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Characterizing software development method using metrics
Author(s) -
Shawky Doaa M.,
AbdElHafiz Salwa K.
Publication year - 2016
Publication title -
journal of software: evolution and process
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 29
eISSN - 2047-7481
pISSN - 2047-7473
DOI - 10.1002/smr.1766
Subject(s) - agile software development , computer science , cohesion (chemistry) , artificial neural network , data mining , software , feature selection , artificial intelligence , software metric , object oriented programming , machine learning , process (computing) , software engineering , software development , quality (philosophy) , software quality , philosophy , chemistry , operating system , organic chemistry , epistemology , programming language
This work investigates whether the development methodology affects some metrics that are related to the quality of the generated code. Trying to characterize the development methodology using software metrics could be beneficial to developers who use agile approaches extensively. Therefore, this study sheds light on the metrics that might be highly affected by following agile approaches. The values of these metrics need to be tracked, because they might result in a quality‐related issue, such as error‐proneness, for example. The proposed approach employs different feature selection and classification methods, which include artificial neural networks. We used 11 object‐oriented systems of various sizes and functionalities. Five of the used systems were developed using agile approaches, while the rest were developed using non‐agile methodologies. The classes and methods of the used systems were represented by a set of metrics that shows some complexity, cohesion, and coupling features of the systems. The generated metrics are used as features to classify between the two different types of the development methodologies using a feed‐forward artificial neural network. The obtained high classification accuracy shows the strong relationship between the used metrics and the type of the followed development process. Copyright © 2015 John Wiley & Sons, Ltd.