What is the Vocabulary of Flaky Tests? An Extended Replication

• URL: http://arxiv.org/abs/2103.12670v1
• Date: Tue, 23 Mar 2021 16:42:22 GMT
• Title: What is the Vocabulary of Flaky Tests? An Extended Replication
• Authors: B. H. P. Camara, M. A. G. Silva, A. T. Endo, S. R. Vergilio
• Abstract summary: We conduct an empirical study to assess the use of code identifiers to predict test flakiness. We validated the performance of trained models using datasets with other flaky tests and from different projects.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Software systems have been continuously evolved and delivered with high quality due to the widespread adoption of automated tests. A recurring issue hurting this scenario is the presence of flaky tests, a test case that may pass or fail non-deterministically. A promising, but yet lacking more empirical evidence, approach is to collect static data of automated tests and use them to predict their flakiness. In this paper, we conducted an empirical study to assess the use of code identifiers to predict test flakiness. To do so, we first replicate most parts of the previous study of Pinto~et~al.~(MSR~2020). This replication was extended by using a different ML Python platform (Scikit-learn) and adding different learning algorithms in the analyses. Then, we validated the performance of trained models using datasets with other flaky tests and from different projects. We successfully replicated the results of Pinto~et~al.~(2020), with minor differences using Scikit-learn; different algorithms had performance similar to the ones used previously. Concerning the validation, we noticed that the recall of the trained models was smaller, and classifiers presented a varying range of decreases. This was observed in both intra-project and inter-projects test flakiness prediction.

Related papers

• FlaKat: A Machine Learning-Based Categorization Framework for Flaky Tests [3.0846824529023382]
  Flaky tests can pass or fail non-deterministically, without alterations to a software system. State-of-the-art research incorporates machine learning solutions into flaky test detection and achieves reasonably good accuracy.
  arXiv  Detail & Related papers  (2024-03-01T22:00:44Z)
• Test Generation Strategies for Building Failure Models and Explaining Spurious Failures [4.995172162560306]
  Test inputs fail not only when the system under test is faulty but also when the inputs are invalid or unrealistic. We propose to build failure models for inferring interpretable rules on test inputs that cause spurious failures. We show that our proposed surrogate-assisted approach generates failure models with an average accuracy of 83%.
  arXiv  Detail & Related papers  (2023-12-09T18:36:15Z)
• Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework [0.0]
  This paper presents the Fault Injection for Undesirable Learning in input Data (FIUL-Data) testing framework. Data mutators explore vulnerabilities of ML systems against the effects of different fault injections. This paper evaluates the framework using data from analytical chemistry, comprising retention time measurements of anti-sense oligonucleotides.
  arXiv  Detail & Related papers  (2023-09-20T12:58:35Z)
• TTAPS: Test-Time Adaption by Aligning Prototypes using Self-Supervision [70.05605071885914]
  We propose a novel modification of the self-supervised training algorithm SwAV that adds the ability to adapt to single test samples. We show the success of our method on the common benchmark dataset CIFAR10-C.
  arXiv  Detail & Related papers  (2022-05-18T05:43:06Z)
• Conformal prediction for the design problem [72.14982816083297]
  In many real-world deployments of machine learning, we use a prediction algorithm to choose what data to test next. In such settings, there is a distinct type of distribution shift between the training and test data. We introduce a method to quantify predictive uncertainty in such settings.
  arXiv  Detail & Related papers  (2022-02-08T02:59:12Z)
• Smoothed Embeddings for Certified Few-Shot Learning [63.68667303948808]
  We extend randomized smoothing to few-shot learning models that map inputs to normalized embeddings. Our results are confirmed by experiments on different datasets.
  arXiv  Detail & Related papers  (2022-02-02T18:19:04Z)
• On the use of test smells for prediction of flaky tests [0.0]
  flaky tests hamper the evaluation of test results and can increase costs. Existing approaches based on the use of the test case vocabulary may be context-sensitive and prone to overfitting. We investigate the use of test smells as predictors of flaky tests.
  arXiv  Detail & Related papers  (2021-08-26T13:21:55Z)
• Continual Learning for Fake Audio Detection [62.54860236190694]
  This paper proposes detecting fake without forgetting, a continual-learning-based method, to make the model learn new spoofing attacks incrementally. Experiments are conducted on the ASVspoof 2019 dataset.
  arXiv  Detail & Related papers  (2021-04-15T07:57:05Z)
• ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms [58.684954492439424]
  We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data. The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
  arXiv  Detail & Related papers  (2021-04-11T12:14:04Z)
• Significance tests of feature relevance for a blackbox learner [6.72450543613463]
  We derive two consistent tests for the feature relevance of a blackbox learner. The first evaluates a loss difference with perturbation on an inference sample. The second splits the inference sample into two but does not require data perturbation.
  arXiv  Detail & Related papers  (2021-03-02T00:59:19Z)
• Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
  We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers. We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
  arXiv  Detail & Related papers  (2020-06-22T21:12:31Z)

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.