Abstract: Techniques disclosed herein relate to improving quality of classification models for differentiating different user intents by improving the quality of training samples used to train the classification models. Pairs of user intents that are difficult to differentiate by classification models trained using the given training samples are identified based upon distinguishability scores (e.g., F-scores). For each of the identified pairs of intents, pairs of training samples each including a training sample associated with a first intent and a training sample associated with a second intent in the pair of intents are ranked based upon a similarity score between the two training samples in each pair of training samples. The identified pairs of intents and the pairs of training samples having the highest similarity scores may be presented to users through a user interface, along with user-selectable options or suggestions for improving the training samples.

Abstract: Systems, devices, and methods of the present invention relate to text classification. A text classification system accesses an utterance of text. The utterance includes at least one word. The text classification system generates a parse tree for the utterance. The parse tree includes at least one terminal node with a word type. The terminal node represents a word of the utterance. The text classification system applies one or more rules to the text. The text classification system then classifies the utterance as a question or a request for an autonomous agent to perform an action.

Abstract: A system and method for using an automated process to identify bugs in software code. The system can be used to assist with the development of large and complex software products. File-level and/or line-level code coverage information is used to automatically trace-test failures to development changes within the product source code over a specified period of time. Information as to the health of a software product and the test criteria it passes or fails is compared at a first time, when the product may have satisfied all test criteria, with the health of the product at a second time when the same criteria may be failing. This information can then be used to narrow down and/or identify specific product failures to a particular change or set of changes in the software code, before any manual analysis need be done.

Abstract: A system and method for using an automated process to identify bugs in software code. The system can be used to assist with the development of large and complex software products. File-level and/or line-level code coverage information is used to automatically trace-test failures to development changes within the product source code over a specified period of time. Information as to the health of a software product and the test criteria it passes or fails is compared at a first time, when the product may have satisfied all test criteria, with the health of the product at a second time when the same criteria may be failing. This information can then be used to narrow down and/or identify specific product failures to a particular change or set of changes in the software code, before any manual analysis need be done.