Towards Word Embeddings for Improved Duplicate Bug Report Retrieval in Software Repositories

Budhiraja, Amar; Dutta, Kartik; Shrivastava, Manish; Reddy, Raghu

doi:10.1145/3234944.3234949

Cited by 21 publications

(40 citation statements)

References 6 publications

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“…(1) DWEN [3]: a deep learning based duplicate bug report detection method, which extracts text features from bug reports and builds a neural network model for predicting whether or not two bug reports are duplicate. (2) DBTM [27]: an information retrieval based duplicate bug report detection method, which models a bug report as a textual document describing certain technical issue(s), and models duplicate bug reports as the ones about the same technical issue(s).…”

Section: Incident Linkagementioning

confidence: 99%

Identifying linked incidents in large-scale online service systems

Chen

Yang

Dong

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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In large-scale online service systems, incidents occur frequently due to a variety of causes, from updates of software and hardware to changes in operation environment. These incidents could significantly degrade system's availability and customers' satisfaction. Some incidents are linked because they are duplicate or interrelated. The linked incidents can greatly help on-call engineers find mitigation solutions and identify the root causes. In this work, we investigate the incidents and their links in a representative real-world incident management (IcM) system. Based on the identified indicators of linked incidents, we further propose LiDAR (Linked Incident identification with DAta-driven Representation), a deep learning based approach to incident linking. More specifically, we incorporate the textual description of incidents and structural information extracted from historical linked incidents to identify possible links among a large number of incidents. To show the effectiveness of our method, we apply our method to a real-world IcM system and find that our method outperforms other state-of-the-art methods.

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Section: Incident Linkagementioning

confidence: 99%

Identifying linked incidents in large-scale online service systems

Chen

Yang

Dong

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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“…As shown by the dictionary sources, the method is applied to android bug reports and an 11.55% performance improvement is achieved compared with REP [45]. A similar study [46] uses word embedding.…”

Section: Bug Report Deduplicationmentioning

confidence: 99%

“…Other machine learning methods [55,56], such as hidden Markov models (HMMs) or deep networks, are proposed. They build a model that identifies the features of duplicate bug reports and utilize it.…”

Section: Bug Report Deduplicationmentioning

confidence: 99%

Improving bug report triage performance using artificial intelligence based document generation model

Lee

Seo

2020

Hum. Cent. Comput. Inf. Sci.

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Along with the fourth industrial revolution, artificial intelligence, big data, Internet of Things, and cloud computing are emerging as cutting-edge technologies globally. In particular, artificial intelligence has unlimited potential to further improve the quality of human life and can solve several difficult engineering problems [1-12]. Moreover, this technology provides basic ideas to derive successful solutions to numerous problems encountered in the software development field.

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“…A combination of tf-idf and topics learned by LDA have also been used (Nguyen et al, 2012;Budhiraja et al, 2018b). Recently, word embeddings have been used to compute similarity of two reports (Yang et al, 2016;Budhiraja et al, 2018a). However, in these approaches, the sequence information of a natural language sentence is not captured.…”

Section: Related Workmentioning

confidence: 99%

Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering

Poddar¹,

Neves²,

Brendel³

et al. 2019

Proceedings of the 2019 Conference of the North

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Tracking user reported bugs requires considerable engineering effort in going through many repetitive reports and assigning them to the correct teams. This paper proposes a neural architecture that can jointly (1) detect if two bug reports are duplicates, and (2) aggregate them into latent topics. Leveraging the assumption that learning the topic of a bug is a sub-task for detecting duplicates, we design a loss function that can jointly perform both tasks but needs supervision for only duplicate classification, achieving topic clustering in an unsupervised fashion. We use a two-step attention module that uses self-attention for topic clustering and conditional attention for duplicate detection. We study the characteristics of two types of real world datasets that have been marked for duplicate bugs by engineers and by nontechnical annotators. The results demonstrate that our model not only can outperform stateof-the-art methods for duplicate classification on both cases, but can also learn meaningful latent clusters without additional supervision.

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Towards Word Embeddings for Improved Duplicate Bug Report Retrieval in Software Repositories

Cited by 21 publications

References 6 publications

Identifying linked incidents in large-scale online service systems

Identifying linked incidents in large-scale online service systems

Improving bug report triage performance using artificial intelligence based document generation model

Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering

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