Leveraging data characteristics for bug localization in deep learning programs
Date
2024-08
Authors
Manke, Ruchira
Major Professor
Advisor
Rajan, Hridesh
Prabhu, Gurpur
Mitra, Simanta
Committee Member
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Altmetrics
Abstract
Deep Learning (DL) is a class of machine learning algorithms that are used in a wide variety of
applications. Like any software system, DL programs may have bugs. To support bug localization
in DL programs, several tools have been proposed in the past. As most of the bugs that occur
due to improper model structure known as structural bugs lead to inadequate performance during
training, it is challenging for developers to identify the root cause and address these bugs. To
support bug detection and localization in DL programs, in this work, we propose Theia, which
detects and localizes structural bugs in DL programs. Unlike the previous works, Theia considers
the training dataset characteristics to automatically detect bugs in DL programs developed using
two deep learning libraries, Keras and PyTorch. Since training the DL models is a time-consuming
process, Theia detects these bugs at the beginning of the training process and alerts the developer
with informative messages containing the bug’s location and actionable fixes which will help them to
improve the structure of the model. We evaluated Theia on a benchmark of 40 real-world buggy DL
programs obtained from Stack Overflow. Our results show that Theia successfully localizes 57/75
structural bugs in 40 buggy programs, whereas NeuraLint, a state-of-the-art approach capable of
localizing structural bugs before training localizes 17/75 bugs.
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