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Title: An empirical study of package coupling in Java open-source
Author: Mubarak, Asma
ISNI:       0000 0004 2688 828X
Awarding Body: Brunel University
Current Institution: Brunel University
Date of Award: 2010
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Excessive coupling between object-oriented classes in systems is generally acknowledged as harmful and is recognised as a maintenance problem that can result in a higher propensity for faults in systems and a „stored up‟ future problem. Characterisation and understanding coupling at different levels of abstraction is therefore important for both the project manager and developer both of whom have a vested interest in software quality. In this Thesis, coupling trends are empirically investigated over multiple versions of seven Java open-source systems (OSS). The first investigation explores the trends in longitudinal changes to open-source systems given by six coupling metrics. Coupling trends are then explored from the perspective of: the relationship between removed classes and their coupling with other classes in the same package; the relationships between coupling and 'warnings’ in packages and the time interval between versions in Java OSS; the relationship between some of these coupling metrics are also explored. Finally, the existence of an 80/20 rule for the coupling metrics is inspected. Results suggest that developer activity comprises a set of high and low periods (peak and trough‟ effect) evident as a system evolves. Findings also demonstrate that addition of coupling may have beneficial effects on a system, particularly if they are added as new functionality through the package Java feature. The fan-in and fan-out coupling metrics reveal particular features and exhibited a wide range of traits in the classes depending on their high or low values; finally, we revealed that one metric (fan-in) is the only metric that appears consistently to exhibit an 80/20 (Pareto) relationship.
Supervisor: Counsell, S. ; Hierons, R. M. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Software metrics ; Evolution ; Refactoring ; Power law ; Warning