Bullard, Lofton A.

In this dissertation we address two significant issues of concern. These are software
quality modeling and data quality assessment. Software quality can be measured by software
reliability. Reliability is often measured in terms of the time between system failures. A
failure is caused by a fault which is a defect in the executable software product. The time
between system failures depends both on the presence and the usage pattern of the software.
Finding faulty components in the development cycle of a software system can lead to a
more reliable final system and will reduce development and maintenance costs. The issue of
software quality is investigated by proposing a new approach, rule-based classification model
(RBCM) that uses rough set theory to generate decision rules to predict software quality.
The new model minimizes over-fitting by balancing the Type I and Type II niisclassiflcation
error rates. We also propose a model selection technique for rule-based models called rulebased
model selection (RBMS). The proposed rule-based model selection technique utilizes
the complete and partial matching rule sets of candidate RBCMs to determine the model
with the least amount of over-fitting. In the experiments that were performed, the RBCMs
were effective at identifying faulty software modules, and the RBMS technique was able to
identify RBCMs that minimized over-fitting. Good data quality is a critical component for building effective software quality models.
We address the significance of the quality of data on the classification performance of learners
by conducting a comprehensive comparative study. Several trends were observed in the
experiments. Class and attribute had the greatest impact on the performance of learners
when it occurred simultaneously in the data. Class noise had a significant impact on the
performance of learners, while attribute noise had no impact when it occurred in less than
40% of the most significant independent attributes. Random Forest (RF100), a group of 100
decision trees, was the most, accurate and robust learner in all the experiments with noisy
data.

Software systems that control military radar systems must be highly reliable. A fault can compromise safety and security, and even cause death of military personnel. In this experiment we identify fault-prone software modules in a subsystem of a military radar system called the Joint Surveillance Target Attack Radar System, JSTARS. An earlier version was used in Operation Desert Storm to monitor ground movement. Product metrics were collected for different iterations of an operational prototype of the subsystem over a period of approximately three years. We used these metrics to train a decision tree model and to fit a discriminant model to classify each module as fault-prone or not fault-prone. The algorithm used to generate the decision tree model was TREEDISC, developed by the SAS Institute. The decision tree model is compared to the discriminant model.