Break-It-Fix-It: Unsupervised Learning for Program Repair

Understand

We consider repair tasks: given a critic (e.g., compiler) that assesses the quality of an input, the goal is to train a fixer that converts a bad example (e.g., code with syntax errors) into a good one (e.g., code with no syntax errors).

• Existing works create training data consisting of (bad, good) pairs by corrupting good examples using heuristics (e.g., dropping tokens).
• However, fixers trained on this synthetically-generated data do not extrapolate well to the real distribution of bad inputs.
• To bridge this gap, we propose a new training approach, Break-It-Fix-It (BIFI), which has two key ideas: (i) we use the critic to check a fixer's output on real bad inputs and add good (fixed) outputs to the training data, and (ii) we train a breaker to generate realistic bad code from good code.