AI-Driven Tools in Modern Software Quality Assurance: An Assessment of Benefits, Challenges, and Future Directions

TL;DR

This paper evaluates how AI tools can enhance modern software quality assurance by analyzing benefits, challenges, and practical applications, including a proof of concept with promising results and identified limitations.

Contribution

It provides a comprehensive assessment of AI integration into QA processes, including a practical test scenario and analysis of current challenges and future needs.

Findings

8.3% flaky test executions with AI agents

Significant potential for AI in QA processes

Identified challenges in explainability and verification

Abstract

Traditional quality assurance (QA) methods face significant challenges in addressing the complexity, scale, and rapid iteration cycles of modern software systems and are strained by limited resources available, leading to substantial costs associated with poor quality. The object of this research is the Quality Assurance processes for modern distributed software applications. The subject of the research is the assessment of the benefits, challenges, and prospects of integrating modern AI-oriented tools into quality assurance processes. We performed comprehensive analysis of implications on both verification and validation processes covering exploratory test analyses, equivalence partitioning and boundary analyses, metamorphic testing, finding inconsistencies in acceptance criteria (AC), static analyses, test case generation, unit test generation, test suit optimization and assessment, end to end scenario execution. End to end regression of sample enterprise application utilizing AI-agents over generated test scenarios was implemented as a proof of concept highlighting practical use of the study. The results, with only 8.3% flaky executions of generated test cases, indicate significant potential for the proposed approaches. However, the study also identified substantial challenges for practical adoption concerning generation of semantically identical coverage, "black box" nature and lack of explainability from state-of-the-art Large Language Models (LLMs), the tendency to correct mutated test cases to match expected results, underscoring the necessity for thorough verification of both generated artifacts and test execution results. The research demonstrates AI's transformative potential for QA but highlights the importance of a strategic approach to implementing these technologies, considering the identified limitations and the need for developing appropriate verification methodologies.