hwadish.blogg.se - Broken code box set

Apply the fixer to the set of bad code, which consists of real code errors made by humans, and use the critic to assess if the fixer’s output is good.Initial fixer, which potentially is trained on synthetic dataīIFI then improves the fixer by performing the following cycle of data generation and training procedure:.The basic idea of BIFI is to introduce a machine learning-based breaker that learns to corrupt good code into realistic bad code, and iteratively train both the fixer and the breaker while using them in conjunction to generate more realistic paired data. Our goal is to use unlabeled data and critic to learn a fixer. repairing missing parenthesis as in the figure below. What we want is a fixer that repairs bad code into good code that satisfies the critic, e.g. The code is bad if there is at least one error and it is good if there are no errors. In code repair, we are given a critic that assesses the quality of an input: for instance, a compiler or code analyzer that tells us if input code has any syntax errors. In our recent work 4 published at ICML 2021, we study how to leverage unlabeled data to learn code fixers effectively. An unsupervised approach for training code repair models would make them much more scalable and widely deployable. On the other hand, unlabeled (unaligned) data-not aligned as (broken, fixed) pairs-is readily available: for example, raw code snippets on the web like GitHub. However, collecting this data for even a single programming language is costly, much less the dozens of languages commonly used in practice. If the content within contains spoilers, please also apply a spoilers tag.Recent works 3 use machine learning models to fix code errors by training the models on human-labeled (broken code, fixed code) pairs.

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