Learning to aggregate: an automated aggregation method for software quality model

Quality models are regarded as a well-accepted approach for assessing high-level abstract quality characteristics (e.g., maintainability) by aggregation from low-level metrics. However, most of the existing quality models adopt the weighted linear aggregation method which suffers from a lack of consensus in how to decide the correct weights. To address this issue, we present an automated aggregation method which adopts a kind of probabilistic weight instead of the subjective weight in previous aggregation methods. In particular, we utilize a topic modeling technique to estimate the probabilistic weight by learning from a software benchmark. In this manner, our approach can enable automated quality assessment by using the learned knowledge without manual effort. In addition, we conduct an application on the maintainability assessment of the systems in our benchmark. The result shows that our approach can reveal the maintainability well through a correlation analysis with the changed lines of code.