一句话：提出 MultIPAs，用程序变换为入门编程作业生成多样化实现，以增广自动程序修复数据集。
公开的入门编程作业（IPA）数据集通常规模小且缺少缺陷标注，难以支撑依赖机器学习的自动程序修复工具；同时，多样化的正确实现有助于为给定错误程序计算更小的修复集合，而非总是复用同一组实现。为满足 IPA 基准增广需求，本文提出 MultIPAs，将程序变换应用于入门编程作业以进行数据增广。该方法通过程序变换生成更多样的实现，扩大基准规模并提升正确实现多样性，从而为修复工具提供更丰富的数据。摘要未给出具体实验评估或定量结果。
There has been a growing interest, over the last few years, in the topic of automated program repair applied to fixing introductory programming assignments (IPAs). However, the datasets of IPAs publicly available tend to be small and with no valuable annotations about the defects of each program. Small datasets are not very useful for program repair tools that rely on machine learning models. Furthermore, a large diversity of correct implementations allows computing a smaller set of repairs to fix a given incorrect program rather than always using the same set of correct implementations for a given IPA. For these reasons, there has been an increasing demand for the task of augmenting IPAs benchmarks.
DOI 原文 ·
@inproceedings{paperbot7104,
title = {MultIPAs: applying program transformations to introductory programming assignments for data augmentation},
author = {Pedro Orvalho and Mikoláš Janota and Vasco Manquinho},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3558931}
}