Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering
| Autor: | Pradeep Karuturi, William Brendel, Leonardo Neves, Sergey Tulyakov, Luís Marujo, Lahari Poddar |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Artificial neural network Computer science business.industry media_common.quotation_subject Aggregate (data warehouse) 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Duplicate detection ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Function (engineering) Cluster analysis computer Computation and Language (cs.CL) media_common |
| Zdroj: | NAACL-HLT (2) |
| DOI: | 10.48550/arxiv.1903.12431 |
| Popis: | 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 non-technical annotators. The results demonstrate that our model not only can outperform state-of-the-art methods for duplicate classification on both cases, but can also learn meaningful latent clusters without additional supervision. Comment: Accepted for publication in NAACL 2019 |
| Databáze: | OpenAIRE |
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