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

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

11 Jun 2021arXiv:2106.06600archive 2025-07-28

Michihiro Yasunaga, Percy Liang

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. Based on these ideas, we iteratively update the breaker and the fixer while using them in conjunction to generate more paired data. We evaluate BIFI on two code repair datasets: GitHub-Python, a new dataset we introduce where the goal is to repair Python code with AST parse errors; and DeepFix, where the goal is to repair C code with compiler errors. BIFI outperforms existing methods, obtaining 90.5% repair accuracy on GitHub-Python (+28.5%) and 71.7% on DeepFix (+5.6%). Notably, BIFI does not require any labeled data; we hope it will be a strong starting point for unsupervised learning of various repair tasks.

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check_ast_error michiyasunaga/bifi/utils/code_error_checker.py official repository unverified MIT (permissive) · 7956f0c3ad623dc9 · report
code_toks_to_code_string michiyasunaga/bifi/utils/code_utils.py official repository unverified MIT (permissive) · d31293eebb3f2fbb · report
detect_encoding michiyasunaga/bifi/utils/code_tokenizer.py official repository unverified MIT (permissive) · 4e338ff6454175b7 · report
fairseq_generate michiyasunaga/bifi/utils/fairseq_utils.py official repository unverified MIT (permissive) · 24311a36d4c36789 · report
parse_fairseq_preds michiyasunaga/bifi/utils/fairseq_utils.py official repository unverified MIT (permissive) · 0b5ffc297fbdf5da · report
toks2lines michiyasunaga/bifi/utils/code_utils.py official repository unverified MIT (permissive) · 1661aeef734f01a4 · report

Tasks

C++ codeCode RepairData AugmentationDomain AdaptationProgram RepairStyle TransferUnsupervised Machine Translation

Datasets

Introduced by this paper, per the archive.

GitHub-Python

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Program Repair DeepFix DrRepair + BIFI Average Success Rate 71.7 #1 of 4 Archive leaderboard report
Program Repair GitHub-Python Transformer + BIFI Accuracy (%) 90.5 #1 of 2 Archive leaderboard report
Program Repair GitHub-Python Transformer Accuracy (%) 62.0 #2 of 2 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerRepairResidual ConnectionSoftmaxTransformer

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