Stack Overflow has become one of the most influential platforms for developers to share knowledge and exchange practical coding solutions. With millions of reusable code snippets contributed by users worldwide, it plays a vital role in modern software development. However, the collaborative and informal nature of the platform may inadvertently promote insecure coding practices, particularly when contributors lack adequate secure coding knowledge. These practices can introduce security weaknesses into software systems, posing serious risks when such code is reused in production environments. This paper empirically investigates the prevalence of Common Weakness Enumeration (CWE) instances in C# code snippets on Stack Overflow and analyzes how such security-relevant code evolves through user revisions. We further examine the extent to which Stack Overflow users are aware of and apply the Microsoft C# Secure Coding Guidelines. The results of our study show that: (1) Users repeatedly post code snippets containing the same security weaknesses. Notably, 40.2% of the latest versions of such answers were marked as “accepted” by the original askers.(2) Revisions to code containing these weaknesses are more likely to deteriorate, whereas users with greater C# experience are more likely to reduce them.(3) More than half of the survey respondents indicated that they were not aware of the Microsoft C# Secure Coding Guidelines. The top three approaches they expected to use for addressing security issues were: marking them as insecure, fixing the issues themselves, and seeking help from the Stack Overflow community.

With the applications of intelligent systems in areas (such as self-driving cars, robotics, and smart cities), the impact of these intelligent systems’ defects cannot be ignored. For example, in a recent report, the self-driving car collided with another self-driving car because it incorrectly identified a roadblock. Therefore, it is necessary to conduct adequate testing of intelligent systems to avoid dangerous behaviors as much as possible. However, due to the particularity of its own structure, the low efficiency, and the high cost of manual collection the large-scale test cases, it is important and challenging to design tools to test the adequacy of intelligent systems.To overcome the above problems, we propose an intelligent system test adequacy evaluation tool ISTA. ISTA implements the automatic generation and optimization of test cases based on coverage analysis, which can improve the test adequacy of the intelligent system while expanding the dataset. To evaluate the usefulness of our developed tool, we analyze the application of ISTA on the five-layer fully-connected dnn model and german credit dataset (text data type) for binary classification as well as on the Rambo model and hmb dataset (image data type) for self-driving car. The evaluation results show that the test dataset is expanded and the models are more fully tested after ISTA’s test case generation and optimization for both text and image data types, with a corresponding increase in the average 80% coverage criteria used.