Abstract: In one example described herein a system can receive, by a server, a software test suite comprising a plurality of data files. Each data file of the plurality of data files can correspond to a test of the software test suite. The system can generate, by the server, a mapping that assigns, for each data file, a qubit of a quantum computer system to the data file. The system can generate, by the server, a quantum assembly language (QASM) file that indicates the mapping and one or more properties associated with the qubit for each data file. The system can send, by the server, the QASM file to the quantum computer system for execution by the quantum computer system. The system can cause, by the server, an action based on an output generated by the quantum computer system.

Abstract: Systems and methods are described for automated classification of defective code from bug tracking tool data. An example method includes receiving a plurality of datasets representing a plurality of bug reports from a bug tracking application. Each dataset may be generated by vectorizing and clustering a source code associated with a respective bug report represented by the dataset. Each dataset may comprise a plurality of classes. At least one class of each dataset may indicate at least one known bug. For each dataset of the plurality of datasets, a respective supervised feature vector may be generated. Each supervised feature vector may be associated with an index of the at least one class with the at least one known bug. Using the supervised feature vectors, a classification model is trained to detect a new bug presence in a new source code.

Abstract: Systems and methods are described for automated classification of defective code from bug tracking tool data. An example method includes receiving a plurality of datasets representing a plurality of bug reports from a bug tracking application. Each dataset may be generated by vectorizing and clustering a source code associated with a respective bug report represented by the dataset. Each dataset may comprise a plurality of classes. At least one class of each dataset may indicate at least one known bug. For each dataset of the plurality of datasets, a respective supervised feature vector may be generated. Each supervised feature vector may be associated with an index of the at least one class with the at least one known bug. Using the supervised feature vectors, a classification model is trained to detect a new bug presence in a new source code.