Predicting Unit Testing Effort Levels of Classes: An Exploratory Study based on Multinomial Logistic Regression Modeling

The study aims at investigating empirically the ability of a Quality Assurance Indicator (Qi), a metric that we proposed in a previous work, to predict different levels of unit testing effort of classes in object-oriented systems. To capture the unit testing effort of classes, we used four metrics to quantify various perspectives related to the code of corresponding unit test cases. Classes were classified, according to the involved unit testing effort, in five categories (levels). We collected data from two open source Java software systems (ANT and JFREECHART) for which JUnit test cases exist. In order to explore the ability of the Qi metric to predict different levels of the unit testing effort of classes, we decided to explore the possibility of using the Multinomial Logistic Regression (MLR) method. The performance of the Qi metric has been compared to the performance of three well-known source code metrics related respectively to size, complexity and coupling. Results suggest that the MLR model based on the Qi metric is able to accurately predict different levels of the unit testing effort of classes.