TEP-GNN: Accurate Execution Time Prediction of Functional Tests Using Graph Neural Networks
Paper i 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.

Software testing

Machine learning

Performance

Författare

Hazem Samoaa

Göteborgs universitet

Antonio Longa

Fondazione Bruno Kessler (FBK)

Mazen Mohamad

Göteborgs universitet

Morteza Haghir Chehreghani

Chalmers, Data- och informationsteknik, Data Science och AI

Philipp Leitner

Chalmers, Data- och informationsteknik, Interaktionsdesign och 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,

Ämneskategorier

Datorteknik

Datavetenskap (datalogi)

Datorsystem

DOI

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

Mer information

Senast uppdaterat

2023-10-26