Automated Program Repair

For the running example in Figure 1, this abstraction would replace the application-specific identifiers triangle and EQUILATERAL with generic placeholders, such as VAR1 and VAR2. After this abstraction, both approaches use an RNN-based sequence-to-sequence network that predicts how to modify the abstracted code. Given the increasing interest in learning-based approaches toward software engineering problems, we will likely see more progress on learning-based repair in the coming years. Key challenges toward effective solutions include finding an appropriate representation of source code changes and obtaining large amounts of high-quality human patches as training data.