Heterogeneous Graph Neural Networks for Software Effort Estimation

06/22/2022
by   Hung Phan, et al.
36

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/06/2022

Story Point Effort Estimation by Text Level Graph Neural Network

Estimating the software projects' efforts developed by agile methods is ...
research
09/02/2016

A deep learning model for estimating story points

Although there has been substantial research in software analytics for e...
research
01/14/2022

Deep Learning for Agile Effort Estimation Have We Solved the Problem Yet?

In the last decade, several studies have proposed the use of automated t...
research
10/14/2021

ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection

Identifying vulnerabilities in the source code is essential to protect t...
research
09/01/2022

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

Fu and Tantithamthavorn have recently proposed GPT2SP, a Transformer-bas...
research
07/11/2022

Boosting Heterogeneous Catalyst Discovery by Structurally Constrained Deep Learning Models

The discovery of new catalysts is one of the significant topics of compu...
research
01/24/2023

A Framework To Improve User Story Sets Through Collaboration

Agile methodologies have become increasingly popular in recent years. Du...

Please sign up or login with your details

Forgot password? Click here to reset