WELL: Applying bug detectors to bug localization via weakly supervised learning-Reference-Cited by-同舟云学术

WELL: Applying bug detectors to bug localization via weakly supervised learning

Published:2024-04-23 Issue: Volume: Page:
ISSN:2047-7473
Container-title:Journal of Software: Evolution and Process
language:en
Short-container-title:J Software Evolu Process

Author:

Zhang Huangzhao¹^ORCID,Li Zhuo¹^ORCID,Li Jia¹,Jin Zhi¹,Li Ge¹

Affiliation:

1. Key Laboratory of High Confidence Software Technologies Peking University Beijing China

Abstract

AbstractBug localization, which is used to help programmers identify the location of bugs in source code, is an essential task in software development. Researchers have already made efforts to harness the powerful deep learning (DL) techniques to automate it. However, training bug localization model is usually challenging because it requires a large quantity of data labeled with the bug's exact location, which is difficult and time‐consuming to collect. By contrast, obtaining bug detection data with binary labels of whether there is a bug in the source code is much simpler. This paper proposes a WEakly supervised bug LocaLization (WELL) method, which only uses the bug detection data with binary labels to train a bug localization model. With CodeBERT finetuned on the buggy‐or‐not binary labeled data, WELL can address bug localization in a weakly supervised manner. The evaluations on three method‐level synthetic datasets and one file‐level real‐world dataset show that WELL is significantly better than the existing state‐of‐the‐art model in typical bug localization tasks such as variable misuse and other bugs.

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/smr.2669

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