  Context: An increasing number of software systems are written in multiple
programming languages (PLs), which are called multi-programming-language (MPL)
systems. MPL bugs (MPLBs) refers to the bugs whose resolution involves multiple
PLs. Despite high complexity of MPLB resolution, there lacks MPLB prediction
methods. Objective: This work aims to construct just-in-time (JIT) MPLB
prediction models with selected prediction metrics, analyze the significance of
the metrics, and then evaluate the performance of cross-project JIT MPLB
prediction. Method: We develop JIT MPLB prediction models with the selected
metrics using machine learning algorithms and evaluate the models in
within-project and cross-project contexts with our constructed dataset based on
18 Apache MPL projects. Results: Random Forest is appropriate for JIT MPLB
prediction. Changed LOC of all files, added LOC of all files, and the total
number of lines of all files of the project currently are the most crucial
metrics in JIT MPLB prediction. The prediction models can be simplified using a
few top-ranked metrics. Training on the dataset from multiple projects can
yield significantly higher AUC than training on the dataset from a single
project for cross-project JIT MPLB prediction. Conclusions: JIT MPLB prediction
models can be constructed with the selected set of metrics, which can be
reduced to build simplified JIT MPLB prediction models, and cross-project JIT
MPLB prediction is feasible.
