Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/427
Title: Validating cohesion metrics by mining open source software data with association rules
Authors: Singh, Pariksha 
Keywords: Open source software;Data mining;Association rule mining;Software measurement
Issue Date: 2008
Abstract: 
Competitive pressure on the software industry encourages organizations to examine
the effectiveness of their software development and evolutionary processes.
Therefore it is important that software is measured in order to improve the quality.
The question is not whether we should measure software but how it should be
measured. Software measurement has been in existence for over three decades and it
is still in the process of becoming a mature science. The many influences of new
software development technologies have led to a diverse growth in software
measurement technologies which have resulted in various definitions and validation
techniques.
An important aspect of software measurement is the measurement of the design,
which nowadays often means the measurement of object oriented design. Chidamer
and Kemerer (1994) designed a metric suite for object oriented design, which has
provided a new foundation for metrics and acts as a starting point for further
development of the software measurement science.
This study documents theoretical object oriented cohesion metrics and calculates
those metrics for classes extracted from a sample of open source software packages.
For each open source software package, the following data is recorded: software size,
age, domain, number of developers, number of bugs, support requests, feature
requests, etc. The study then tests by means of association rules which theoretical
cohesion metrics are validated hypothesis: that older software is more cohesive than
younger software, bigger packages is less cohesive than smaller packages, and the
smaller the software program the more maintainable it is.
This study attempts to validate existing theoretical object oriented cohesion metrics
by mining open source software data with association rules.
Description: 
Dissertation submitted for the fulfillment of the requirement for the degree of Masters in Information Technology,
Durban University of Technology, South Africa, 2008.
URI: http://hdl.handle.net/10321/427
DOI: https://doi.org/10.51415/10321/427
Appears in Collections:Theses and dissertations (Accounting and Informatics)

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