Intelligent building networking system realizes real-time monitoring and control of the building environment through sensor technology and data communication protocols, which has become an important trend in the development of modern building intelligence. However, the traditional software testing methods have the problems of low test coverage and low efficiency when facing complex IoT systems. Based on this, this paper proposes a data structure modeling and test case automatic generation method based on improved genetic algorithm. The method constructs a four-layer system architecture including input layer, coding layer, core processing layer and output layer, adopts a combination of binary coding and floating-point coding to represent test cases, designs a multi-objective fitness function that integrally considers code coverage, execution efficiency and resource utilization, and realizes the optimal generation of test cases through improved selection, crossover and mutation operations. The experimental results show that the execution time of the improved genetic algorithm in function optimization is 51.19 seconds, which is 4.65 seconds faster than that of the IAGA algorithm, and the convergence state can be reached in an average of 125.354 iterations, which is significantly better than the comparison algorithm. Validation on seven standard test programs shows that the proposed method achieves an average coverage of 82.5%, which is 8.9% and 4.7% higher than the DTG and NDTG algorithms, respectively. The method can effectively improve the quality of test cases and provides technical support for the reliability assurance of smart building networking systems.
