Author name cluster

Xuan Huo

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4 papers

1 author row

AAAI Conference 2020 Conference Paper

Control Flow Graph Embedding Based on Multi-Instance Decomposition for Bug Localization

Xuan Huo
Ming Li
Zhi-Hua Zhou

During software maintenance, bug report is an effective way to identify potential bugs hidden in a software system. It is a great challenge to automatically locate the potential buggy source code according to a bug report. Traditional approaches usually represent bug reports and source code from a lexical perspective to measure their similarities. Recently, some deep learning models are proposed to learn the uniﬁed features by exploiting the local and sequential nature, which overcomes the difﬁculty in modeling the difference between natural and programming languages. However, only considering local and sequential information from one dimension is not enough to represent the semantics, some multi-dimension information such as structural and functional nature that carries additional semantics has not been well-captured. Such information beyond the lexical and structural terms is extremely vital in modeling program functionalities and behaviors, leading to a better representation for identifying buggy source code. In this paper, we propose a novel model named CG-CNN, which is a multi-instance learning framework that enhances the uni- ﬁed features for bug localization by exploiting structural and sequential nature from the control ﬂow graph. Experimental results on widely-used software projects demonstrate the effectiveness of our proposed CG-CNN model.

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AAAI Conference 2019 Conference Paper

Automatic Code Review by Learning the Revision of Source Code

Shu-Ting Shi
Ming Li
David Lo
Ferdian Thung
Xuan Huo

Code review is the process of manual inspection on the revision of the source code in order to find out whether the revised source code eventually meets the revision requirements. However, manual code review is time-consuming, and automating such the code review process will alleviate the burden of code reviewers and speed up the software maintenance process. To construct the model for automatic code review, the characteristics of the revisions of source code (i. e. , the difference between the two pieces of source code) should be properly captured and modeled. Unfortunately, most of the existing techniques can easily model the overall correlation between two pieces of source code, but not for the “difference” between two pieces of source code. In this paper, we propose a novel deep model named DACE for automatic code review. Such a model is able to learn revision features by contrasting the revised hunks from the original and revised source code with respect to the code context containing the hunks. Experimental results on six open source software projects indicate by learning the revision features, DACE can outperform the competing approaches in automatic code review.

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IJCAI Conference 2017 Conference Paper

Enhancing the Unified Features to Locate Buggy Files by Exploiting the Sequential Nature of Source Code

Xuan Huo
Ming Li

Bug reports provide an effective way for end-users to disclose potential bugs hidden in a software system, while automatically locating the potential buggy source files according to a bug report remains a great challenge in software maintenance. Many previous approaches represent bug reports and source code from lexical and structural information correlated their relevance by measuring their similarity, and recently a CNN-based model is proposed to learn the unified features for bug localization, which overcomes the difficulty in modeling natural and programming languages with different structural semantics. However, previous studies fail to capture the sequential nature of source code, which carries additional semantics beyond the lexical and structural terms and such information is vital in modeling program functionalities and behaviors. In this paper, we propose a novel model LS-CNN, which enhances the unified features by exploiting the sequential nature of source code. LS-CNN combines CNN and LSTM to extract semantic features for automatically identifying potential buggy source code according to a bug report. Experimental results on widely-used software projects indicate that LS-CNN significantly outperforms the state-of-the-art methods in locating buggy files.

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IJCAI Conference 2016 Conference Paper

Learning Unified Features from Natural and Programming Languages for Locating Buggy Source Code

Xuan Huo
Ming Li
Zhi-Hua Zhou

Bug reports provide an effective way for end-users to disclose potential bugs hidden in a software system, while automatically locating the potential buggy source code according to a bug report remains a great challenge in software maintenance. Many previous studies treated the source code as natural language by representing both the bug report and source code based on bag-of-words feature representations, and correlate the bug report and source code by measuring similarity in the same lexical feature space. However, these approaches fail to consider the structure information of source code which carries additional semantics beyond the lexical terms. Such information is important in modeling program functionality. In this paper, we propose a novel convolutional neural network NP-CNN, which leverages both lexical and program structure information to learn unified features from natural language and source code in programming language for automatically locating the potential buggy source code according to bug report. Experimental results on widely-used software projects indicate that NP-CNN significantly outperforms the state-of-the-art methods in locating the buggy source files.

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