Abstract: A method and system for analyzing a proximity between a first user population and a second user population includes receiving a request to perform a proximity analysis between the first user population and the second user population, accessing data related to the first user population and the second user population, providing the data related to the first user population and the second user population as input to a machine-learning (ML) model for analyzing the data to determine the proximity between the first user population and the second user population, receiving from the ML model as an output at least one of a composite proximity score between the first user population and the second user population, and providing display data relating to the output to a visualization mechanism for display. The composite proximity score may be calculated based on multiple characteristics and/or comparison metrics.

Abstract: A machine learning model can be trained to infer the probability of the presence of categories of a software bug in a source code file. A bug tracker can provide information concerning the category to which a software bug belongs. The bug data supplied to a machine learning model for inferring the presence of particular categories of bugs can be filtered to exclude a specified category or categories of bugs. Information including but not limited to organizational boundaries can be inferred from the category of bugs present in a body of source code. The inferred organization boundaries can be used to generate team-specific machine learning models.

Abstract: Information concerning software bugs including bug detection, bug prediction data and/or historical bug data can be used to determine whether it is safe to commit, integrate, deploy and/or deliver a software change. If the change is deemed unsafe, the change can be rejected automatically. Alternatively, the change can proceed following approval by an administrator, supervisor, implementer, manager and/or other designated approval mechanism. Actions taken to override a block can be recorded along with information concerning failed deployments and/or deliveries, a higher than normal customer failure rate after deployment and/or delivery or through manual data entry.

Abstract: Information concerning software bugs including bug detection, bug prediction data and/or historical bug data can be used to determine whether it is safe to commit, integrate, deploy and/or deliver a software change. If the change is deemed unsafe, the change can be rejected automatically. Alternatively, the change can proceed following approval by an administrator, supervisor, implementer, manager and/or other designated approval mechanism. Actions taken to override a block can be recorded along with information concerning failed deployments and/or deliveries, a higher than normal customer failure rate after deployment and/or delivery or through manual data entry.

Abstract: For each detected bug, historical code with similar characteristics and bug corrections from a historical bug dataset can be displayed in a source code editor. Relevant training and/or testing data can be found by comparing an internal representation of the code under development with an internal representation of the original buggy code in the historical bug dataset. Training and/or testing data that is relevant to the current code can be distinguished from irrelevant training and/or testing data by determining that the code syntax tokens from the current and historical data overlap to at least a specified percentage. Code can be devolved into a set of metrics. The degree of overlap between the metric sets can be determined. If a computed risk factor for the bug correction meets or exceeds a specified threshold, the bug correction can be automatically applied. Additional testing can be automatically performed on and/or added to the corrected code.

Abstract: A machine learning model can be trained to infer the probability of the presence of categories of a software bug in a source code file. A bug tracker can provide information concerning the category to which a software bug belongs. The bug data supplied to a machine learning model for inferring the presence of particular categories of bugs can be filtered to exclude a specified category or categories of bugs. Information including but not limited to organizational boundaries can be inferred from the category of bugs present in a body of source code. The inferred organization boundaries can be used to generate team-specific machine learning models.

Abstract: For each detected bug, historical code with similar characteristics and bug corrections from a historical bug dataset can be displayed in a source code editor. Relevant training and/or testing data can be found by comparing an internal representation of the code under development with an internal representation of the original buggy code in the historical bug dataset. Training and/or testing data that is relevant to the current code can be distinguished from irrelevant training and/or testing data by determining that the code syntax tokens from the current and historical data overlap to at least a specified percentage. Code can be devolved into a set of metrics. The degree of overlap between the metric sets can be determined. If a computed risk factor for the bug correction meets or exceeds a specified threshold, the bug correction can be automatically applied. Additional testing can be automatically performed on and/or added to the corrected code.

Abstract: Information concerning software bugs including bug detection, bug prediction data and/or historical bug data can be used to determine whether it is safe to commit, integrate, deploy and/or deliver a software change. If the change is deemed unsafe, the change can be rejected automatically. Alternatively, the change can proceed following approval by an administrator, supervisor, implementer, manager and/or other designated approval mechanism. Actions taken to override a block can be recorded along with information concerning failed deployments and/or deliveries, a higher than normal customer failure rate after deployment and/or delivery or through manual data entry.

Abstract: A risk factor that software written by a developer includes bugs can be calculated for the developer. The risk factor can be used to determine the quality of the developer's code. The risk factor associated with code produced by a particular developer can be provided to a manager or management system. The risk factor can be used to provide bug-based information to a corporate review and reward process.

Abstract: A probabilistic machine learning model is generated to identify potential bugs in a source code file. Source code files with and without bugs are analyzed to find features indicative of a pattern of the context of a software bug, wherein the context is based on a syntactic structure of the source code. The features may be extracted from a line of source code, a method, a class and/or any combination thereof. The features are then converted into a binary representation of feature vectors that train a machine learning model to predict the likelihood of a software bug in a source code file.

Abstract: Multidimensional key based construction of a filterable hierarchy is provided. A data service initiates operations to construct the hierarchy by joining current node-key elements with an element delimiter into a current node string. Child node elements are also joined with the element delimiter into child node string(s). The child node string(s) are further encapsulated with nesting delimiter(s) and the resulting string is concatenated to the current node string. Next, parent node elements are joined with the element delimiter. The current node-key is generated by concatenating the parent node string to the current node string (that includes the child node string(s)) with a parent delimiter. The current node-key is inserted into a representation for the current node within a data structure.

Abstract: A product or service is enhanced by optimizing success factors associated with the product or service. A enhancement application initiates operations to compute a predicted score (of a success of the product or service) and a suggestion to achieve the predicted score by retrieving performance and/or configuration data associated with existing products or services from a data source. The performance and/or configuration data is analyzed to generate a model of success factors associated with the existing products or services. Next, configuration conditions of a current product are received from a stakeholder. In response, predicted score(s) are computed for the success factors using the model by simulating the configuration conditions of the current product or service on the model. Furthermore, the predicted score(s) and/or suggestion(s) to achieve the predicted score(s) are provided in a visualization to the stakeholder.