Bug Localization with Combination of Deep Learning and Information Retrieval

Lam, An Ngoc; Nguyen, Anh Tuan; Nguyen, Hoan Anh; Nguyen, Tien N.

doi:10.1109/icpc.2017.24

Cited by 136 publications

(121 citation statements)

References 45 publications

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“…The embedding vectors are concatenated and then used to compute a prediction score for the patch. Different from existing deep learning techniques working on the source code [16], [17], [24], [36], [44], [66], [68], our hierarchical deep learning-based architecture takes into account the structure of code changes (i.e., files, hunks, lines) and the sequential nature of source code (by considering each line of code as a sequence of words) to predict stable patches in the Linux kernel.…”

Section: Resultsmentioning

confidence: 99%

“…The DBN learns a semantic representation (in the form of a continuous-valued vector) of each source code file from token vectors extracted from programs' ASTs. Lam et al combined deep learning with information retrieval to localize buggy files based on bug reports [36]. Bui and Jiang proposed a deep learning based approach to automatically learn cross-language representations for various kinds of structural code elements (i.e., expressions, statements, and methods) for program translation [8].…”

Section: Related Workmentioning

confidence: 99%

“…The key challenge is to accurately represent the structure of code changes, which are not contiguous text like the commit message, but rather amount to scattered fragments of removed and added code across multiple files, within multiple hunks. Thus, different from existing deep learning techniques working on source code [24], [36], [66], [68], PatchNet constructs separate embedding vectors representing the removed code and the added code in each hunk of each affected file in the given patch. The information about a file's hunks are then concatenated to build an embedding vector for the affected file.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

PatchNet: Hierarchical Deep Learning-Based Stable Patch Identification for the Linux Kernel

Hoang

Lawall²,

Tian³

et al. 2021

IIEEE Trans. Software Eng.

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Linux kernel stable versions serve the needs of users who value stability of the kernel over new features. The quality of such stable versions depends on the initiative of kernel developers and maintainers to propagate bug fixing patches to the stable versions. Thus, it is desirable to consider to what extent this process can be automated. A previous approach relies on words from commit messages and a small set of manually constructed code features. This approach, however, shows only moderate accuracy. In this paper, we investigate whether deep learning can provide a more accurate solution. We propose PatchNet, a hierarchical deep learning-based approach capable of automatically extracting features from commit messages and commit code and using them to identify stable patches. PatchNet contains a deep hierarchical structure that mirrors the hierarchical and sequential structure of commit code, making it distinctive from the existing deep learning models on source code. Experiments on 82,403 recent Linux patches confirm the superiority of PatchNet against various state-of-the-art baselines, including the one recently-adopted by Linux kernel maintainers.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PatchNet: Hierarchical Deep Learning-Based Stable Patch Identification for the Linux Kernel

Hoang

Lawall²,

Tian³

et al. 2021

IIEEE Trans. Software Eng.

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“…In recent years some researchers have proposed deep-learning-based models to localize bugs. Lam et al [29,28] and Huo et al [20,19] proposed deep learning models based on RBM, CNN, LSTM. Their work proved that, unlike the other state-of-the-art traditional models, the deep learning models are able to minimize the lexical gap between bug reports and source files.…”

Section: Rq1: Minimizing the Lexical Gap Between Bug Reports And Sourmentioning

confidence: 99%

“…In the past few years, different architectures of Deep Neural Nets (DNN) have been proposed to localize bugs. The motivation behind our study is based on the fact that these deeplearning-based models have shown to outperform the state-of-the-art traditional ML-based bug localization models [20,29,28,19]. Hence, our primary objective is to examine the effectiveness of these models, in meeting the expectations of the software practitioner.…”

Section: Introductionmentioning

confidence: 99%

On Usefulness of the Deep-Learning-Based Bug Localization Models to Practitioners

Polisetty

Miranskyy

Başar

2019

Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering

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Background : Developers spend a significant amount of time and efforts to localize bugs. In the literature, many researchers proposed state-of-the-art bug localization models to help developers localize bugs easily. The practitioners, on the other hand, expect a bug localization tool to meet certain criteria, such as trustworthiness, scalability, and efficiency. The current models are not capable of meeting these criteria, making it harder to adopt these models in practice. Recently, deep-learning-based bug localization models have been proposed in the literature, which show a better performance than the state-of-the-art models.Aim: In this research, we would like to investigate whether deep learning models meet the expectations of practitioners or not.Method : We constructed a Convolution Neural Network and a Simple Logistic model to examine their effectiveness in localizing bugs. We train these models on five open source projects written in Java and compare their performance with the performance of other state-of-the-art models trained on these datasets.Results: Our experiments show that although the deep learning models perform better than classic machine learning models, they meet the adoption criteria set by the practitioners only partially.Conclusions: This work provides evidence that the practitioners should be cautious while using the current state of the art models for production-level use-cases. It also highlights the need for standardization of performance benchmarks to ensure that bug localization models are assessed equitably and realistically.

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Multi‐graph learning‐based software defect location

Yin

Shi

Zhao

et al. 2023

J Software Evolu Process

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Software quality is key to the success of software systems. Modern software systems are growing in their worth based on industry needs and becoming more complex, which inevitably increases the possibility of more defects in software systems. Software repairing is time‐consuming, especially locating the source files related to specific software defect reports. To locate defective source files more quickly and accurately, automated software defect location technology is generated and has a huge application value. The existing deep learning‐based software defect location method focuses on extracting the semantic correlation between the source file and the corresponding defect reports. However, the extensive code structure information contained in the source files is ignored. To this end, we propose a software defect location method, namely, multi‐graph learning‐based software defect location (MGSDL). By extracting the program dependency graphs for functions, each source file is converted into a graph bag containing multiple graphs (i.e., multi‐graph). Further, a multi‐graph learning method is proposed, which learns code structure information from multi‐graph to establish the semantic association between source files and software defect reports. Experiments' results on four publicly available datasets, AspectJ, Tomcat, Eclipse UI, and SWT, show that MGSDL improves on average 3.88%, 5.66%, 13.23%, 9.47%, and 3.26% over the competitive methods in five evaluation metrics, rank@10, rank@5, MRR, MAP, and AUC, respectively.

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Bug Localization with Combination of Deep Learning and Information Retrieval

Cited by 136 publications

References 45 publications

PatchNet: Hierarchical Deep Learning-Based Stable Patch Identification for the Linux Kernel

PatchNet: Hierarchical Deep Learning-Based Stable Patch Identification for the Linux Kernel

On Usefulness of the Deep-Learning-Based Bug Localization Models to Practitioners

Multi‐graph learning‐based software defect location

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