Kavli Affiliate: Zhuo Li
| First 5 Authors: Zhuo Li, Huangzhao Zhang, Zhi Jin, Ge Li,
| Summary:
Bug 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 SOTA model in typical bug localization tasks such as variable
misuse and other programming bugs.
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