4 results about "Test graph" patented technology

The application relates to the technical field of software test automation, and provides a requirement-driven test case automatic generation method and system based on a semantic test graph, which comprises the following steps: performing multidimensional static analysis on source code, respectively constructing an abstract syntax tree, a control flow graph and a function call graph, and extracting function call relations and data flow dependency relations; constructing a semantic test graph by taking functions as nodes, taking function call relations as main edges and taking data flow dependency relations as auxiliary edges, and aggregating function nodes in the semantic test graph into several function modules; obtaining several test requirement nodes, calculating semantic similarity between the test requirement nodes and the function modules, establishing a mapping relationship from the test requirement nodes to the function modules, and forming a requirement-program structure association graph; and based on the requirement-program structure association graph, generating a test intention for each test requirement node, and converting the test intention into an executable test case. The highly corresponding relationship between the generated test case and the business requirement is ensured.

This application provides an OPC model training method and an OPC model training device, relating to the field of model training technology. The method includes: cutting a chip design layout file to obtain multiple basic blocks; adding noise to the multiple basic blocks to obtain multiple noisy blocks; training a graph generator based on the multiple noisy blocks and corresponding real noise; using the graph generator to generate multiple test graphs based on multiple preset noise vectors; and training the model based on the multiple test graphs to obtain an OPC model. By training the graph generator based on the multiple noisy blocks and corresponding real noise, the graph generator implicitly learns the data distribution of the graphs, that is, it learns the rules of chip design. This results in multiple test graphs generated by the graph generator having more diverse forms and conforming to design rules. The OPC model trained based on these multiple test graphs is also more accurate and reliable.

This invention discloses a method and apparatus for evaluating medical language models based on dynamic cost graph matching and inference path logic review, belonging to the interdisciplinary field of artificial intelligence and medical informatics. The proposed technical solution includes: constructing a knowledge graph; receiving the output content of the medical language model under test and parsing it into a test graph; vectorizing and optimizing the node text information of the knowledge graph and the test graph; calculating the graph edit distance between the test graph and the knowledge graph based on a graph edit distance algorithm, using dynamically calculated node replacement costs, converting it into a similarity score, normalizing it, and then calculating the local and global similarity scores between the test graph and the knowledge graph; using a depth-first search algorithm to detect logical loops, or detecting semantic coherence by calculating the embedding vector similarity of adjacent nodes on the path; and finally generating an evaluation report. This invention achieves automated, multi-dimensional, and in-depth evaluation of the output content of medical language models.

The application relates to the technical field of software test automation, and provides a requirement-driven test case automatic generation method and system based on a semantic test graph, which comprises the following steps: performing multidimensional static analysis on source code, respectively constructing an abstract syntax tree, a control flow graph and a function call graph, and extracting function call relations and data flow dependency relations; taking a function as a node, taking a function call relation as a main edge, and taking a data flow dependency relation as an auxiliary edge to construct a semantic test graph; aggregating function nodes in the semantic test graph into a plurality of function modules; obtaining a plurality of test requirement nodes, calculating semantic similarity between the test requirement nodes and the function modules, establishing a mapping relation from the test requirement nodes to the function modules, and forming a requirement-program structure association graph; based on the requirement-program structure association graph, generating a test intention for each test requirement node, and converting the test intention into an executable test case. The highly corresponding relation between the generated test case and the business requirement is ensured.