  Fu and Tantithamthavorn have recently proposed GPT2SP, a Transformer-based
deep learning model for SP estimation of user stories. They empirically
evaluated the performance of GPT2SP on a dataset shared by Choetkiertikul et al
including 16 projects with a total of 23,313 issues. They benchmarked GPT2SP
against two baselines (namely the naive Mean and Median estimators) and the
method previously proposed by Choetkiertikul et al. (which we will refer to as
DL2SP from now on) for both within- and cross-project estimation scenarios, and
evaluated the extent to which each components of GPT2SP contribute towards the
accuracy of the SP estimates. Their results show that GPT2SP outperforms DL2SP
with a 6%-47% improvement over MAE for the within-project scenario and a 3%-46%
improvement for the cross-project scenarios. However, when we attempted to use
the GPT2SP source code made available by Fu and Tantithamthavorn to reproduce
their experiments, we found a bug in the computation of the Mean Absolute Error
(MAE), which may have inflated the GPT2SP's accuracy reported in their work.
Therefore, we had issued a pull request to fix such a bug, which has been
accepted and merged into their repository at
https://github.com/awsm-research/gpt2sp/pull/2.
  In this report, we describe the results we achieved by using the fixed
version of GPT2SP to replicate the experiments conducted in the original paper
for RQ1 and RQ2. Following the original study, we analyse the results
considering the Medan Absolute Error (MAE) of the estimation methods over all
issues in each project, but we also report the Median Absolute Error (MdAE) and
the Standard accuracy (SA) for completeness.
