Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{196442,
        author = {Grishma Lad and Priyanka Sharma},
        title = {AI-Powered Self-Healing and Adaptive Software Testing Framework},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3800-3803},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196442},
        abstract = {Automated test execution has become a fundamental part of modern software development, especially with the growing adoption of spry methodologies and continuous integration practices. While automation works as a bridge in improving efficiency and reducing manual effort, it also introduces new obstacles. One of the most common issues is the fragility of test scripts, which often fail due to small and frequent changes in the application, for example, modifications in user interface elements or underlying structure. These failures are not always due to the actual defects, but rather by the minor inconveniences, which leads to debugging and maintenance efforts that are not fundamentally necessary.
In recent times, researchers have explored the usage of artificial intelligence (AI) and machine learning (ML) to address some of these limitations in software testing execution. Building on these ideas, this paper presents an AI-powered self-healing and adaptive software testing framework that aims to make machine-driven testing stronger and more efficient. The proposed framework is designed to automatically predict test failures, analyze their root causes, and update test cases without requiring manual intervention. It uses a combination of techniques, including similarity-based matching for identifying changes in application elements, natural language processing for generating test scenarios, and machine learning models that learn from past executions.
Another important aspect of the framework is its ability to adapt over time. Instead of repeatedly failing in similar situations, the system improves by learning from previous test runs and applying more accurate fixes in future executions. This makes the testing process more stable and reduces the overall maintenance burden on developers and testers.
All in all, the goal of the approach is to minimize incorrect test failures, improve reliability, and make automated testing more practical in fast-changing development environments. While the framework is presented at a conceptual level, it highlights the potential of AI in transforming traditional testing practices into more intelligent and adaptive systems},
        keywords = {},
        month = {April},
        }