Robot Framework
Description
This dataset contains experimental data used to evaluate the efficiency and effectiveness of software test automation using the Robot Framework in an insurance-domain User Acceptance Testing (UAT) environment. The dataset compares the execution performance of manually performed test scenarios with their corresponding automated implementations using Robot Framework. The dataset consists of 54 paired business features or test scenarios, resulting in 108 observations, with each scenario tested in two modes: manual execution and automated execution. The same test scenarios were executed under comparable UAT conditions to enable a paired comparison between manual and automated testing. The dataset records execution duration, automation status, pass status, feature category, feature identifier, operator, testing environment, and execution date. The detailed feature-level data also include calculated time savings, percentage reduction in execution time, and efficiency ratio between manual and automated execution. The test scenarios cover seven major functional categories: Upload Submission, Payment Billing – Full, Payment Billing – Partial, Online Submission (ilovelife), Manual Submission & Premium Receipt, Search/Client/Admin, and Transaction. The overall descriptive results indicate that manual execution required an average of 18.98 minutes, whereas automated execution required an average of 3.52 minutes. This corresponds to an average reduction of approximately 81.5% in execution time, with an overall efficiency improvement factor of approximately 5.39×. The dataset also records a 100% pass rate for both manual and automated execution, indicating that the observed efficiency improvement was achieved without degradation in the recorded test outcome. The dataset is structured to support research on software test automation, Robot Framework, quality assurance, regression testing, software engineering, testing efficiency, and process automation. It can be used for descriptive analysis, paired performance comparison, efficiency evaluation, and further statistical modeling of software testing automation.