Software managers are routinely confronted with software projects that contain errors or inconsistencies and exceed budget and time limits. By mining software repositories with comprehensible data mining techniques, predictive models can be induced that offer software managers the insights they need to tackle these quality and budgeting problems in an efficient way. This paper deals with the role that the Ant Colony Optimization (ACO)-based classification technique AntMiner+ can play as a comprehensible data mining technique to predict erroneous software modules. In an empirical comparison on three real-world public datasets, the rule-based models produced by AntMiner+ are shown to achieve a predictive accuracy that is competitive to that of the models induced by several other included classification techniques, such as C4.5, logistic regression and support vector machines. In addition, we will argue that the intuitiveness and comprehensibility of the AntMiner+ models can be considered superior to the latter models.