[1] M. Choetkiertikul, H. K. Dam, T. Tran, T. T. M. Pham, A. Ghose, and T. Menzies, “A deep learning model for estimating story points,” IEEE Trans. Softw. Eng., vol. PP, no. 99, p. 1, 2018.
@article{Choetkiertikul2018,
author = {Choetkiertikul, M and Dam, H K and Tran, T and Pham, T T M and Ghose, A and Menzies, T},
doi = {10.1109/TSE.2018.2792473},
issn = {0098-5589 VO - PP},
journal = {IEEE Transactions on Software Engineering},
keywords = {Estimation,Machine learning,Planning,Predictive models,Software,Springs,deep learning,effort estimation,software analytics,story point estimation},
number = {99},
pages = {1},
title = {{A deep learning model for estimating story points}},
volume = {PP},
year = {2018}
}
These are the datasets and supplementary resources for our submission entitled: A deep learning model for estimating story points
For verifiability, we provided our story points datasets (the issues with story points from 16 projects collected from 9 open source repositories), the pre-train data (the title and the description of the issues without story points collected from 9 open source repositories) that has been used for pre-training LSTM, the pre-trained LSTM models, and our Deep-SE source code including the script to run the experiments.
We also provide the replication of the Purru et al.'s method and their dataset that we used in our study.