Segment-Based Test Case Prioritization: A Multi-objective Approach

• URL: http://arxiv.org/abs/2408.00705v1
• Date: Thu, 1 Aug 2024 16:51:01 GMT
• Title: Segment-Based Test Case Prioritization: A Multi-objective Approach
• Authors: Hieu Huynh, Nhu Pham, Tien N. Nguyen, Vu Nguyen,
• Abstract summary: Test case prioritization ( TCP) is a cost-efficient solution to schedule test cases in an execution order that maximizes an objective function. We introduce a multi-objective optimization approach to prioritize UI test cases using evolutionary search algorithms and four coverage criteria. Our approach significantly outperforms other methods in terms of Average Percentage of Faults Detected (APFD) and APFD with Cost.
• Score: 8.972346309150199
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Regression testing of software is a crucial but time-consuming task, especially in the context of user interface (UI) testing where multiple microservices must be validated simultaneously. Test case prioritization (TCP) is a cost-efficient solution to address this by scheduling test cases in an execution order that maximizes an objective function, generally aimed at increasing the fault detection rate. While several techniques have been proposed for TCP, most rely on source code information which is usually not available for UI testing. In this paper, we introduce a multi-objective optimization approach to prioritize UI test cases, using evolutionary search algorithms and four coverage criteria focusing on web page elements as objectives for the optimization problem. Our method, which does not require source code information, is evaluated using two evolutionary algorithms (AGE-MOEA and NSGA-II) and compared with other TCP methods on a self-collected dataset of 11 test suites. The results show that our approach significantly outperforms other methods in terms of Average Percentage of Faults Detected (APFD) and APFD with Cost (APFDc), achieving the highest scores of 87.8\% and 79.2\%, respectively. We also introduce a new dataset and demonstrate the significant improvement of our approach over existing ones via empirical experiments. The paper's contributions include the application of web page segmentation in TCP, the construction of a new dataset for UI TCP, and empirical comparisons that demonstrate the improvement of our approach.

Related papers

• Optimizing Metamorphic Testing: Prioritizing Relations Through Execution Profile Dissimilarity [2.6749261270690434]
  An oracle determines whether the output of a program for executed test cases is correct. For machine learning programs, such an oracle is often unavailable or impractical to apply. Prioritizing MRs enhances fault detection effectiveness and improves testing efficiency.
  arXiv  Detail & Related papers  (2024-11-14T04:14:30Z)
• On Speeding Up Language Model Evaluation [48.51924035873411]
  Development of prompt-based methods with Large Language Models (LLMs) requires making numerous decisions. We propose a novel method to address this challenge. We show that it can identify the top-performing method using only 5-15% of the typically needed resources.
  arXiv  Detail & Related papers  (2024-07-08T17:48:42Z)
• Fuzzy Inference System for Test Case Prioritization in Software Testing [0.0]
  Test case prioritization ( TCP) is a vital strategy to enhance testing efficiency. This paper introduces a novel fuzzy logic-based approach to automate TCP.
  arXiv  Detail & Related papers  (2024-04-25T08:08:54Z)
• Better Practices for Domain Adaptation [62.70267990659201]
  Domain adaptation (DA) aims to provide frameworks for adapting models to deployment data without using labels. Unclear validation protocol for DA has led to bad practices in the literature. We show challenges across all three branches of domain adaptation methodology.
  arXiv  Detail & Related papers  (2023-09-07T17:44:18Z)
• Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed Evaluation Methodology [2.043517674271996]
  We discuss how a normally good protocol may have weaknesses in the context of MVTS anomaly detection. We propose a simple, yet challenging, baseline based on Principal Components Analysis (PCA) that surprisingly outperforms many recent Deep Learning (DL) based approaches on popular benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-24T20:24:12Z)
• Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization [101.08992036691673]
  This paper explores a realistic unsupervised fine-tuning scenario, considering the presence of out-of-distribution samples from unknown classes. In particular, we focus on simultaneously enhancing out-of-distribution detection and the recognition of instances associated with known classes. We present a simple, efficient, and effective approach called Universal Entropy Optimization (UEO)
  arXiv  Detail & Related papers  (2023-08-24T16:47:17Z)
• On Pitfalls of Test-Time Adaptation [82.8392232222119]
  Test-Time Adaptation (TTA) has emerged as a promising approach for tackling the robustness challenge under distribution shifts. We present TTAB, a test-time adaptation benchmark that encompasses ten state-of-the-art algorithms, a diverse array of distribution shifts, and two evaluation protocols.
  arXiv  Detail & Related papers  (2023-06-06T09:35:29Z)
• Evaluating Search-Based Software Microbenchmark Prioritization [6.173678645884399]
  This paper empirically evaluate single- and multi-objective search-based microbenchmark prioritization techniques. We find that search algorithms (SAs) are only competitive with but do not outperform the best greedy, coverage-based baselines.
  arXiv  Detail & Related papers  (2022-11-24T10:45:39Z)
• Test case prioritization using test case diversification and fault-proneness estimations [0.0]
  We propose an approach for TCP that takes into account test case coverage data, bug history, and test case diversification. The diversification of test cases is preserved by incorporating fault-proneness on a clustering-based approach scheme. The experiments show that the proposed methods are superior to coverage-based TCP methods.
  arXiv  Detail & Related papers  (2021-06-19T15:55:24Z)
• SIMPLE: SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation [81.03485688525133]
  We propose a novel multi-person pose estimation framework, SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation (SIMPLE) Specifically, in the training process, we enable SIMPLE to mimic the pose knowledge from the high-performance top-down pipeline. Besides, SIMPLE formulates human detection and pose estimation as a unified point learning framework to complement each other in single-network.
  arXiv  Detail & Related papers  (2021-04-06T13:12:51Z)
• FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking [92.48078680697311]
  Multi-object tracking (MOT) is an important problem in computer vision. We present a simple yet effective approach termed as FairMOT based on the anchor-free object detection architecture CenterNet. The approach achieves high accuracy for both detection and tracking.
  arXiv  Detail & Related papers  (2020-04-04T08:18:00Z)

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.