In the big data environment, software testing faces challenges such as a massive increase in data volume, higher demands for processing speed, increased data diversity, and issues related to data authenticity. To address these challenges, software testing must incorporate technical skills, business understanding, and interdisciplinary abilities. Simultaneously, testing requirements are evolving towards real-time, automation, and intelligence, with security becoming increasingly crucial. Currently, software testing education in universities suffers from slow curriculum updates and limited practical opportunities, leading to a shortage of qualified professionals in the market. Thus, it is recommended to establish a multi-level course system, covering basic, intermediate, and advanced courses that systematically teach big data testing techniques. Moreover, a project-driven and practice-oriented teaching model should be adopted to enhance students' practical skills through real or simulated projects. Additionally, establishing a scientific evaluation and feedback mechanism is essential to ensure that students can address their weaknesses in a timely manner. Educational institutions must dynamically adjust course content in response to industry changes, maintain close connections with the industry, and support continuous learning and career development to keep pace with rapid advancements in big data technology.
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