\textbf{Background:} software effort estimation (SEE) usually suffers from data scarcity problem due to the expensive or long process of data collection. As a result, companies usually have limited data projects for effort estimation, causing unsatisfactory prediction performance. Few studies have investigated strategies to generate additional SEE data to aid such learning.
\textbf{Aim:} to propose a synthetic data generator to address the data scarcity problem of SEE. The proposed approach should be general to be used with any state-of-the-art SEE method. Ideally, it should be simple and hardly have negative effect on SEE performance.
\textbf{Method:} our synthetic generator enlarges the SEE data set size by slightly displacing some randomly chosen training examples. It can be used with any SEE method as a data preprocessor.
Its effectiveness is justified with 6 state-of-the-art SEE models across 14 SEE data sets. We also compare our data generator against the only existing approach in the SEE literature.
\textbf{Results:} our synthetic projects can significantly improve the performance of some SEE methods especially when the training data is insufficient. When they cannot significantly improve the prediction performance, they are not detrimental either.
Besides, our synthetic data generator is significantly superior or perform similarly to its competitor in the SEE literature.
\textbf{Conclusion:} our data generator plays a non-harmful if not significantly beneficial effect on the SEE methods investigated in this paper. Therefore, it is helpful in addressing the data scarcity problem of SEE.