Abstract: Example embodiments relate to generating sets of recommended inputs for changing predicted results of a predictive model. The examples disclosed herein access, from a database, a historical set of inputs and results of a predictive model. A function is approximated based on the historical set of inputs and results, and a gradient of the function is computed using a result of the function with respect to a local maximum value of the function. A set of recommended inputs is generated based on the gradient of the function, where a recommended input produces a positive result of the function.

Abstract: Techniques to create and use cluster models to predict build failures are provided. In one aspect, clusters in a set of builds may be identified. The identified clusters may be used to create a model. The model may be used to predict causes of build failures. In another aspect, a failed build may be identified. A clustering model may be retrieved. A cause of problems with the failed build may be predicted using the clustering model.

Abstract: Example embodiments relate to determining code coverage based on production sampling. In example embodiments, a production execution data set that includes metrics for code units of a software application is obtained, where the metrics include input and output values for each of the code units and an average execution count for each of the code units. Further, application code execution is tracked during a testing procedure of the software application to determine executed lines of code. At this stage, production code coverage of the software application is determined based on the production execution data set and the executed lines of code.

Abstract: Example embodiments relate to generating sets of recommended inputs for changing predicted results of a predictive model. The examples disclosed herein access, from a database, a historical set of inputs and results of a predictive model. A function is approximated based on the historical set of inputs and results, and a gradient of the function is computed using a result of the function with respect to a local maximum value of the function. A set of recommended inputs is generated based on the gradient of the function, where a recommended input produces a positive result of the function.

Abstract: Techniques to create and use cluster models to predict build failures are provided. In one aspect, clusters in a set of builds may be identified. The identified clusters may be used to create a model. The model may be used to predict causes of build failures. In another aspect, a failed build may be identified. A clustering model may be retrieved. A cause of problems with the failed build may be predicted using the clustering model.

Abstract: According to an example, a Trie is formed from URLs and nodes of the Trie are assigned a weight. A node is selected based on its weight and child nodes of the selected node merged together. A URL classification is output based on a path in the Trie.

Abstract: Example embodiments relate to classifying application protocol interfaces (APIs) in terms of their relation to user experience. Example embodiments may determine that a user action occurred and monitor a first thread separate from a user interface (UI) thread of the computing device. The first thread may process a first API in response to the occurrence of the user action. A determination may be made as to whether the first API being processed on the first thread is related to the user action. Responsive to the first API being related to the user action, a determination may be made as to whether the first API causes the first thread to update the UI thread of the computing device. Responsive to the UI thread being updated, the first API may be tagged as affecting user experience.

Abstract: Example embodiments relate to classifying application protocol interfaces (APIs) in terms of their relation to user experience. Example embodiments may determine that a user action occurred and monitor a first thread separate from a user interface (UI) thread of the computing device. The first thread may process a first API in response to the occurrence of the user action. A determination may be made as to whether the first API being processed on the first thread is related to the user action. Responsive to the first API being related to the user action, a determination may be made as to whether the first API causes the first thread to update the UI thread of the computing device. Responsive to the UI thread being updated, the first API may be tagged as affecting user experience.

Abstract: Example embodiments relate to determining code coverage based on production sampling. In example embodiments, a production execution data set that includes metrics for code units of a software application is obtained, where the metrics include input and output values for each of the code units and an average execution count for each of the code units. Further, application code execution is tracked during a testing procedure of the software application to determine executed lines of code. At this stage, production code coverage of the software application is determined based on the production execution data set and the executed lines of code.

Abstract: Disclosed herein are a system, non-transitory computer-readable medium, and method for responding to natural language queries. Keywords likely to appear in a natural language query are determined and each likely keyword is associated with a module. A response to a natural language query comprises information generated by each module associated with a likely keyword appearing in the natural language query.

Abstract: According to an example, a Trie is formed from URLs and nodes of the Trie are assigned a weight. A node is selected based on its weight and child nodes of the selected node merged together. A URL classification is output based on a path in the Trie.

Abstract: Disclosed herein are a system, non-transitory computer-readable medium, and method for responding to natural language queries. Keywords likely to appear in a natural language query are determined and each likely keyword is associated with a module. A response to a natural language query comprises information generated by each module associated with a likely keyword appearing in the natural language query.