Automating aggregation for software quality modeling

Software Quality model is a well-accepted way for assessing high-level quality characteristics (e.g., maintainability) by aggregation from low-level metrics. Aggregation method in a software quality model denotes how to aggregate low-level metrics to high-level quality characteristics. Most of the existing quality models adopt the weighted linear aggregation method. The main drawback of weighted linear method is that it 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 leverage 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 probabilistic relationship without manual effort. To evaluate the effectiveness of proposed aggregation approach, we conduct an empirical study on assessing one typical high-level quality characteristic (i.e., maintainability) which is regarded as an important characteristic defined in ISO 9126. The achieved results on 10 open source projects with totally 269 versions show that our method can reveal maintainability well and it outperforms a weighted linear aggregation method baseline in most of the projects.