ICST 2024
Mon 27 - Fri 31 May 2024 Canada
Tue 28 May 2024 16:00 - 16:30 at Room 5 - Session 2

To ensure the quality of software source code, numerous software testing approaches have been studied. Software is now integral to numerous devices, enterprise services, and public services. Although the demand for software quality has increased, Software Science has yet to provide a definitive solution for bug prediction methodologies. In this study, we propose a novel bug prediction methodology for software testing using Graph Neural Network (GNN) techniques. We attempt to apply the machine learning technique of Graph Convolutional Neural Networks (GCN) to Control Flow Graphs (CFG) generated from the tri-address information of the test target source code. In the CFG, multiple graph centrality values are utilized as graph feature for bug prediction. Hence, our bug prediction model based on graph neural network (BP-GNN) exhibits a better result with an accuracy value of 82%. This result represents an 15% improvement compared to the outcomes of previous study using Akaike Information Criterion (AIC) with graph centrality annotation for same CFG data.

Tue 28 May

Displayed time zone: Eastern Time (US & Canada) change

16:00 - 17:30
Session 2InSTA at Room 5
16:00
30m
Research paper
Software Bug Prediction Model using Graph Neural Network
InSTA
Tomohiro Takeda , Satoshi Masuda Tokyo City University
16:30
10m
Talk
Closing
InSTA