As a part of a research project concerning software maintainability assessment in collaboration with the development team, we wanted to explore dissensions between developers and the confounding effect of size. To this end, this study replicated and extended a recent study from Schnappinger et al. with the public part of its dataset and the metrics extracted from the graph-based tool Javanalyser. The entire processing pipeline was automated, from metrics extraction to the training of machine learning models. The study was extended by predicting the continuous maintainability to take account of dissensions. Then, all experimental shots were duplicated to evaluate the overall influence of the class size. In the end, the original study was successfully replicated. Moreover, good performance was achieved on the continuous maintainability prediction. Finally, the class size was not sufficient for fine-grained maintainability prediction. This study shows the necessity to explore the nature of what is measured by code metrics, and is also the first step in the construction of a maintainability model.

As a part of a research project concerning software maintainability assessment in collaboration with the development team, we were interested in the frequent use of metrics as predictors. Many metrics exist, often with opaque and arguable implementations. We claim metrics mix the assessment of presentation, structure and model. In order to focus on true detectable maintainability defects, we computed metrics solely based on the structure of the program. Our approach was to parse the source code of Java programs as a graph, and to compute metrics in a declarative query language. To this end, we developed Javanalyser and implemented 34 metrics using Spoon to parse Java programs and Neo4j as graph database. We will show that the program graph constitutes a steady basis to compute metrics and conduct future machine-learning studies to assess maintainability.