Methodology for identifying factors that impact software quality

Abstract

Software quality has become an important concern in our daily lives. Understanding the factors that influence software quality is crucial to the software industry. An improved understanding of software quality drivers will help software engineers and managers make more informed decisions in controlling and improving the software process. While many of the studies on software quality have focused on the measurement aspects of software quality, very few have analyzed the factors that could influence the quality itself. One of the main reasons for this is the fact that the quality is measured in terms of ordinal or nominal data while the factors which are numerous, are measured in terms of indices or real numbers. Traditional techniques such as regression or analysis of variance which are generally used to measure the impact of different variables on the quality fail in such a situation. On the other hand, techniques such as artificial neural networks, logistic regression or classification trees work better with dependent variables which are categorical or ordinal. This paper demonstrates the use of these techniques to identify the factors that effect software quality and attempts to rank these factors in the order of their importance.