A Generic Approach to Fix Test Flakiness in Real-World Projects

• URL: http://arxiv.org/abs/2404.09398v1
• Date: Mon, 15 Apr 2024 01:07:57 GMT
• Title: A Generic Approach to Fix Test Flakiness in Real-World Projects
• Authors: Yang Chen, Reyhaneh Jabbarvand,
• Abstract summary: FlakyDoctor is a neuro-symbolic technique that combines the power of LLMs-generalizability-and program analysis-soundness to fix different types of test flakiness. Comparing to three alternative flakiness repair approaches, FlakyDoctor can repair 8% more ID tests than DexFix, 12% more OD flaky tests than OD, and 17% more OD flaky tests than iFixFlakies.
• Score: 7.122378689356857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Test flakiness, a non-deterministic behavior of builds irrelevant to code changes, is a major and continuing impediment to delivering reliable software. The very few techniques for the automated repair of test flakiness are specifically crafted to repair either Order-Dependent (OD) or Implementation-Dependent (ID) flakiness. They are also all symbolic approaches, i.e., leverage program analysis to detect and repair known test flakiness patterns and root causes, failing to generalize. To bridge the gap, we propose FlakyDoctor, a neuro-symbolic technique that combines the power of LLMs-generalizability-and program analysis-soundness-to fix different types of test flakiness. Our extensive evaluation using 873 confirmed flaky tests (332 OD and 541 ID) from 243 real-world projects demonstrates the ability of FlakyDoctor in repairing flakiness, achieving 57% (OD) and 59% (ID) success rate. Comparing to three alternative flakiness repair approaches, FlakyDoctor can repair 8% more ID tests than DexFix, 12% more OD flaky tests than ODRepair, and 17% more OD flaky tests than iFixFlakies. Regardless of underlying LLM, the non-LLM components of FlakyDoctor contribute to 12-31% of the overall performance, i.e., while part of the FlakyDoctor power is from using LLMs, they are not good enough to repair flaky tests in real-world projects alone. What makes the proposed technique superior to related research on test flakiness mitigation specifically and program repair, in general, is repairing 79 previously unfixed flaky tests in real-world projects. We opened pull requests for all cases with corresponding patches; 19 of them were accepted and merged at the time of submission.

Related papers

• Automating Quantum Software Maintenance: Flakiness Detection and Root Cause Analysis [4.554856650068748]
  Flaky tests, which pass or fail inconsistently without code changes, are a major challenge in software engineering. We aim to create an automated framework to detect flaky tests in quantum software.
  arXiv  Detail & Related papers  (2024-10-31T02:43:04Z)
• Leveraging Stack Traces for Spectrum-based Fault Localization in the Absence of Failing Tests [44.13331329339185]
  We introduce a new approach, SBEST, that integrates stack trace data with test coverage to enhance fault localization. Our approach shows a significant improvement, increasing Mean Average Precision (MAP) by 32.22% and Mean Reciprocal Rank (MRR) by 17.43% over traditional stack trace ranking methods.
  arXiv  Detail & Related papers  (2024-05-01T15:15:52Z)
• Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable [70.77600345240867]
  A novel arbitrary-in-arbitrary-out (AIAO) strategy makes watermarks resilient to fine-tuning-based removal. Unlike the existing methods of designing a backdoor for the input/output space of diffusion models, in our method, we propose to embed the backdoor into the feature space of sampled subpaths. Our empirical studies on the MS-COCO, AFHQ, LSUN, CUB-200, and DreamBooth datasets confirm the robustness of AIAO.
  arXiv  Detail & Related papers  (2024-05-01T12:03:39Z)
• 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)
• 230,439 Test Failures Later: An Empirical Evaluation of Flaky Failure Classifiers [9.45325012281881]
  Flaky tests are tests that can non-deterministically pass or fail, even in the absence of code changes. How to quickly determine if a test failed due to flakiness, or if it detected a bug?
  arXiv  Detail & Related papers  (2024-01-28T22:36:30Z)
• FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair [0.5749787074942512]
  Flaky tests are problematic because they non-deterministically pass or fail for the same software version under test. In this paper, we focus on predicting the type of fix that is required to remove flakiness and then repair the test code on that basis. One key idea is to guide the repair process with additional knowledge about the test's flakiness in the form of its predicted fix category.
  arXiv  Detail & Related papers  (2023-06-21T19:34:16Z)
• Debugging Flaky Tests using Spectrum-based Fault Localization [14.609208863749831]
  Flaky tests hamper regression testing as they destroy trust and waste computational and human resources. We introduce SFFL (Spectrum-based Flaky Fault localization), an extension of traditional coverage-based SFL. An evaluation on 101 flaky tests taken from 48 open-source Python projects demonstrates that SFFL is effective.
  arXiv  Detail & Related papers  (2023-05-08T14:40:05Z)
• Fast and Accurate Error Simulation for CNNs against Soft Errors [64.54260986994163]
  We present a framework for the reliability analysis of Conal Neural Networks (CNNs) via an error simulation engine. These error models are defined based on the corruption patterns of the output of the CNN operators induced by faults. We show that our methodology achieves about 99% accuracy of the fault effects w.r.t. SASSIFI, and a speedup ranging from 44x up to 63x w.r.t.FI, that only implements a limited set of error models.
  arXiv  Detail & Related papers  (2022-06-04T19:45:02Z)
• NADS: Neural Architecture Distribution Search for Uncertainty Awareness [79.18710225716791]
  Machine learning (ML) systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a distribution different from training data. Existing OoD detection approaches are prone to errors and even sometimes assign higher likelihoods to OoD samples. We propose Neural Architecture Distribution Search (NADS) to identify common building blocks among all uncertainty-aware architectures.
  arXiv  Detail & Related papers  (2020-06-11T17:39:07Z)
• TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task [80.38130122127882]
  TACRED is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE) In this paper, we investigate the questions: Have we reached a performance ceiling or is there still room for improvement? We find that label errors account for 8% absolute F1 test error, and that more than 50% of the examples need to be relabeled.
  arXiv  Detail & Related papers  (2020-04-30T15:07:37Z)
• 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.