Review of Fault Detection Based on Determining Software Inter-Dependency Patterns for Integration Testing Using Machine Learning on Logs Data
- DOI
- 10.2991/978-94-6463-716-8_86How to use a DOI?
- Keywords
- Fault Detection; Software Testing Inter-Dependency Patterns; Integration Testing; Logs Data
- Abstract
This study reviews a machine learning model applied to log files that creates entity and relationship patterns to be used in fault detection. Fault detection is an important part of integration testing when software project development is ongoing, and it could be integrated into a continuous testing approach. As the project grows, new features and code are added, and the code becomes more complex. The use of test logs enables the detection of patterns before they become deeply embedded in the code, which might make them difficult to comprehend and understand relationships and entity components. The model presented in this work is helpful in identifying which parts of the code are frequently changed, providing useful information for test case creation. Additionally, the entity relationship models are automatically created, and they may provide relevant patterns to create new tests to avoid or identify new faults in established relationships if they have never been tested.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Shashikant Ishvarbhai Patel AU - Rakesh Kumar Bhujade PY - 2025 DA - 2025/05/26 TI - Review of Fault Detection Based on Determining Software Inter-Dependency Patterns for Integration Testing Using Machine Learning on Logs Data BT - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025) PB - Atlantis Press SP - 1169 EP - 1183 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-716-8_86 DO - 10.2991/978-94-6463-716-8_86 ID - Patel2025 ER -