A Comparative Analysis of the Effectiveness and Efficiency of Manual and Automated Testing in Software Quality Assurance
Abstract
Purpose: This study aims to analyze the effectiveness, bottlenecks, and reliability of automated testing and manual testing, as well as to compare the two approaches in detecting bugs and supporting software product quality. Accelerating release cycles can be counterproductive if functional stability is ignored; thus, evaluating these two quality assurance methods is crucial to finding an optimal balance in software development. Methods/Study design/approach: The study employed a quantitative approach through a survey of 105 respondents, comprising QA Engineers, software developers, and academics, using a five-level Likert scale questionnaire. The collected data were rigorously analyzed using descriptive statistics, the Wilcoxon Signed-Rank Test, and the Friedman Test to objectively evaluate the performance, reliability, and operational bottlenecks of both testing methods. Result/Findings: The results demonstrate that automated testing is highly effective for regression testing and repetitive execution, while manual testing remains significantly effective for UI/UX evaluation and complex dynamic cases. Both methods face distinct challenges. Automated testing is bottlenecked by script maintenance efforts and flaky tests, whereas manual testing is hindered by lengthy execution times and human error. Automated testing was found to be significantly more accurate in detecting recurring functional bugs. Furthermore, an integrated combination of both automated and manual testing was proven to substantially reduce escaped defects. Novelty/Originality/Value: This research provides empirical evidence confirming that despite the strong push for automation in modern Agile and DevOps environments, neither method can completely replace the other. Both testing approaches are most effective when utilized complementarily according to their intrinsic characteristics. This synergy is essential to prevent bottlenecks in the software release cycle and ensure superior product quality.