  Mistakes in binary conditions are a source of error in many software systems.
They happen when developers use, e.g., < or > instead of <= or >=. These
boundary mistakes are hard to find and impose manual, labor-intensive work for
software developers. While previous research has been proposing solutions to
identify errors in boundary conditions, the problem remains open. In this
paper, we explore the effectiveness of deep learning models in learning and
predicting mistakes in boundary conditions. We train different models on
approximately 1.6M examples with faults in different boundary conditions. We
achieve a precision of 85% and a recall of 84% on a balanced dataset, but lower
numbers in an imbalanced dataset. We also perform tests on 41 real-world
boundary condition bugs found from GitHub, where the model shows only a modest
performance. Finally, we test the model on a large-scale Java code base from
Adyen, our industrial partner. The model reported 36 buggy methods, but none of
them were confirmed by developers.
