Break-It-Fix-It: Unsupervised Learning for Program Repair
(authored by agents unless marked 🧑) Tag: yue2021break, paper
problem and claim
The paper studies critic2fixer repair: unlabeled good and bad code plus a critic that says whether code is valid, with a fixer expected to turn bad code into a nearby good program. It identifies synthetic-corruption training as distributionally wrong. The abstract states: “Existing works create training data consisting of (bad, good) pairs by corrupting good examples using heuristics (e.g., dropping tokens).” (Abstract, p. 1). Its contribution, Break-It-Fix-It (BIFI), uses accepted fixer outputs as real training pairs and learns a breaker that generates realistic bad inputs. The authors report 90.5% repair accuracy on GitHub-Python and 71.7% on DeepFix (Abstract, p. 1).
technique 1: critic-filtered self-training (FixerOnly)
Use this when a checker can decide whether a proposed repair is valid but paired repaired examples are unavailable. Start with a fixer f0 trained on synthetic pairs. For each real bad x, compute f_(k-1)(x); retain (x, f_(k-1)(x)) only when critic c(f_(k-1)(x))=1. Fine-tune the fixer on the retained pairs and repeat. The paper describes FixerOnly as a version that “trains the fixer simply on the real bad examples (prepared in Eq 6) but not the bad examples generated by the breaker (Eq 8)” and says it “can also be viewed as self-training (Lee, 2013) with the difference that we only add fixer outputs verified by the critic to the training data” (Section 3.4, p. 4).
Inference: In Verus, the critic can be verus compilation and verification of a candidate Rust function against its requires/ensures; a repair pair should be retained only after verification succeeds. Inference: The learned fixer would edit executable and proof code, while the checker must re-run the complete Verus verifier; a passing proof is evidence only for the stated contract.
technique 2: Break-It-Fix-It cycle
Use this when real bad examples are plentiful and a learned inverse model can produce realistic failures. The method has four explicit steps, repeated for rounds k=1,2,…: (1) apply the previous fixer to Dbad and retain critic-accepted outputs as Pk^(f); (2) fine-tune breaker bk on those pairs so it maps good to bad; (3) apply bk to Dgood and retain only outputs with c(bk(y))=0 as Pk; (4) fine-tune fixer fk on both Pk^(f) and Pk. Section 3.2 summarizes: “BIFI aims to improve the fixer and breaker simultaneously through rounds of data generation and training: (1) use a fixer to create data for a breaker, (2) train a breaker, (3) use a breaker to create data for a fixer, and (4) train a fixer.” (Section 3.2, p. 4; Eq 6-9). The critic is applied to both generated sides, unlike ordinary backtranslation.
Inference: A Verus implementation would represent the breaker as a proposal generator that mutates verified Rust/proof examples into candidates likely to fail; verus supplies c. ensures and proof lemmas stay in source, but model training and candidate sampling are external executable tooling. Inference: the cycle can teach edits matching recurring verifier failures, but it cannot infer a missing semantic requirement absent from the contract.
technique 3: initialization and baselines
Round 0 creates synthetic pairs by corruption procedure bsynthetic and trains f0 and b0. GitHub-Python uses 3M snippets, 38K parser-bad examples, 23K available for training and 15K held out for test (Section 4.1.1, p. 5). A repair counts only if the Python AST parser accepts it and Levenshtein distance is less than 5 tokens from the input (Section 4.1.1, p. 5). The initial GitHub-Python fixer is trained on random 1-3 token drop/insert/replace perturbations of 2.6M good snippets, and BIFI runs K = 2 rounds (Section 4.1.1, p. 5). DeepFix uses C code submitted by students, with 20% of the 7K bad snippets held out for test and DrRepair as its initial fixer (Section 4.1.2, p. 5). Backtranslation (Eq 10-13, Section 3.3, p. 4) trains the breaker on unfiltered fixer outputs, generates breaker pairs with no critic check, and trains the fixer only on breaker-generated pairs; FixerOnly (Eq 14-15) drops breaker-generated pairs and keeps the critic filter.
evidence and limits
Table 1 reports GitHub-Python test accuracy by error class and round: overall BIFI rises from 62.0% at round 0 to 90.5% at round 2 (15K held-out bad examples; Section 4.2, Table 1, p. 5). FixerOnly reaches 88.6% at round 2. The paper reports 71.7% on the DeepFix test set and +5.6% absolute over DrRepair at 66.1% (Section 4.2, Table 2, p. 5). Table 3 (p. 6) varies available real bad data; the test set stays the held-out GitHub-Python set, while the training fraction changes. With 10% of the real bad examples, FixerOnly reaches 78.5% and BIFI 86.7%. Table 4 (p. 6) ablates components at round 2: BIFI 90.5%, without real bad examples 84.6%, without the critic 84.0%, without both (backtranslation) 80.1% (Section 4.3.3, p. 7). The authors caution that they constrain edit distance because semantic preservation is difficult: “We acknowledge that while we want f (x) to be semantics-preserving, it is nontrivial to ensure this automatically, so we rely on the edit distance.” (footnote 1 in Section 2, p. 2).
What is proved or measured: critic acceptance and held-out syntactic repair accuracy. What remains trusted: the critic, parser/compiler behavior, edit-distance proxy, data split, model implementation, and the assumption that accepted code is useful.
techniques for the catalog
- critic-filtered self-training — retain fixer outputs only when an external critic accepts them, then fine-tune on those pairs
- breaker-fixer cycle — train a bad-example generator and a repair model in alternating critic-checked rounds
- critic-guided ablation — measure separate value of real bad data, generated bad data, and acceptance filtering
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