FixEval: Execution-based Evaluation of Program Fixes for Programming Problems

The complexity of modern software has led to a drastic increase in the time and cost associated with detecting and rectifying software bugs. In response, researchers have explored various methods to automatically generate fixes for buggy code. However, due to the large combinatorial space of possibl...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Md Mahim Anjum Haque, Wasi Uddin Ahmad, Lourentzou, Ismini, Brown, Chris
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 30.03.2023
Subjects
Combinatorial analysis
Evaluation
Fixing
Programming languages
Software testing
Source code
Online AccessGet full text

Cover

More Information
Summary:The complexity of modern software has led to a drastic increase in the time and cost associated with detecting and rectifying software bugs. In response, researchers have explored various methods to automatically generate fixes for buggy code. However, due to the large combinatorial space of possible fixes for any given bug, few tools and datasets are available to evaluate model-generated fixes effectively. To address this issue, we introduce FixEval, a benchmark comprising of buggy code submissions to competitive programming problems and their corresponding fixes. FixEval offers an extensive collection of unit tests to evaluate the correctness of model-generated program fixes and assess further information regarding time, memory constraints, and acceptance based on a verdict. We consider two Transformer language models pretrained on programming languages as our baseline and compare them using match-based and execution-based evaluation metrics. Our experiments show that match-based metrics do not reflect model-generated program fixes accurately. At the same time, execution-based methods evaluate programs through all cases and scenarios designed explicitly for that solution. Therefore, we believe FixEval provides a step towards real-world automatic bug fixing and model-generated code evaluation. The dataset and models are open-sourced at https://github.com/mahimanzum/FixEval.
ISSN:2331-8422