Two papers accepted at Empirical Software Engineering

The paper A Longitudinal Study of Static Analysis Warning Evolution and the Effects of PMD on Software Quality in Apache Open Source Projects is joint work by Alexander Trautsch, Steffen Herbold, and Jens Grabowski and addresses research questions regarding the evolution of the internal software code quality with respect to warning of static analysis tools. Our study shows that, on average, the code quality improved in the last two decades, mainly due to better coding guidelines, that, e.g., prohibit if-statements without an explicit if-block with {}. These quality improvements seem to be largely independent from the active use of static analysis tools within the build pipeline the projects. 

The paper On the Feasibility of Automated Prediction of Bug and Non-Bug Issues is joint work by Steffen Herbold, Alexander Trautsch, and Fabian Trautsch and deals with the question if machine learning can help to identify which issue reports describe bugs. We find that state-of-the-art models are almost as accurate as software developers and that such models, especially if trained on huge amounts of data, tend have a very high recall, i.e., hardly miss bugs.