees and Bayesian classifiers.

Index Terms:

software libraries; learning (artificial intelligence); decision trees; software reusability; software metrics; software quality; object-oriented programming; user interfaces; fuzzy logic; pattern classification; Bayes methods; management of change; software development management; machine learning; class library interface evolution prediction; object-oriented system evolution management; reusable class libraries; user interface; version compatibility; symptomatic instability detection; fuzzy logic-based approach; structural metrics; stability indicators; commercial C++ class libraries; Bayesian classifiers; top down induction of decision trees

Citation:

H.A. Sahraoui, A.M. Boukadoum, H. Lounis, F. Etheve, "Predicting class libraries interface evolution: an investigation into machine learning approaches," apsec, pp.456, Seventh Asia-Pacific Software Engineering Conference (APSEC'00), 2000

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