# sk_p: a neural program corrector for MOOCs

arXiv: https://arxiv.org/abs/1607.02902 · HTML: https://mlsanity.com/paper/1607.02902/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2016.

## Built on by (most cited first, 10 of 10)

- [Break-It-Fix-It: Unsupervised Learning for Program Repair](https://mlsanity.com/paper/2106.06600/) (2021)
- [Repair Is Nearly Generation: Multilingual Program Repair with LLMs](https://mlsanity.com/paper/2208.11640/) (2022)
- [Repairing Bugs in Python Assignments Using Large Language Models](https://mlsanity.com/paper/2209.14876/) (2022)
- [Counterexample Guided Program Repair Using Zero-Shot Learning and MaxSAT-based Fault Localization](https://mlsanity.com/paper/2502.07786/) (2024)
- [Variable Name Recovery in Decompiled Binary Code using Constrained Masked Language Modeling](https://mlsanity.com/paper/2103.12801/) (2021)
- [MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation](https://mlsanity.com/paper/2210.03003/) (2022)
- [The Impact of Fine-tuning Large Language Models on Automated Program Repair](https://mlsanity.com/paper/2507.19909/) (2025)
- [Program Repair with Minimal Edits Using CodeT5](https://mlsanity.com/paper/2309.14760/) (2023)
- [A Study of Variable-Role-based Feature Enrichment in Neural Models of Code](https://mlsanity.com/paper/2303.04942/) (2023)
- [Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Assignments](https://mlsanity.com/paper/2404.01754/) (2024)

## Abstract (arXiv)

We present a novel technique for automatic program correction in MOOCs, capable of fixing both syntactic and semantic errors without manual, problem specific correction strategies. Given an incorrect student program, it generates candidate programs from a distribution of likely corrections, and checks each candidate for correctness against a test suite. The key observation is that in MOOCs many programs share similar code fragments, and the seq2seq neural network model, used in the natural-language processing task of machine translation, can be modified and trained to recover these fragments. Experiment shows our scheme can correct 29% of all incorrect submissions and out-performs state of the art approach which requires manual, problem specific correction strategies.