  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. The generated models were used
as input in the applied deep learning architecture, with linear output. The
results were very promising, realizing that pre-trained incorporation models
can be used to estimate software effort based only on requirements texts. We
highlight the results obtained to apply the pre-trained BERT model with
fine-tuning in a single project repository, whose value is the Mean Absolute
Error (MAE) is 4.25 and the standard deviation of only 0.17, which represents a
result very positive when compared to similar works. The main advantages of the
proposed estimation method are reliability, the possibility of generalization,
speed, and low computational cost provided by the fine-tuning process, and the
possibility to infer new or existing requirements.

Software Engineering;Computation and Language;Machine Learning