A comparison between software design and code metrics for the prediction of software fault content
Software metrics play an important role in measuring the quality of software. It is desirable to predict the quality of software as early as possible, and hence metrics have to be collected early as well. This raises a number of questions that has not been fully answered. In this paper we discuss, prediction of fault content and try to answer what type of metrics should be collected, to what extent design metrics can be used for prediction, and to what degree prediction accuracy can be improved if code metrics are included. Based on a data set collected from a real project, we found that both design and code metrics are correlated with the number of faults. When the metrics are used to build prediction models of the number of faults, the design metrics are as good as the code metrics, little improvement can be achieved if both design metrics and code metrics are used to model the relationship between the number of faults and the software metrics. The empirical results from this study indicate that the structural properties of the software influencing the fault content is established before the coding phase.
software metrics correlation analysis fault prediction metric selection regression analysis