Abstract
Intelligent Measurement Terminals (IMTs) are pivotal components within the power grid measurement system. Conducting thorough testing of IMT applications before their deployment is a critical step in ensuring the safety of the power grid. To enhance the efficiency of application testing, this paper proposes an automated test case generation method for IMT applications based on natural language processing (NLP) techniques. First, the hierarchical relationships among various application functions are represented as a directed graph based on the Chinese requirements specifications of the application under test. Subsequently, the action flow of each function is analyzed through a four-step process: Chinese word segmentation, part-of-speech tagging, named entity recognition, and syntactic structure analysis. Finally, black-box function test cases are automatically generated according to the directed graph and the analyzed action flow. The proposed method is tested on two requirements specifications for IMT applications. The experimental results show that the proposed method achieves comparable test coverage and success rates to manual test case writing while demonstrating much less time costs.
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