Abstract: A code path autosuggestion system retrieves, from a repository, defect data associated with a first software defect. Using the defect data, the code path autosuggestion system searches a dataset for a second software defect, the second software defect associated with the first software defect. As a result of the search, the code path autosuggestion system determines a set of regions of source code associated with the second software defect. The code path autosuggestion system uploads the set of regions of source code to the repository as candidates for patching the first software defect.

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: A document that includes a plurality of textual portions is accessed. At least some of the textual portions include an input command that is configured to be directed to a process. For each respective textual portion that includes an input command the respective textual portion is input to a machine-learning model (MLM) that has been trained with a plurality of textual phrases and process identifiers that identify corresponding processes, a process identifier that identifies a process to which the input command included in the respective textual portion is to be directed is received from the MLM, and a command record that identifies the input command is stored in a data structure.

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 for providing an unsupervised classification model by converting unsupervised data to supervised data. In one implementation, a processing device can receive an unlabeled dataset comprising one or more data records. The processing device can divide the unlabeled dataset into a plurality of groups. The processing device can then generate, for each group of the plurality of groups, a corresponding label. The processing device can generate a labeled dataset by assigning, to each group of the plurality of groups, the corresponding label. The processing device can then classify the labeled dataset using a classification model.