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: 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.