Corpus ID: 220265686

SE3M: A Model for Software Effort Estimation Using Pre-trained Embedding Models

@article{Fvero2020SE3MAM,
  title={SE3M: A Model for Software Effort Estimation Using Pre-trained Embedding Models},
  author={Eliane Maria De Bortoli F{\'a}vero and Dalcimar Casanova and Andrey R. Pimentel},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.16831}
}
Estimating effort based on requirement texts presents many challenges, especially in obtaining viable features to infer effort. Aiming to explore a more effective technique for representing textual requirements to infer effort estimates by analogy, this paper proposes to evaluate the effectiveness of pre-trained embeddings models. For this, two embeddings approach, context-less and contextualized models are used. Generic pre-trained models for both approaches went through a fine-tuning process… CONTINUE READING

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