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A Preliminary Study of Fixed Flaky Tests in Rust Projects on GitHub

Tom Schroeder, Minh Phan, Yang Chen

TL;DR

Flaky tests impede regression testing, and Rust's nondeterministic features present unique challenges that have been understudied. The authors construct a Rust-focused dataset from GitHub, identify 1,146 potentially flaky issues, and manually analyze 53 tests to classify root causes and fix strategies. They find asynchronous waits, concurrency issues, logic errors, and network problems as the dominant root causes, with fixes predominantly applied to main code and some cases only partially mitigating flakiness. This work provides the first Rust-centric examination of flaky tests on GitHub and establishes a foundation for detection methods, remediation strategies, and cross-language comparisons, while pointing to avenues for dataset expansion and broader applicability.

Abstract

Prior research has extensively studied flaky tests in various domains, such as web applications, mobile applications, and other open-source projects in a range of multiple programing languages, including Java, Javascript, Python, Ruby, and more. However, little attention has been given to flaky tests in Rust -- an emerging popular language known for its safety features relative to C/C++. Rust incorporates interesting features that make it easy to detect some flaky tests, e.g., the Rust standard randomizes the order of elements in hash tables, effectively exposing implementation-dependent flakiness. However, Rust still has several sources of nondeterminism that can lead to flaky tests. We present our work-in-progress on studying flaky tests in Rust projects on GitHub. Searching through the closed Github issues and pull requests. We focus on flaky tests that are fixed, not just reported, as the fixes can offer valuable information on root causes, manifestation characteristics, and strategies of fixes. By far, we have inspected 53 tests. Our initial findings indicate that the predominant root causes include asynchronous wait (33.9%), concurrency issues (24.5%), logic errors (9.4%). and network-related problems (9.4%).

A Preliminary Study of Fixed Flaky Tests in Rust Projects on GitHub

TL;DR

Flaky tests impede regression testing, and Rust's nondeterministic features present unique challenges that have been understudied. The authors construct a Rust-focused dataset from GitHub, identify 1,146 potentially flaky issues, and manually analyze 53 tests to classify root causes and fix strategies. They find asynchronous waits, concurrency issues, logic errors, and network problems as the dominant root causes, with fixes predominantly applied to main code and some cases only partially mitigating flakiness. This work provides the first Rust-centric examination of flaky tests on GitHub and establishes a foundation for detection methods, remediation strategies, and cross-language comparisons, while pointing to avenues for dataset expansion and broader applicability.

Abstract

Prior research has extensively studied flaky tests in various domains, such as web applications, mobile applications, and other open-source projects in a range of multiple programing languages, including Java, Javascript, Python, Ruby, and more. However, little attention has been given to flaky tests in Rust -- an emerging popular language known for its safety features relative to C/C++. Rust incorporates interesting features that make it easy to detect some flaky tests, e.g., the Rust standard randomizes the order of elements in hash tables, effectively exposing implementation-dependent flakiness. However, Rust still has several sources of nondeterminism that can lead to flaky tests. We present our work-in-progress on studying flaky tests in Rust projects on GitHub. Searching through the closed Github issues and pull requests. We focus on flaky tests that are fixed, not just reported, as the fixes can offer valuable information on root causes, manifestation characteristics, and strategies of fixes. By far, we have inspected 53 tests. Our initial findings indicate that the predominant root causes include asynchronous wait (33.9%), concurrency issues (24.5%), logic errors (9.4%). and network-related problems (9.4%).

Paper Structure

This paper contains 5 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Analysis and Future Work
  3. Categories of Flakiness Root Causes
  4. Categories of Fix Strategies
  5. Future Work

Figures (1)

  • Figure 1: Categories of fixes. (a) shows the effectiveness of fixes per root cause category; (b) outlines the scope of changes for fixes per root cause category.