MultIPAs: applying program transformations to introductory programming assignments for data augmentation

Abstract

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.