MiSFIT: Mining Software Fault Information and Types

As software becomes more important to society, the number, age, and complexity of systems grow. Software organizations require continuous process improvement to maintain the reliability, security, and quality of these software systems. Software organizations can utilize data from manual fault classification to meet their process improvement needs, but organizations lack the expertise or resources to implement them correctly. This dissertation addresses the need for the automation of software fault classification. Validation results show that automated fault classification, as implemented in the MiSFIT tool, can group faults of similar nature. The resulting classifications result in good agreement for common software faults with no manual effort. To evaluate the method and tool, I develop and apply an extended change taxonomy to classify the source code changes that repaired software faults from an open source project. MiSFIT clusters the faults based on the changes. I manually inspect a random sample of faults from each cluster to validate the results. The automatically classified faults are used to analyze the evolution of a software application over seven major releases. The contributions of this dissertation are an extended change taxonomy for software fault analysis, a method to cluster faults by the syntax of the repair, empirical evidence that fault distribution varies according to the purpose of the module, and the identification of project-specific trends from the analysis of the changes