DEFTri: A Few-Shot Label Fused Contextual Representation Learning For
  Product Defect Triage in e-Commerce
Ipsita Mohanty
Defect Triage is a time-sensitive and critical process in a large-scale agile
software development lifecycle for e-commerce. Inefficiencies arising from
human and process dependencies in this domain have motivated research in
automated approaches using machine learning to accurately assign defects to
qualified teams. This work proposes a novel framework for automated defect
triage (DEFTri) using fine-tuned state-of-the-art pre-trained BERT on labels
fused text embeddings to improve contextual representations from
human-generated product defects. For our multi-label text classification defect
triage task, we also introduce a Walmart proprietary dataset of product defects
using weak supervision and adversarial learning, in a few-shot setting.