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Agile development processes based on user stories often face issues such as incomplete, inconsistent, and inaccurate user requirements, which increase the workload of agile development teams and reduce the efficiency of product function development, ultimately resulting in the inability to respond quickly to user requirements. This paper proposes a user requirement quality assessment method based on user stories to address these problems. This method relies on the agile development process, constructs a user requirement quality assessment framework, defines a user story model and a user requirement quality model, develops seven user requirement quality assessment criteria, and designs a user requirement quality assessment process. A data experiment exploring the development of smartphone requirements is used to validate the feasibility and effectiveness of the method. The experimental results demonstrate that the method improves user requirement quality to some extent, providing an automated solution for agile development teams to enhance user requirement quality.

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In the era of the popularization of the Internet of Things (IOT), analyzing people’s daily life behavior through the data collected by devices is an important method to mine potential daily requirements. The network method is an important means to analyze the relationship between people’s daily behaviors, while the mainstream first-order network (FON) method ignores the high-order dependencies between daily behaviors. A higher-order dependency network (HON) can more accurately mine the requirements by considering higher-order dependencies. Firstly, our work adopts indoor daily behavior sequences obtained by video behavior detection, extracts higher-order dependency rules from behavior sequences, and rewires an HON. Secondly, an HON is used for the RandomWalk algorithm. On this basis, research on vital node identification and community detection is carried out. Finally, results on behavioral datasets show that, compared with FONs, HONs can significantly improve the accuracy of random walk, improve the identification of vital nodes, and we find that a node can belong to multiple communities. Our work improves the performance of user behavior analysis and thus benefits the mining of user requirements, which can be used to personalized recommendations and product improvements, and eventually achieve higher commercial profits.