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With the development of computer technology, the old and outdated teaching cases cannot meet requirements on teaching currently. Therefore, in order to solve this problem, we need to rebuild the teaching content and teaching cases. Moreover, in the traditional classroom teaching, each student gets the same practice content, which is not pertinent. In order to solve these two problems, we propose a new method called Software Testing Computer Assistant Education (STCAE), which is based on machine learning, information retrieval, and natural language processing technology. STCAE has three steps: First, STCAE uses NLP to extract the text features from the classified bug reports in the database and to classify all the samples. Second, STCAE scores these bug reports according to the corresponding weight model. Third, STCAE updates the ratings based on the feedbacks of teachers and students on the case. In constructing STCAE, we consider the interactive, creative, pertinent, and error‐correcting capabilities thoroughly in teaching needs, overcoming the four shortcomings of traditional CAI. In addition, we build Software Testing Computer Assistant Education System (STCAES) under STCAE and introduce STCAES into daily teaching. All the achievements in the new course shows that STCAE has achieved great success in practical classroom teaching.

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