CoChat
Sign Up
Cited by 208Software Testing and Debugging TechniquesSoftware Engineering ResearchSoftware Engineering Techniques and Practices

The DOI matches its Crossref record; 89 of the 93 references it cites were found in a registry or an index.

Machine Learning Applied to Software Testing: A Systematic Mapping Study

Durelli, Vinicius H. S., Durelli, Rafael S., Borges, Simone S., Endo, Andre T., Eler, Marcelo M., Dias, Diego R. C., Guimaraes, Marcelo P.·2019·IEEE Transactions on Reliability
DOI 10.1109/tr.2019.2892517
Open this paper in CoChat — free account

Abstract

Software testing involves probing into the behavior of software systems to uncover faults. Most testing activities are complex and costly, so a practical strategy that has been adopted to circumvent these issues is to automate software testing. There has been a growing interest in applying machine learning (ML) to automate various software engineering activities, including testing-related ones. In this paper, we set out to review the state-of-the art of how ML has been explored to automate and streamline software testing and provide an overview of the research at the intersection of these two fields by conducting a systematic mapping study. We selected 48 primary studies. These selected studies were then categorized according to study type, testing activity, and ML algorithm employed to automate the testing activity. The results highlight the most widely used ML algorithms and identify several avenues for future research. We found that ML algorithms have been used mainly for test-case generation, refinement, and evaluation. Also, ML has been used to evaluate test oracle construction and to predict the cost of testing-related activities. The results of this paper outline the ML algorithms that are most commonly used to automate software-testing activities, helping researchers to understand the current state of research concerning ML applied to software testing. We also found that there is a need for better empirical studies examining how ML algorithms have been used to automate software-testing activities.

Overview

Integrity signals

Registry

Crossref · 10.1109/tr.2019.2892517

Cites

89 of 89 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 93 references

Research works printed but not linked: 11

  1. choi, 0, Guided GUI testing of android apps with minimal restart and approximate learning, Proc ACM SIGPLAN Int Conf Object Oriented Program Syst Lang Appl, 623
  2. noorian, 0, Machine learning-based software testing: Towards a classification framework, Proc Int Conf on Software Eng Knowledge Eng, 225
  3. 10.1109/TEST.1995.529902 · DOI
  4. 10.1109/QSIC.2008.29 · DOI
  5. 10.1109/ICFEM.1997.630406 · DOI
  6. stone, 2012, Bayes’ Rule A Tutorial Introduction to Bayesian Analysis
  7. lantz, 2015, Machine Learning with R
  8. 10.1145/1007512.1007539 · DOI
  9. myers, 2011, The Art of Software Testing
  10. 10.1145/2896921.2896923 · DOI
  11. stringer, 2013, Action Research

Cited by

Sorted by citations

See all 208

Related

Metadata

Authors
Durelli, V. H. S., Durelli, R. S., Borges, S. S., Endo, A. T., Eler, M. M., Dias, D. R. C., & Guimaraes, M. P.
Affiliations
  • Universidade Federal de Lavras (BR)
  • Universidade Tecnológica Federal do Paraná (BR)
  • Universidade de São Paulo (BR)
  • Federal University of São João del-Rei (BR)
ORCID
Venue
IEEE Transactions on Reliability
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019-09
Volume
68
Issue
3
Pages
1189-1212
ISSN
  • 0018-9529 (ISSN-L)
  • 1558-1721
DOI
10.1109/tr.2019.2892517
OpenAlex
W2912899164
Cited by
207
References
93
Citations by year
2026: 13 · 2025: 42 · 2024: 33 · 2023: 28 · 2022: 36 · 2021: 23 · 2020: 21 · 2019: 5
FWCI
51.06
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Transactions on Reliability (publishedVersion)
Funders
CNPq/Brazil (420363/2018-1)
Topics
  • Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
Keywords
machine learning, software testing, test automation, test case generation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review