A Data-Driven Analysis of Software Testing Automation Challenges Using Structural Equation Modeling (SEM) Approach

The adoption of automation in software testing presents challenges that can hinder its effectiveness and scalability. This study systematically investigates these challenges using a multi-phase research approach. First, a Systematic Literature Review (SLR) was conducted to identify 14 Critical Challenges (CCs) in automation adoption. Second, a questionnaire survey of 50 industry experts validated these challenges and examined their interrelationships. Finally, Structural Equation Modeling (SEM) a mathematical and statistical approach was employed to analyze correlations and uncover structural dependencies among the challenges. The SEM analysis identified five latent variables: Human Resource Constraints (HRC), Technological & Process Challenges (TPC), Financial & Resource Constraints (FRC), Security & Reliability Issues (SRI), and Future Adaptability & Scalability (FAS) that significantly influence automation adoption. Hypothesis testing revealed that HRC (-0.32), TPC (-0.45), FRC (−0.41), and SRI (−0.38) negatively impact automation success, whereas FAS (+0.51) plays a pivotal role in enabling successful adoption. Model validation through Confirmatory Factor Analysis (CFA) and Exploratory Factor Analysis (EFA) confirmed strong construct reliability and fit indices (RMSEA =0.043, CFI =0.95, TLI =0.92, SRMR =0.037). The study highlights the need for workforce training, standardized automation processes, cost-effective solutions, and security enhancements. By providing an empirically validated framework, this research contributes to both academia and industry, guiding decision-makers in optimizing automation strategies and improving software testing efficiency.