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Detection of faulty components in object-oriented systems using design metrics and a machine learning algorithm

Object-Oriented (OO) technology claims faster development and higher quality of software than the procedural paradigm. The quality of the product is the single most important reason that determines its acceptance and success. The basic project management problem is "delivery of a product with targeted quality, within the budget, and on schedule". We propose a state-of-the-art approach that gets closer to the solution by improving the software development process used. An important objective in all software development is to ensure that the delivered product is as fault-free as possible. We proposed three hypotheses that relate the OO design properties---inheritance, cohesion, and coupling---and the fault-proneness as software's quality indicator. We built classification models that predict which components are likely to be faulty, based on an appropriate suite of OO design measures. The models represent empirical evidence that the aforementioned relationships exist. We used the C4.5 machine learning algorithm as a predictive modeling technique, because it is robust, reliable, and allows intelligible interpretation of the results. We defined three new measures that quantify the specific contribution of each of the metrics selected by the model(s), and also provide a deeper insight into the design structure of the product. We evaluated the quality of the predictive models using an objective set of standards. The models built have high quality.

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.20961
Date January 1998
CreatorsIkonomovski, Stefan V.
ContributorsRatzer, Gerald (advisor)
PublisherMcGill University
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Formatapplication/pdf
CoverageMaster of Science (School of Computer Science.)
RightsAll items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated.
Relationalephsysno: 001654729, proquestno: MQ50796, Theses scanned by UMI/ProQuest.

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