AI-driven Java Performance Testing: Balancing Result Quality with Testing Time

• URL: http://arxiv.org/abs/2408.05100v2
• Date: Sat, 14 Sep 2024 11:26:31 GMT
• Title: AI-driven Java Performance Testing: Balancing Result Quality with Testing Time
• Authors: Luca Traini, Federico Di Menna, Vittorio Cortellessa,
• Abstract summary: We propose and study an AI-based framework to dynamically halt warm-up iterations at runtime. Our framework significantly improves the accuracy of the warm-up estimates provided by state-of-practice and state-of-the-art methods. Our study highlights that integrating AI to dynamically estimate the end of the warm-up phase can enhance the cost-effectiveness of Java performance testing.
• Score: 0.40964539027092917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Performance testing aims at uncovering efficiency issues of software systems. In order to be both effective and practical, the design of a performance test must achieve a reasonable trade-off between result quality and testing time. This becomes particularly challenging in Java context, where the software undergoes a warm-up phase of execution, due to just-in-time compilation. During this phase, performance measurements are subject to severe fluctuations, which may adversely affect quality of performance test results. However, these approaches often provide suboptimal estimates of the warm-up phase, resulting in either insufficient or excessive warm-up iterations, which may degrade result quality or increase testing time. There is still a lack of consensus on how to properly address this problem. Here, we propose and study an AI-based framework to dynamically halt warm-up iterations at runtime. Specifically, our framework leverages recent advances in AI for Time Series Classification (TSC) to predict the end of the warm-up phase during test execution. We conduct experiments by training three different TSC models on half a million of measurement segments obtained from JMH microbenchmark executions. We find that our framework significantly improves the accuracy of the warm-up estimates provided by state-of-practice and state-of-the-art methods. This higher estimation accuracy results in a net improvement in either result quality or testing time for up to +35.3% of the microbenchmarks. Our study highlights that integrating AI to dynamically estimate the end of the warm-up phase can enhance the cost-effectiveness of Java performance testing.

Related papers

• Which Combination of Test Metrics Can Predict Success of a Software Project? A Case Study in a Year-Long Project Course [1.553083901660282]
  Testing plays an important role in securing the success of a software development project. We investigate whether we can quantify the effects various types of testing have on functional suitability.
  arXiv  Detail & Related papers  (2024-08-22T04:23:51Z)
• Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications [0.0]
  Run to run variability in parallel programs caused by floating-point non-associativity has been known to significantly affect algorithms. We investigate the statistical properties of floating-point non-associativity within modern parallel programming models. We examine the recently-added deterministic options in PyTorch within the context of GPU deployment for deep learning.
  arXiv  Detail & Related papers  (2024-08-09T16:07:37Z)
• TSI-Bench: Benchmarking Time Series Imputation [52.27004336123575]
  TSI-Bench is a comprehensive benchmark suite for time series imputation utilizing deep learning techniques. The TSI-Bench pipeline standardizes experimental settings to enable fair evaluation of imputation algorithms. TSI-Bench innovatively provides a systematic paradigm to tailor time series forecasting algorithms for imputation purposes.
  arXiv  Detail & Related papers  (2024-06-18T16:07:33Z)
• Quantum Algorithm Exploration using Application-Oriented Performance Benchmarks [0.0]
  The QED-C suite of Application-Oriented Benchmarks provides the ability to gauge performance characteristics of quantum computers. We investigate challenges in broadening the relevance of this benchmarking methodology to applications of greater complexity.
  arXiv  Detail & Related papers  (2024-02-14T06:55:50Z)
• PACE: A Program Analysis Framework for Continuous Performance Prediction [0.0]
  PACE is a program analysis framework that provides continuous feedback on the performance impact of pending code updates. We design performance microbenchmarks by mapping the execution time of functional test cases given a code update. Our experiments achieved significant performance in predicting code performance, outperforming current state-of-the-art by 75% on neural-represented code stylometry features.
  arXiv  Detail & Related papers  (2023-12-01T20:43:34Z)
• From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation [60.14902811624433]
  We discuss a paradigm shift from static evaluation methods to adaptive testing. This involves estimating the characteristics and value of each test item in the benchmark and dynamically adjusting items in real-time. We analyze the current approaches, advantages, and underlying reasons for adopting psychometrics in AI evaluation.
  arXiv  Detail & Related papers  (2023-06-18T09:54:33Z)
• Re-Evaluating LiDAR Scene Flow for Autonomous Driving [80.37947791534985]
  Popular benchmarks for self-supervised LiDAR scene flow have unrealistic rates of dynamic motion, unrealistic correspondences, and unrealistic sampling patterns. We evaluate a suite of top methods on a suite of real-world datasets. We show that despite the emphasis placed on learning, most performance gains are caused by pre- and post-processing steps.
  arXiv  Detail & Related papers  (2023-04-04T22:45:50Z)
• DELTA: degradation-free fully test-time adaptation [59.74287982885375]
  We find that two unfavorable defects are concealed in the prevalent adaptation methodologies like test-time batch normalization (BN) and self-learning. First, we reveal that the normalization statistics in test-time BN are completely affected by the currently received test samples, resulting in inaccurate estimates. Second, we show that during test-time adaptation, the parameter update is biased towards some dominant classes.
  arXiv  Detail & Related papers  (2023-01-30T15:54:00Z)
• Planning for Sample Efficient Imitation Learning [52.44953015011569]
  Current imitation algorithms struggle to achieve high performance and high in-environment sample efficiency simultaneously. We propose EfficientImitate, a planning-based imitation learning method that can achieve high in-environment sample efficiency and performance simultaneously. Experimental results show that EI achieves state-of-the-art results in performance and sample efficiency.
  arXiv  Detail & Related papers  (2022-10-18T05:19:26Z)
• Uncertainty-Driven Action Quality Assessment [67.20617610820857]
  We propose a novel probabilistic model, named Uncertainty-Driven AQA (UD-AQA), to capture the diversity among multiple judge scores. We generate the estimation of uncertainty for each prediction, which is employed to re-weight AQA regression loss. Our proposed method achieves competitive results on three benchmarks including the Olympic events MTL-AQA and FineDiving, and the surgical skill JIGSAWS datasets.
  arXiv  Detail & Related papers  (2022-07-29T07:21:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.