HINDBR: Heterogeneous Information Network Based Duplicate Bug Report Prediction

Xiao, Guanping; Du, Xiaoting; Sui, Yulei; Yue, Tao

doi:10.1109/issre5003.2020.00027

Cited by 18 publications

(9 citation statements)

References 44 publications

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“…The first is devising a process to evaluate whether a bug report is new or a duplicate. Consistent with best practices (Deshmukh et al, 2017;Akilan et al, 2020;Kukkar et al, 2020;Rodrigues et al, 2020;Sehra et al, 2020;Xiao et al, 2020), our proposed system uses the components of the bug reports for this assessment. Because our research targets an organization with a standard format for the bug reporting process, the elements in bug reports follow a general formula with explicit or implicit rules.…”

Section: Duplicate Bug Report Detectionmentioning

confidence: 99%

Sentence embedding and fine-tuning to automatically identify duplicate bugs

Isotani

Washizaki²,

Fukazawa³

et al. 2023

Front. Comput. Sci.

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Industrial software maintenance is critical but burdensome. Activities such as detecting duplicate bug reports are often performed manually. Herein an automated duplicate bug report detection system improves maintenance efficiency using vectorization of the contents and deep learning–based sentence embedding to calculate the similarity of the whole report from vectors of individual elements. Specifically, sentence embedding is realized using Sentence-BERT fine tuning. Additionally, its performance is experimentally compared to baseline methods to validate the proposed system. The proposed system detects duplicate bug reports more effectively than existing methods.

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Section: Duplicate Bug Report Detectionmentioning

confidence: 99%

Sentence embedding and fine-tuning to automatically identify duplicate bugs

Isotani

Washizaki²,

Fukazawa³

et al. 2023

Front. Comput. Sci.

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“…A few papers (Xie et al, 2018;Isotani et al, 2021) noted the effectiveness of word embedding techniques, otherwise very little comparative results have been reported. Similarly, bug report graphical representation (Xiao et al, 2020) has been rarely exploited for duplicate detection. In this sense, future studies should investigate deep graph models such as GCN and TBCNN for this objective.…”

Section: Bug Report Duplicate Detectionmentioning

confidence: 99%

“…As noted earlier, the performance of DL models tend to improve relative to data quality and size. For this reason, Xiao et al (2020) collected more than 2M bug reports to evaluate DBRP models for duplicate detection. Moreover, the data set consists attributes useful for multiple tasks; this enables cross project experiments to increase data sets sizes and inter tasks model evaluation.…”

Section: Public Data Sets For Dbrpmentioning

confidence: 99%

Deep Bug Reports Processing (DBRP): A Systematic Literature Review

Ahmad

Kholief

et al. 2023

Preprint

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Many software projects utilize Bug Tracking System (BTS) to manage and process bug reports. Over the years, the number of bug report submissions has increased exponentially with some projects receiving as many as about a hundred submissions daily. Bug report processing (BRP) consists of five key processes: duplicate detection, severity prediction, fix-time prediction, bug triage, and bug localization. Previously, traditional machine learning (ML) algorithms -based models were proposed to automate BRP tasks. However, in recent years deep BRP (DBRP) models were proposed to exploit the ever-increasing bug repositories for automatic extraction of semantic and contextual features of bug reports. Although, some papers reviewed related literature from many perspectives of software maintenance, no existing work comprehensively reviewed the state of DBRP. In this paper, we review the state of DBRP models in the five key BRP tasks. For this, we collect papers from four international databases published between 2015 and 2021. We evaluate the papers with regard to the deep neural networks, text representation models, and bug report features utilized. Finally, we present findings and prospects for future research works in DBRP. Our review analyzes how word embedding, CNNs, LSTMs, attention mechanism are used to represent bug report and source code, model performance and challenges such as data imbalance, feature utilization and complexity.

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“…This doctoral thesis investigates a heterogeneous information network's impact on representing data from different sources. Xiao et al (2020) defined a novel deep neural network (named HIND BR ) that used a heterogeneous information network (HEN) to detect similar duplicate bug reports. The experimental results suggest that the HIND BR method is better than the deep learning-based approach (XIAO et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Xiao et al (2020) defined a novel deep neural network (named HIND BR ) that used a heterogeneous information network (HEN) to detect similar duplicate bug reports. The experimental results suggest that the HIND BR method is better than the deep learning-based approach (XIAO et al, 2020). This thesis investigates unified HEN representation to support three BRR activities (bug report severity prediction, fixer recommendation, and bug localization) holistically.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous information network to support the bug report resolution process

Barbosa¹

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Contexto. Ao longo do ciclo de vida de um software, inúmeros documentos (por exemplo, relatórios de incidentes e código fonte) são produzidos por stakeholders. Os relatórios de incidentes (RI) são os principais documentos de insumo para apoiar as atividades (predição da severidade de relatórios de incidentes e recomendação de responsáveis pela correção do software) do processo de resolução de relatórios de incidentes (RRI). Já o código fonte combinado com os relatórios de incidentes são insumos para apoiar atividades de localização de defeitos. A Automação dessas atividades do processo RRI requer uma preocupação em como obter uma representação semanticamente representativa. Tradicionalmente utiliza-se Bag-of-Word (BoW) para representar os documentos de software para apoiar a execução automática dessas atividades por meio de algoritmos de aprendizado de máquina. Lacuna. No entanto, pouca atenção foi dada para representações baseadas em redes de informações heterogêneas (RIH), que permitem representar redes complexas respeitando os relacionamentos entre diferentes objetos. Contribuição. Esta tese de doutorado contribui para o avanço do estado da arte no que se refere aos modelos de representação de informações para apoiar a execução automática de atividades do processo RRI. Também avança na investigação de (i) algoritmos semissupervisionados que utilizam redes heterogêneas bipartidas para apoiar a predição de severidade de RI, (ii) um método que combina a representação BoW e redes de informações heterogêneas para apoiar a atividade de localização de defeitos, e (iii) uma abordagem holística que reutilizar uma rede de informações heterogêneas para apoiar atividades de RRI. Resultados. Os resultados demonstram que redes de informações heterogêneas podem ser uma alternativa promissora para apoiar a automação do processo RRI. Conclusões. Um processo RRI automático numa perspectica holística utilizando rede de informações heterogêneas apresentou resultados promissores ao serem comparados com representações do estado da arte.

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HINDBR: Heterogeneous Information Network Based Duplicate Bug Report Prediction

Cited by 18 publications

References 44 publications

Sentence embedding and fine-tuning to automatically identify duplicate bugs

Sentence embedding and fine-tuning to automatically identify duplicate bugs

Deep Bug Reports Processing (DBRP): A Systematic Literature Review

Heterogeneous information network to support the bug report resolution process

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