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Article Citation - WoS: 8Automation Testing Tools: a Comparative View(Union Scientists Bulgaria, 2020) Atesogullari, Dilara; Mishra, AlokEffective software testing leads to assurance towards high quality in software development. Automation testing tool facilitates in faster testing process in testing stage thus completion and implementation of software on time. One of the most significant issues for automation is to select the automation-testing tool and the appropriate framework. The objective of this paper is to assess and compare twenty-one available automation-testing tools on twenty attributes in comprehensive manner. This study will assist software testing professionals and researchers towards further insight in this area.Conference Object Hybrid AI-Driven Decision Model for Test Automation in Agile Software Development(Institute of Electrical and Electronics Engineers Inc., 2025) Bon, Mohammad; Yazici, AliTest 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.
