Test Automation Frameworks for Data-Driven Applications

Abstract

Effective software testing is becoming more and more necessary as software systems gain relevance and meet stricter quality standards. Reducing the quantity of resources required is a better long-term option than increasing the number of test engineers or extending the testing period. The use of automated testing has become essential to guaranteeing the quality and reliability of software as the field of development for software continues to advance in complexity and scale. The use of automated testing frameworks is essential for expediting the testing process; nevertheless, choosing the right framework type is difficult and requires careful consideration. Therefore, the purpose of this thesis is to examine if a data-driven strategy to test automation may be introduced by gathering and curating user behaviour data to provide testing input. This article presents an open source test automation tool and test automation framework for business intelligence and data warehouse applications. Because it may boost test coverage by running test cases repeatedly, unless the amount of test cases is enormous, data-driven automation can play a significant role in this situation. A framework for data-driven continuous testing has been put out in this study. This framework allows for the efficient execution of many scripts. This framework runs different test scenarios using an Excel spreadsheet. Selenium has been used in combination with this framework. Various parameters are being used to execute these scenarios. The paper's findings demonstrate that the framework can manage a high number of test cases and provide precise answers in accordance with the test case. This approach eliminates the need for manual testing in automation.