Abstract: A change tracking and analytics system and method receives software code blocks from one or more repositories of software related to a software project, such as a new video game. Changes associated with software code blocks, such as relative to previous versions of the software code blocks, may be determined and logged. Additionally, various analytics, such as metrics associated with complexity, divergence from a master version of software code blocks, and/or any cascading effects of the software code blocks may be generated and stored in association with the software code blocks. The change information and analytics may then be used to generate any variety of reports indicating complexity, divergence, or the like over time, information related to software code blocks, and/or information related to behavior of software teams. The change information may also be used to make changes to allocated resources, such as quality assurance resources and/or software engineering resources.

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 change tracking and analytics system and method receives software code blocks from one or more repositories of software related to a software project, such as a new video game. Changes associated with software code blocks, such as relative to previous versions of the software code blocks, may be determined and logged. Additionally, various analytics, such as metrics associated with complexity, divergence from a master version of software code blocks, and/or any cascading effects of the software code blocks may be generated and stored in association with the software code blocks. The change information and analytics may then be used to generate any variety of reports indicating complexity, divergence, or the like over time, information related to software code blocks, and/or information related to behavior of software teams. The change information may also be used to make changes to allocated resources, such as quality assurance resources and/or software engineering resources.

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 change tracking and analytics system and method receives software code blocks from one or more repositories of software related to a software project, such as a new video game. Changes associated with software code blocks, such as relative to previous versions of the software code blocks, may be determined and logged. Additionally, various analytics, such as metrics associated with complexity, divergence from a master version of software code blocks, and/or any cascading effects of the software code blocks may be generated and stored in association with the software code blocks. The change information and analytics may then be used to generate any variety of reports indicating complexity, divergence, or the like over time, information related to software code blocks, and/or information related to behavior of software teams. The change information may also be used to make changes to allocated resources, such as quality assurance resources and/or software engineering resources.

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: Embodiments disclosed herein include a system that is capable of processing test data across multiple sessions of a video game. In some cases, the tests are performed over multiple video games that share a game engine. The generated test data may be analyzed as the test is being performed and key performance indicators may be extracted from the test data reducing the test data by at least an order of magnitude. Further, the extracted key performance indicators are used to automatically conduct further testing, such as regression testing, based on an analysis of the key performance indicators with respect to trend data generated from prior tests of the video game or of a shared game engine used by multiple video games.

Abstract: A change tracking and analytics system and method receives software code blocks from one or more repositories of software related to a software project, such as a new video game. Changes associated with software code blocks, such as relative to previous versions of the software code blocks, may be determined and logged. Additionally, various analytics, such as metrics associated with complexity, divergence from a master version of software code blocks, and/or any cascading effects of the software code blocks may be generated and stored in association with the software code blocks. The change information and analytics may then be used to generate any variety of reports indicating complexity, divergence, or the like over time, information related to software code blocks, and/or information related to behavior of software teams. The change information may also be used to make changes to allocated resources, such as quality assurance resources and/or software engineering resources.

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: Methods for providing crash clustering with game state data are provided. In one aspect, a method includes receiving a plurality of crash reports associated with an application, wherein each of the plurality of crash reports includes a call stack and a plurality of game state parameters. The method also includes determining, by a machine learning algorithm, a plurality of clusters for grouping the plurality of crash reports, wherein each of the plurality of clusters is associated with at least one game state parameter of the plurality of game state parameters. The method also includes associating each of the plurality of crash reports with one of the plurality of clusters. The method also includes selecting a priority cluster from the plurality of clusters. The method also includes causing a quality assurance action to be performed based on the selected priority cluster. Systems and machine-readable media are also provided.