TEP-GNN: Accurate Execution Time Prediction of Functional Tests Using Graph Neural Networks
Paper in proceeding, 2022

Predicting the performance of production code prior to actual execution is known to be highly challenging. In this paper, we propose a predictive model, dubbed TEP-GNN, which demonstrates that high-accuracy performance prediction is possible for the special case of predicting unit test execution times. TEP-GNN uses FA-ASTs, or flow-augmented ASTs, as a graph-based code representation approach, and predicts test execution times using a powerful graph neural network (GNN) deep learning model. We evaluate TEP-GNN using four real-life Java open source programs, based on 922 test files mined from the projects’ public repositories. We find that our approach achieves a high Pearson correlation of 0.789, considerable outperforming a baseline deep learning model. Our work demonstrates that FA-ASTs and GNNs are a feasible approach for predicting absolute performance values, and serves as an important intermediary step towards being able to predict the performance of arbitrary code prior to execution.

Machine learning

Performance

Software testing

Author

Peter Samoaa

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Antonio Longa

Fondazione Bruno Kessler (FBK)

Mazen Mohamad

University of Gothenburg

Morteza Haghir Chehreghani

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Philipp Leitner

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 13709 LNCS 464-479
9783031213878 (ISBN)

23rd International Conference on Product-Focused Software Process Improvement, PROFES 2022
Jyväskylä, Finland,

Subject Categories

Computer Engineering

Computer Science

Computer Systems

DOI

10.1007/978-3-031-21388-5_32

More information

Latest update

9/19/2024