Abstract: A quality analysis tool for visual-programming scripting languages uses machine learning to process changes from visual-programming environments. The quality analysis tool can receive data associated with a code submission via a visual-programming scripting language, process the data to identify features in the data that correspond to previously identified defects, apply a pattern matching algorithm to the identified features, determine a risk prediction based on a learned pattern recognition model associated with a pattern in the features, and transmit a notice of predicted risk. The quality analysis tool can train models for use with visual-programming scripting languages and visual-programming environments.

Abstract: A visual anomaly detection system can test a video game under test. The testing can involve applying captured video frames to a large language model using dynamically generated prompts. The captured video frames can be obtained directly from an output port of a user computing system enabling the video frames to be applied to the machine learning model without modification and with minimal to no user intervention. Additionally, the systems disclosed herein can control the user computing system hosting the video game under test enabling the test system to react to test results in real-time or near real-time (e.g., within milliseconds, while the video game is executing, before a next action is performed with respect to the video game, and the like) and to modify the testing process as tests are being performed.

Abstract: A quality analysis tool for visual-programming scripting languages uses machine learning to process changes from visual-programming environments. The quality analysis tool can receive data associated with a code submission via a visual-programming scripting language, process the data to identify features in the data that correspond to previously identified defects, apply a pattern matching algorithm to the identified features, determine a risk prediction based on a learned pattern recognition model associated with a pattern in the features, and transmit a notice of predicted risk. The quality analysis tool can train models for use with visual-programming scripting languages and visual-programming environments.

Abstract: A test case selection system and method uses a test selection model to select test cases from a library of test cases to be used for quality assurance (QA) testing of a software application to maximize the chances of finding bugs from executing the selected test cases. The test case selection model may be a machine learning based regression model trained using outcomes of previous QA testing. In some case, the test case selection system may provide periodic and/or continuous refinement of the test case selection model from one QA testing run to the next. The model refinements may include updating weights associated with the test case selection model in the form of a regression model. Additionally, the test case selection system may provide performance analytics between a test case selection model-based selection of test cases and random selection of test cases.

Abstract: A test case selection system and method uses a test selection model to select test cases from a library of test cases to be used for quality assurance (QA) testing of a software application to maximize the chances of finding bugs from executing the selected test cases. The test case selection model may be a machine learning based regression model trained using outcomes of previous QA testing. In some case, the test case selection system may provide periodic and/or continuous refinement of the test case selection model from one QA testing run to the next. The model refinements may include updating weights associated with the test case selection model in the form of a regression model. Additionally, the test case selection system may provide performance analytics between a test case selection model-based selection of test cases and random selection of test cases.

Abstract: A test case selection system and method uses a test selection model to select test cases from a library of test cases to be used for quality assurance (QA) testing of a software application to maximize the chances of finding bugs from executing the selected test cases. The test case selection model may be a machine learning based regression model trained using outcomes of previous QA testing. In some case, the test case selection system may provide periodic and/or continuous refinement of the test case selection model from one QA testing run to the next. The model refinements may include updating weights associated with the test case selection model in the form of a regression model. Additionally, the test case selection system may provide performance analytics between a test case selection model-based selection of test cases and random selection of test cases.

Abstract: A test case selection system and method uses a test selection model to select test cases from a library of test cases to be used for quality assurance (QA) testing of a software application to maximize the chances of finding bugs from executing the selected test cases. The test case selection model may be a machine learning based regression model trained using outcomes of previous QA testing. In some case, the test case selection system may provide periodic and/or continuous refinement of the test case selection model from one QA testing run to the next. The model refinements may include updating weights associated with the test case selection model in the form of a regression model. Additionally, the test case selection system may provide performance analytics between a test case selection model-based selection of test cases and random selection of test cases.