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Insights Into Test Code Quality Prediction: Managing Machine Learning Techniques

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Valeria PontilloAuthors Info & Claims

EASE '24: Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering

Page 2

https://doi.org/10.1145/3661167.3661268

Published: 18 June 2024 Publication History

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Abstract

Test cases represent the first line of defence against the introduction of software faults, especially when testing for regressions. They must be constantly maintained and updated as part of software components to keep them useful. With the help of testing frameworks, developers create test methods and run them periodically on their code. The entire team relies on the results from these tests to decide whether to merge a pull request or deploy the system. Unfortunately, tests are not immune to bugs or technical debts: indeed, they often suffer from issues that can preclude their effectiveness. Typical problems in test cases are called flaky tests and test smells.

Over the last decades, the software engineering research community has been proposing a number of static and dynamic approaches to assist developers with the (semi-)automatic detection and removal of these problems. Despite this, most of these approaches rely on expensive dynamic steps and depend on tunable thresholds. These limitations have been partially targeted through machine learning solutions that could predict test quality issues using various features, like source code vocabulary or a mixture of static and dynamic metrics.

In this tutorial, I will discuss our experience building prediction models to detect quality issues in test code. The tutorial will discuss the design choices to make in the context of test code quality prediction and the implications these choices have for the reliability of the resulting models.

Index Terms

Insights Into Test Code Quality Prediction: Managing Machine Learning Techniques
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Empirical software validation
      2. Software defect analysis
        Software testing and debugging

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Published In

EASE '24: Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering

June 2024

728 pages

ISBN:9798400717017

DOI:10.1145/3661167

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 18 June 2024

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EASE 2024

EASE 2024: 28th International Conference on Evaluation and Assessment in Software Engineering

June 18 - 21, 2024

Salerno, Italy

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Overall Acceptance Rate 71 of 232 submissions, 31%

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Abstract

Index Terms

Recommendations

On the Use of Mutation Faults in Empirical Assessments of Test Case Prioritization Techniques

Empirical Studies of a Safe Regression Test Selection Technique

Prioritizing Test Cases For Regression Testing

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