Test Case Generation via SOW Segmentation and Knowledge Graph Retrieval
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Solution Overview
Problem
Current methods for generating test cases in software projects are inefficient and lack accuracy, as they do not effectively utilize project documentation and historical test cases to align with product requirements, leading to suboptimal quality assurance.
Innovation Solution
A method and electronic device for generating test cases by obtaining an original statement of work, segmenting it, and combining it with product documentation and historical test cases to create targeted test cases, using techniques such as modal normalization and knowledge graph retrieval to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional test case generation methods are used, then the process is simple, but the accuracy and alignment with product requirements deteriorate
Solution Approach 1:
The original SOW is segmented into multiple segmented SOWs, and each segmented SOW is processed independently to generate corresponding test cases. This segmentation allows the system to handle complex requirements in manageable units while maintaining overall accuracy and alignment with product requirements.
Solution Approach 2:
A knowledge graph is constructed as an intermediary structure that stores and organizes product documentation and historical test cases. The knowledge graph serves as a mediator between the segmented SOWs and the generated test cases, enabling accurate retrieval and alignment without requiring direct complex processing between all input elements.
2Reliability
If comprehensive product documentation and historical test cases are utilized, then test case quality improves, but processing time increases
Solution Approach 1:
The knowledge graph is constructed in advance by pre-processing and organizing product documentation and historical test cases before the actual test case generation begins. This preliminary action ensures that when test cases need to be generated, the system can quickly retrieve relevant information from the pre-organized knowledge graph rather than processing raw documentation in real-time.
Solution Approach 2:
Historical test cases are incorporated into the knowledge graph and used as reference feedback during the generation process. The system retrieves similar historical test cases and uses them to improve the quality and relevance of newly generated test cases, creating a feedback loop that enhances reliability without requiring complete re-processing of all historical data each time.
3Productivity
If manual test case creation is used, then flexibility is maintained, but productivity deteriorates
Solution Approach 1:
The system performs automated test case generation by processing segmented SOWs through the knowledge graph and generating test cases automatically without requiring manual intervention for each test case creation. This self-service approach significantly improves productivity while maintaining ease of operation through a simple workflow of inputting the original SOW and retrieving generated test cases.
Data Source
AI summary
The disclosure discloses a method for generating test cases, an apparatus for generating test cases, an electronic device and a storage medium, and relates to a field of computer technology, in particular to a field of artificial intelligence such as a large model and natural language processing. The method includes: obtaining an original statement of work (SOW) of a target project and product information of a software product to be tested associated with the target project; obtaining product documentation and target historical test cases of the software product to be tested according to the product information; segmenting the original SOW and obtaining a plurality of segmented SOWs; and generating target test cases of the software product to be tested according to the plurality of segmented SOWs, the product documentation and the target historical test cases.