A method, comprising: receiving, by a device, historical software data associated with a software development platform, the historical software data including data generated by a number of different software development tools associated with the software development platform, such as software testing tools; training, by the device, a neural network model, with the historical software data, to generate a trained neural network model, the neural network model being trained to predict a set of test cases to utilize for testing software associated with the software development platform; receiving, by the device and from the software development platform, historical test configuration data, wherein the historical test configuration data includes data identifying: historical sets of test cases, historical types of test case executions performed for the historical sets of test cases, and historical target devices utilized to perform the test case executions; training, by the device, an execution model, with the historical test configuration data, to generate a trained execution model, the execution model including an artificial intelligence model; receiving, by the device, software data identifying the software to be tested in the software development platform; processing, by the device, the software data, with the trained neural network model, to predict the set of test cases to execute for testing the software in the software development platform; processing, by the device, the set of test cases, with the trained execution model, to identify one or more of configurations, scripts, or test targets for executing the set of test cases in the software development platform; and performing, by the device, actions based on the set of test cases and the one or more of the configurations, the scripts, or the test targets, wherein performing the actions comprises: causing the set of test cases to be executed in the software development platform based on the one or more of the configurations, the scripts, or the test targets; providing recommendations for corrections to the software based on results of execution of the set of test cases; causing the corrections to the software to be automatically implemented by the software development platform; and retraining the execution model based on the set of test cases and the one or more of the configurations, the scripts, or the test targets. receiving, by a device, historical software data associated with a software development platform, the historical software data including data generated by a number of different software development tools associated with the software development platform, such as software testing tools; training, by the device, a neural network model, with the historical software data, to generate a trained neural network model, the neural network model being trained to predict a set of test cases to utilize for testing software associated with the software development platform; receiving, by the device and from the software development platform, historical test configuration data, wherein the historical test configuration data includes data identifying: historical sets of test cases, historical types of test case executions performed for the historical sets of test cases, and historical target devices utilized to perform the test case executions; historical sets of test cases, historical types of test case executions performed for the historical sets of test cases, and historical target devices utilized to perform the test case executions; training, by the device, an execution model, with the historical test configuration data, to generate a trained execution model, the execution model including an artificial intelligence model; receiving, by the device, software data identifying the software to be tested in the software development platform; processing, by the device, the software data, with the trained neural network model, to predict the set of test cases to execute for testing the software in the software development platform; processing, by the device, the set of test cases, with the trained execution model, to identify one or more of configurations, scripts, or test targets for executing the set of test cases in the software development platform; and performing, by the device, actions based on the set of test cases and the one or more of the configurations, the scripts, or the test targets, wherein performing the actions comprises: causing the set of test cases to be executed in the software development platform based on the one or more of the configurations, the scripts, or the test targets; providing recommendations for corrections to the software based on results of execution of the set of test cases; causing the corrections to the software to be automatically implemented by the software development platform; and retraining the execution model based on the set of test cases and the one or more of the configurations, the scripts, or the test targets. causing the set of test cases to be executed in the software development platform based on the one or more of the configurations, the scripts, or the test targets; providing recommendations for corrections to the software based on results of execution of the set of test cases; causing the corrections to the software to be automatically implemented by the software development platform; and retraining the execution model based on the set of test cases and the one or more of the configurations, the scripts, or the test targets.