2019/03/29 by Lahari Poddar, Poddar, Lahari, Leonardo Neves +10
Computer Science · #Advanced Malware Detection Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Software Engineering Research #cs.CL
paper · pdf · doi:10.48550/arxiv.1903.12431
Accepted for publication in NAACL 2019
openalex publication_date 2019/03/29 · arxiv created 2019/04/04 · arxiv updated 2019/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.