Fine-Grained Assertion-Based Test Selection

• URL: http://arxiv.org/abs/2403.16001v1
• Date: Sun, 24 Mar 2024 04:07:30 GMT
• Title: Fine-Grained Assertion-Based Test Selection
• Authors: Sijia Gu, Ali Mesbah,
• Abstract summary: Regression test selection techniques aim at reducing test execution time by selecting only the tests that are affected by code changes. We propose a novel approach that increases the selection precision by analyzing test code at statement level and treating test assertions as the unit for selection.
• Score: 6.9290255098776425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For large software applications, running the whole test suite after each code change is time- and resource-intensive. Regression test selection techniques aim at reducing test execution time by selecting only the tests that are affected by code changes. However, existing techniques select test entities at coarse granularity levels such as test class, which causes imprecise test selection and executing unaffected tests. We propose a novel approach that increases the selection precision by analyzing test code at statement level and treating test assertions as the unit for selection. We implement our fine-grained test selection approach in a tool called SELERTION and evaluate it by comparing against two state-of-the-art test selection techniques using 11 open-source subjects. Our results show that SELERTION increases selection precision for all the subjects. Our test selection reduces, on average, 63% of the overall test time, making regression testing up to 23% faster than the other techniques. Our results also indicate that subjects with longer test execution time benefit more by our fine-grained selection technique.

Related papers

• Do Test and Environmental Complexity Increase Flakiness? An Empirical Study of SAP HANA [47.29324864511411]
  Flaky tests fail seemingly at random without changes to the code. We study characteristics of tests and the test environment that potentially impact test flakiness.
  arXiv  Detail & Related papers  (2024-09-16T07:52:09Z)
• Precise Error Rates for Computationally Efficient Testing [75.63895690909241]
  We revisit the question of simple-versus-simple hypothesis testing with an eye towards computational complexity. An existing test based on linear spectral statistics achieves the best possible tradeoff curve between type I and type II error rates.
  arXiv  Detail & Related papers  (2023-11-01T04:41:16Z)
• Sequential Kernelized Independence Testing [101.22966794822084]
  We design sequential kernelized independence tests inspired by kernelized dependence measures. We demonstrate the power of our approaches on both simulated and real data.
  arXiv  Detail & Related papers  (2022-12-14T18:08:42Z)
• Test2Vec: An Execution Trace Embedding for Test Case Prioritization [12.624724734296342]
  Execution traces of test cases can be a good alternative to abstract their behavior for automated testing tasks. We propose a novel embedding approach, Test2Vec, that maps test execution traces to a latent space. Results show that our proposed TP improves best alternatives by 41.80% in terms of the median normalized rank of the first failing test case.
  arXiv  Detail & Related papers  (2022-06-28T20:38:36Z)
• Hybrid Intelligent Testing in Simulation-Based Verification [0.0]
  Several millions of tests may be required to achieve coverage goals. Coverage-Directed Test Selection learns from coverage feedback to bias testing towards the most effective tests. Novelty-Driven Verification learns to identify and simulate stimuli that differ from previous stimuli.
  arXiv  Detail & Related papers  (2022-05-19T13:22:08Z)
• Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification [0.0]
  We introduce a novel method for automatic constraint extraction and test selection. Coverage-directed test selection is based on supervised learning from coverage feedback. We show how coverage-directed test selection can reduce manual constraint writing, prioritise effective tests, reduce verification resource consumption, and accelerate coverage closure on a large, real-life industrial hardware design.
  arXiv  Detail & Related papers  (2022-05-17T17:49:30Z)
• UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning [89.56465237941013]
  We propose UNICON, a simple yet effective sample selection method which is robust to high label noise. We obtain an 11.4% improvement over the current state-of-the-art on CIFAR100 dataset with a 90% noise rate.
  arXiv  Detail & Related papers  (2022-03-28T07:36:36Z)
• Automated Support for Unit Test Generation: A Tutorial Book Chapter [21.716667622896193]
  Unit testing is a stage of testing where the smallest segment of code that can be tested in isolation from the rest of the system is tested. Unit tests are typically written as executable code, often in a format provided by a unit testing framework such as pytest for Python. This chapter introduces the concept of search-based unit test generation.
  arXiv  Detail & Related papers  (2021-10-26T11:13:40Z)
• Test-Agnostic Long-Tailed Recognition by Test-Time Aggregating Diverse Experts with Self-Supervision [85.07855130048951]
  We study a more practical task setting, called test-agnostic long-tailed recognition, where the training class distribution is long-tailed. We propose a new method, called Test-time Aggregating Diverse Experts (TADE), that trains diverse experts to excel at handling different test distributions. We theoretically show that our method has provable ability to simulate unknown test class distributions.
  arXiv  Detail & Related papers  (2021-07-20T04:10:31Z)
• TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks [14.547623982073475]
  Deep learning systems are notoriously difficult to test and debug. It is essential to conduct test selection and label only those selected "high quality" bug-revealing test inputs for test cost reduction. We propose a novel test prioritization technique that brings order into the unlabeled test instances according to their bug-revealing capabilities, namely TestRank.
  arXiv  Detail & Related papers  (2021-05-21T03:41:10Z)
• Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design [63.48989885374238]
  When the infection prevalence of a disease is low, Dorfman showed 80 years ago that testing groups of people can prove more efficient than testing people individually. Our goal in this paper is to propose new group testing algorithms that can operate in a noisy setting.
  arXiv  Detail & Related papers  (2020-04-26T23:41:33Z)

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.