Document Type
Dissertation
Degree
Doctor of Philosophy (PhD)
Major/Program
Computer Science
First Advisor's Name
Naphtali Rishe
First Advisor's Committee Title
Committee Chair
Second Advisor's Name
Xudong He
Third Advisor's Name
Masoud Milani
Fourth Advisor's Name
B. M. Golam Kibria
Keywords
software measurement, fault prediction
Date of Defense
11-9-2012
Abstract
As users continually request additional functionality, software systems will continue to grow in their complexity, as well as in their susceptibility to failures. Particularly for sensitive systems requiring higher levels of reliability, faulty system modules may increase development and maintenance cost. Hence, identifying them early would support the development of reliable systems through improved scheduling and quality control. Research effort to predict software modules likely to contain faults, as a consequence, has been substantial.
Although a wide range of fault prediction models have been proposed, we remain far from having reliable tools that can be widely applied to real industrial systems. For projects with known fault histories, numerous research studies show that statistical models can provide reasonable estimates at predicting faulty modules using software metrics. However, as context-specific metrics differ from project to project, the task of predicting across projects is difficult to achieve. Prediction models obtained from one project experience are ineffective in their ability to predict fault-prone modules when applied to other projects. Hence, taking full benefit of the existing work in software development community has been substantially limited. As a step towards solving this problem, in this dissertation we propose a fault prediction approach that exploits existing prediction models, adapting them to improve their ability to predict faulty system modules across different software projects.
Identifier
FI12120408
Recommended Citation
Babic, Djuradj, "Adaptive Software Fault Prediction Approach Using Object-Oriented Metrics" (2012). FIU Electronic Theses and Dissertations. 767.
https://digitalcommons.fiu.edu/etd/767
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