# Heterogeneous Graph Neural Networks for Software Effort Estimation

arXiv: https://arxiv.org/abs/2206.11023 · HTML: https://mlsanity.com/paper/2206.11023/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 16 of 16)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Learning to Represent Programs with Graphs](https://mlsanity.com/paper/1711.00740/) (2017)
- [Attention-based Graph Neural Network for Semi-supervised Learning](https://mlsanity.com/paper/1803.03735/) (2018)
- [Generative Code Modeling with Graphs](https://mlsanity.com/paper/1805.08490/) (2018)
- [Text Level Graph Neural Network for Text Classification](https://mlsanity.com/paper/1910.02356/) (2019)
- [Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code](https://mlsanity.com/paper/2003.07914/) (2020)
- [Auto Graph Encoder-Decoder for Neural Network Pruning](https://mlsanity.com/paper/2011.12641/) (2020)
- [funcGNN: A Graph Neural Network Approach to Program Similarity](https://mlsanity.com/paper/2007.13239/) (2020)
- [A deep learning model for estimating story points](https://mlsanity.com/paper/1609.00489/) (2016)
- [Learning Intermediate Representations using Graph Neural Networks for NUMA and Prefetchers Optimization](https://mlsanity.com/paper/2203.00611/) (2022)
- [A Versatile Dataset of Agile Open Source Software Projects](https://mlsanity.com/paper/2202.00979/) (2022)
- [Story Point Effort Estimation by Text Level Graph Neural Network](https://mlsanity.com/paper/2203.03062/) (2022)

## Built on by (most cited first, 3 of 3)

- [Explainable Spatio-Temporal GCNNs for Irregular Multivariate Time Series: Architecture and Application to ICU Patient Data](https://mlsanity.com/paper/2411.01070/) (2024)
- [Agile Story-Point Estimation: Is RAG a Better Way to Go?](https://mlsanity.com/paper/2604.03443/) (2026)
- [Relationally Guided Use Case Modeling with LLMs](https://mlsanity.com/paper/2609.18291/) (2026)

## Abstract (arXiv)

Software effort can be measured by story point [35]. Current approaches for automatically estimating story points focus on applying pre-trained embedding models and deep learning for text regression to solve this problem which required expensive embedding models. We propose HeteroSP, a tool for estimating story points from textual input of Agile software project issues. We select GPT2SP [12] and Deep-SE [8] as the baselines for comparison. First, from the analysis of the story point dataset [8], we conclude that software issues are actually a mixture of natural language sentences with quoted code snippets and have problems related to large-size vocabulary. Second, we provide a module to normalize the input text including words and code tokens of the software issues. Third, we design an algorithm to convert an input software issue to a graph with different types of nodes and edges. Fourth, we construct a heterogeneous graph neural networks model with the support of fastText [6] for constructing initial node embedding to learn and predict the story points of new issues. We did the comparison over three scenarios of estimation, including within project, cross-project within the repository, and cross-project cross repository with our baseline approaches. We achieve the average Mean Absolute Error (MAE) as 2.38, 2.61, and 2.63 for three scenarios. We outperform GPT2SP in 2/3 of the scenarios while outperforming Deep-SE in the most challenging scenario with significantly less amount of running time. We also compare our approaches with different homogeneous graph neural network models and the results show that the heterogeneous graph neural networks model outperforms the homogeneous models in story point estimation. For time performance, we achieve about 570 seconds as the time performance in both three processes: node embedding initialization, model construction, and story point estimation.