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Fu and Tantithamthavorn have recently proposed GPT2SP, a Transformer-baseddeep learning model for SP estimation of user stories. They empiricallyevaluated the performance of GPT2SP on a dataset shared by Choetkiertikul et alincluding 16 projects with a total of 23,313 issues. They benchmarked GPT2SPagainst two baselines (namely the naive Mean and Median estimators) and themethod previously proposed by Choetkiertikul et al. (which we will refer to asDL2SP from now on) for both within- and cross-project estimation scenarios, andevaluated the extent to which each components of GPT2SP contribute towards theaccuracy of the SP estimates. Their results show that GPT2SP outperforms DL2SPwith 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 usethe GPT2SP source code made available by Fu and Tantithamthavorn to reproducetheir 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 beenaccepted and merged into their repository athttps://github.com/awsm-research/gpt2sp/pull/2.  In this report, we describe the results we achieved by using the fixedversion of GPT2SP to replicate the experiments conducted in the original paperfor RQ1 and RQ2. Following the original study, we analyse the resultsconsidering the Medan Absolute Error (MAE) of the estimation methods over allissues in each project, but we also report the Median Absolute Error (MdAE) andthe Standard accuracy (SA) for completeness.

Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Second Replication Study (GPT2SP Replication Report)