Exploiting gated graph neural network for detecting and explaining self-admitted technical debts

Yu, Jiaojiao; Zhao, Kunsong; Liu, Jin; Liu, Xiao; Zhou, Xu; Wang, Xin

doi:10.1016/j.jss.2022.111219

Cited by 10 publications

(4 citation statements)

References 38 publications

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“…Recently, due to the powerful ability to model dependency relationships within a graph, GNNs have been widely applied in various deep learning tasks and applications 55–57 . Therefore, our model employs GCN to learn dependency information when encoding event types, as mentioned in subsection 4.3.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, due to the powerful ability to model dependency relationships within a graph, GNNs have been widely applied in various deep learning tasks and applications. [55][56][57] Therefore, our model employs GCN to learn dependency information when encoding event types, as mentioned in subsection 4.3. Moreover, due to the significant advantages of hyperbolic space in capturing hierarchical structures, we have also applied hyperbolic space in our model to learn the hierarchical relationships among event types.…”

Section: Ablation Studymentioning

confidence: 99%

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HyperED: A hierarchy‐aware network based on hyperbolic geometry for event detection

Zhang,

Xie,

Liu

et al. 2024

Computational Intelligence

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Event detection plays an essential role in the task of event extraction. It aims at identifying event trigger words in a sentence and classifying event types. Generally, multiple event types are usually well‐organized with a hierarchical structure in real‐world scenarios, and hierarchical correlations between event types can be used to enhance event detection performance. However, such kind of hierarchical information has received insufficient attention which can lead to misclassification between multiple event types. In addition, the most existing methods perform event detection in Euclidean space, which cannot adequately represent hierarchical relationships. To address these issues, we propose a novel event detection network HyperED which embeds the event context and types in Poincaré ball of hyperbolic geometry to help learn hierarchical features between events. Specifically, for the event detection context, we first leverage the pre‐trained BERT or BiLSTM in Euclidean space to learn the semantic features of ED sentences. Meanwhile, to make full use of the dependency knowledge, a GNN‐based model is applied when encoding event types to learn the correlations between events. Then we use a simple neural‐based transformation to project the embeddings into the Poincaré ball to capture hierarchical features, and a distance score in hyperbolic space is computed for prediction. The experiments on MAVEN and ACE 2005 datasets indicate the effectiveness of the HyperED model and prove the natural advantages of hyperbolic spaces in expressing hierarchies in an intuitive way.

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Section: Methodsmentioning

confidence: 99%

Section: Ablation Studymentioning

confidence: 99%

HyperED: A hierarchy‐aware network based on hyperbolic geometry for event detection

Zhang,

Xie,

Liu

et al. 2024

Computational Intelligence

Self Cite

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“…One of the threats to construct validity in the study concerns the potentially different interpretations of discussed topics between interviewees and researchers. Because we focus on SATD in this study and most Code Comments [6], [7], [12], [14], [15], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66] Issue Trackers [3], [12], [16] Commit Messages [12] Pull Requests [12] Automated Differentiation Between Fixed and Unfixed SATD -Automated Tracing Between SATD in Different Sources [11], [12], [36], [37] and Code and Related Development Tasks -Automated SATD Prioritization [9], [67], …”

Section: Threats To Validity 61 Construct Validitymentioning

confidence: 99%

Self-Admitted Technical Debt in the Embedded Systems Industry: An Exploratory Case Study

Soliman

Avgeriou

et al. 2023

IIEEE Trans. Software Eng.

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Technical debt denotes shortcuts taken during software development, mostly for the sake of expedience. When such shortcuts are admitted explicitly by developers (e.g., writing a TODO/Fixme comment), they are termed as Self-Admitted Technical Debt or SATD. There has been a fair amount of work studying SATD management in Open Source projects, but SATD in industry is relatively unexplored. At the same time, there is no work focusing on developers' perspectives towards SATD and its management. To address this, we conducted an exploratory case study in cooperation with an industrial partner to study how they think of SATD and how they manage it. Specifically, we collected data by identifying and characterizing SATD in different sources (issues, source code comments, and commits) and carried out a series of interviews with 12 software practitioners. The results show: 1) the core characteristics of SATD in industrial projects; 2) developers' attitudes towards identified SATD and statistics; 3) triggers for practitioners to introduce and repay SATD; 4) relations between SATD in different sources; 5) practices used to manage SATD; 6) challenges and tooling ideas for SATD management.

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“…proposed a graph matching network, which utilizes the CFG of the program to deal with the challenge of binary function similarity search Ling et al (2021). proposed a deep graph matching and searching model based on graph neural networks(Kipf and Welling, 2017; Wang et al, 2021b,a;Yu et al, 2022; for code retrieval. They represented both natural language queries and code snippets based on the unified graph-structured data Iyer et al (2020).…”

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confidence: 99%

CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training

Wang¹,

Wang²,

Wan³

et al. 2022

Preprint

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Recent years have witnessed increasing interest in code representation learning, which aims to represent the semantics of source code into distributed vectors. Currently, various works have been proposed to represent the complex semantics of source code from different views, including plain text, Abstract Syntax Tree (AST), and several kinds of code graphs (e.g., Control/Data Flow Graph). However, most of them only consider a single view of source code independently, ignoring the correspondences among different views. In this paper, we propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training, and name our model as CODE-MVP. Specifically, we first extract multiple code views using compiler tools, and learn the complementary information among them under a contrastive learning framework. Inspired by the type checking in compilation, we also design a finegrained type inference objective in the pretraining. Experiments on three downstream tasks over five datasets demonstrate the superiority of CODE-MVP when compared with several state-of-the-art baselines. For example, we achieve 2.4/2.3/1.1 gain in terms of MRR/MAP/Accuracy metrics on natural language code retrieval, code similarity, and code defect detection tasks, respectively.

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Exploiting gated graph neural network for detecting and explaining self-admitted technical debts

Cited by 10 publications

References 38 publications

HyperED: A hierarchy‐aware network based on hyperbolic geometry for event detection

HyperED: A hierarchy‐aware network based on hyperbolic geometry for event detection

Self-Admitted Technical Debt in the Embedded Systems Industry: An Exploratory Case Study

CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training

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