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- W4312396726 abstract "AbstractPredicting 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.KeywordsPerformanceSoftware testingMachine learning" @default.
- W4312396726 created "2023-01-04" @default.
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- W4312396726 date "2022-01-01" @default.
- W4312396726 modified "2023-09-30" @default.
- W4312396726 title "TEP-GNN: Accurate Execution Time Prediction of Functional Tests Using Graph Neural Networks" @default.
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- W4312396726 doi "https://doi.org/10.1007/978-3-031-21388-5_32" @default.
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