Title page for ETD etd-04142010-140421


Type of Document Dissertation
Author Luo, Yixin
URN etd-04142010-140421
Title Improving Software Quality Using An Ontology-based Approach
Degree Doctor of Philosophy (Ph.D.)
Department Computer Science
Advisory Committee
Advisor Name Title
Carver, Doris Committee Chair
Barbosa, Roberto Committee Member
Chen, Janhua Committee Member
Chen, Ye-sho Committee Member
Kraft, Donald Committee Member
Keywords
  • code smell
  • anti-pattern
  • refactoring
  • ontology
  • software quality
Date of Defense 2010-03-31
Availability unrestricted
Abstract
Ensuring quality in software development is a challenging process. The concepts of anti-pattern and bad code smells utilize the knowledge of reoccurring problems to improve the quality of current and future software development. Anti-patterns describe recurring bad design solutions while bad code smells describe source code that is error-free but difficult to understand and maintain. Code refactoring aims to remove bad code smells without changing a programís functionality while improving program quality. There are metrics-based tools to detect a few bad code smells from source code; however, the knowledge and understanding of these indicators of low quality software are still insufficient to resolve many of the problems they represent. Minimal research addresses the relationships between or among bad code smells, anti-patterns and refactoring. In this research, we present a new ontology, Ontology for Anti-patterns, Bad Code Smells and Refactoring (OABR), to define the concepts and their relation properties. Such an ontological infrastructure encourages a common understanding of these concepts among the software community and provides more concise definitions that help to avoid overlapping and inconsistent description. It utilizes reasoning capabilities associated with ontology to analyze the software development domain and offer new insights into the domain. Software quality issues such as understandability and maintainability can be improved by identifying and resolving anti-patterns associated with code smells as well as preventing bad code smells before coding begins.
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