LLM-Based Video Analytics Test Scenario Generation in Smart Cities

Rapid advances in the field of artificial intelligence have made significant contributions to the automation of software development and testing stages. Software created for use in various fields is tested with test scenarios created manually by software test experts or using test automation. Testing large-scale software with these methods complicates the testing phases because it requires increased human intervention and includes complex applications. In this study, an LLM-based scenario generation framework enhanced with prompt engineering is proposed for testing software to be used for video analysis in smart cities and smart campus areas. Thus, software test scenarios are created by strengthening large language models that are fast, flexible and have high learning ability using prompt engineering techniques. Test scenarios produced through LLM reinforced with prompt engineering techniques were evaluated with rarity and reality metrics and it was determined that more robust scenarios were produced compared to randomly generated test scenarios in the relevant field.