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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00364210" target="_blank" >RIV/68407700:21730/22:00364210 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1145/3540250.3558931" target="_blank" >https://doi.org/10.1145/3540250.3558931</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3540250.3558931" target="_blank" >10.1145/3540250.3558931</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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. This paper presents MultIPAs, a program transformation tool that can augment IPAs benchmarks by: (1) applying six syntactic mutations that conserve the program's semantics and (2) applying three semantic mutilations that introduce faults in the IPAs. Moreover, we demonstrate the usefulness of MultIPAs by augmenting with millions of programs two publicly available benchmarks of programs written in the C language, and also by generating an extensive benchmark of semantically incorrect programs.

Název v anglickém jazyce

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

Popis výsledku anglicky

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. This paper presents MultIPAs, a program transformation tool that can augment IPAs benchmarks by: (1) applying six syntactic mutations that conserve the program's semantics and (2) applying three semantic mutilations that introduce faults in the IPAs. Moreover, we demonstrate the usefulness of MultIPAs by augmenting with millions of programs two publicly available benchmarks of programs written in the C language, and also by generating an extensive benchmark of semantically incorrect programs.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

ISBN

978-1-4503-9413-0

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1657-1661

Název nakladatele

ACM

Místo vydání

New York

Místo konání akce

Singapur

Datum konání akce

14. 11. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001118262900146