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: A computer-implemented method according to one embodiment includes updating code segments of source code to include predetermined code pieces. The updated code segments are configured to output information about associated test cases upon the test cases being run. The method further includes running the test cases, and subsequent to a change being made to the source code, determining a prioritized subset of the test cases. The prioritized subset of the test cases is run. A computer program product according to another embodiment includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.

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: A method, system and computer program product for processing data are provided. In the method, a request is received to perform an access-controlled operation in a user device. A biometric feature input from an input module of the user device is received for the request. It is determined whether the received biometric feature matching with a primary biometric feature, the primary biometric feature being identified from a plurality of biometric features stored in the user device and being used to authenticate a user for the access-controlled operation. The access-controlled operation is enabled in response to determining the received biometric feature matching with a primary biometric feature.

Abstract: Aspects of the present invention disclose a method for pasting based on semantic fragments. The method includes one or more processors in response to identifying a copy operation request, obtaining copied data. The method further includes splitting the copied data into a plurality of semantic fragments based on semantic analysis. The method further includes rendering the plurality of semantic fragments on a display of a computing device. The method further includes in response to identifying a user selection of at least one of the plurality of semantic fragments, determining whether to paste the at least one of the plurality of semantic fragments.

Abstract: A method, system and computer program product for processing data are provided. In the method, a request is received to perform an access-controlled operation in a user device. A biometric feature input from an input module of the user device is received for the request. It is determined whether the received biometric feature matching with a primary biometric feature, the primary biometric feature being identified from a plurality of biometric features stored in the user device and being used to authenticate a user for the access-controlled operation. The access-controlled operation is enabled in response to determining the received biometric feature matching with a primary biometric feature.