Abstract: Provided is a panel type radiator with reinforcing ribs and cooling fins in lintel welding, which includes multiple cooling fins, two oil collecting tubes, multiple reinforcing ribs and multiple welding seams. Each cooling fin is formed by welding two cooling sub-fins oppositely arranged. An edge of a long side of the cooling fin intersects with an outer surface of the reinforcing rib, and a gap suitable for a lintel-shaped weld is provided at an intersection. A welding material droplet weld is formed between an outer edge at an attached position of the long side of the cooling fin and the outer surface of the reinforcing rib, and the shape of the weld is lintel-shaped. The lintel-shaped weld is divided into three regions. The welding material fusion welding connection lintel region is self-fusion welding metal of the welding material.

Abstract: The present disclosure discloses a defect location method and device based on coverage information, the method including: characterizing program structure information and test case coverage information of a target program in a graph to obtain a graph-characterized structure; generating a node attribute sequence and an adjacency matrix based on the graph-characterized structure; and inputting the node attribute sequence and the adjacency matrix to a trained graph neural network model, so that the graph neural network model outputs a suspicious degree list of the target program based on the node attribute sequence and the adjacency matrix. Through the graph-based unified information characterization, the coverage information can be saved without loss and compression, and the structure information may also be considered, thereby improving the accuracy of defect location.

Abstract: The present disclosure discloses a defect location method and device based on coverage information, the method including: characterizing program structure information and test case coverage information of a target program in a graph to obtain a graph-characterized structure; generating a node attribute sequence and an adjacency matrix based on the graph-characterized structure; and inputting the node attribute sequence and the adjacency matrix to a trained graph neural network model, so that the graph neural network model outputs a suspicious degree list of the target program based on the node attribute sequence and the adjacency matrix. Through the graph-based unified information characterization, the coverage information can be saved without loss and compression, and the structure information may also be considered, thereby improving the accuracy of defect location.