Hybrid AI-Driven Decision Model for Test Automation in Agile Software Development

Abstract

Test automation plays an essential role in Agile Software Development (ASD), but its implementation remains complex. This study conducts a Systematic Literature Review (SLR) to identify key points of test automation and recent developments in Artificial Intelligence (AI). Based on 21 factors proposed by Butt et al., we construct a three-phase decision-support model addressing software, tools, tests, human, and economic dimensions. To improve this model, modern AI techniques - including natural language processing (NLP), machine learning (ML), Mabl (a self-healing, AI-based test automation tool) and Parasoft Selenic - are used. These technologies automate test case generation, prioritization, and maintenance, aligning with Agile's fast-paced demands. Our proposed hybrid model applies NLP to identify effecting factors, ML for impact scoring, and reinforcement learning (RL) for guiding automation strategies. The goal is to decrease manual processes, improve decision accuracy, and to adapt to evolving requirements. However, challenges such as data quality and the need for AI expertise remain. Future work should focus on practical validation and explore applications in non-functional testing. This study offers a practical, AI-enhanced framework to support Agile teams in streamlining test automation. © 2025 IEEE.