Method and apparatus for generating test cases based on natural language processing

By generating structured test cases based on natural language processing, the problem of low efficiency and unstable quality in test case generation in existing technologies is solved. It achieves efficient and accurate automated generation of test cases and risk identification, meets regulatory requirements, and improves test coverage and applicability.

CN120653568BActive Publication Date: 2026-06-26HANGZHOU ZHONGMING ENGINEERING TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510858957.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-06-25
Publication Date
2026-06-26
Estimated Expiration
2045-06-25

AI Technical Summary

Technical Problem

Existing technologies in software testing suffer from low efficiency in test case generation, unstable quality, weak risk identification capabilities, insufficient compliance assurance, and limited test path construction capabilities, making it difficult to cope with the testing needs of complex and ever-changing business scenarios and high-risk functional modules.

Method used

A natural language processing-based approach is adopted to extract key entity information through preprocessing to generate a structured requirement table. This table is then combined with a rule base and semantic similarity algorithm to match test templates, dynamically populate test cases, and introduce a multi-dimensional risk assessment model to mark risk levels and compliance requirements, thereby dynamically creating and expanding test paths.

Benefits of technology

It significantly improves the automation and accuracy of test case generation, reduces manual intervention, ensures that the generated test cases meet regulatory standards, and enhances test coverage and applicability, especially its test adaptability in high-risk scenarios.

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Abstract

The application provides a test case generation method and device based on natural language processing, which comprises: preprocessing input data and generating a structured requirement table; matching a rule base to determine compliance requirements; selecting the most similar test template through a semantic similarity algorithm; dynamically filling parameters to generate test cases and supplement complete content; outputting structured test cases and marking risk levels. The risk level is determined by a weighted fusion algorithm that comprehensively evaluates dimensions such as semantic similarity, risk matching degree, compliance matching degree, and template usage frequency. For high-risk test cases, the application also extends the test path by creating and associating action test nodes. The application realizes automatic generation of test cases through natural language processing technology, significantly improves the efficiency and accuracy of natural language processing, reduces the cost of manual testing, enhances the test coverage and complex scenario adaptability through multi-dimensional risk assessment and path expansion mechanism, and ensures that the test cases meet the requirements of regulatory standards.
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