Abstract: According to an aspect, software test failures are analyzed using natural language processing (NLP) and machine learning. A failure is detected during a code build associated with a software product. Each change set since a last successful code build associated with the software product is identified and analyzed using NLP to extract change set features. A software defect origin model is applied to the extracted features in each analyzed change set to detect an origin of the failure.

Abstract: According to an aspect, software test failures are analyzed using natural language processing (NLP) and machine learning. A failure is detected during a code build associated with a software product. Each change set since a last successful code build associated with the software product is identified and analyzed using NLP to extract change set features. A software defect origin model is applied to the extracted features in each analyzed change set to detect an origin of the failure.

Abstract: According to an aspect, software test failures are analyzed using natural language processing (NLP) and machine learning. A failure is detected during a code build associated with a software product. Each change set since a last successful code build associated with the software product is identified and analyzed using NLP to extract change set features. A software defect origin model is applied to the extracted features in each analyzed change set to detect an origin of the failure. The software defect origin model includes a mathematical description of patterns learned from previously detected failures and their corresponding features.

Abstract: A computer-implemented method is provided for determining quality metrics for a question set. In an implementation, a test question set model may be produced based upon calculated quality metrics of a test question set with respect to a test corpus, and including features representing quality metrics. The test question set model may be compared to a baseline question set model based on a distance calculated between one or more projected model features of the baseline question set model and one or more runtime model features of the test question set model. Contents of the test question set may be adjusted based upon the calculated distance.

Abstract: A computer-implemented method is provided for determining quality metrics for a question set. In an implementation, a test question set model may be produced based upon calculated quality metrics of a test question set with respect to a test corpus, and including features representing quality metrics. The test question set model may be compared to a baseline question set model based on a distance calculated between one or more projected model features of the baseline question set model and one or more runtime model features of the test question set model. Contents of the test question set may be adjusted based upon the calculated distance.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a user interface automation tool. The user interface automation tool executes a script to perform automation functions on user interface controls in a user interface of an application. Responsive to automation of a given user interface control failing, the user interface automation tool identifies a candidate user interface control that is the same as a user interface control expected in the script using a machine learning model. The user interface automation tool corrects the script to refer to the candidate user interface control to form a corrected script. The user interface automation tool performs a user interface function on the candidate user interface control according the corrected script.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a user interface automation tool. The user interface automation tool executes a script to perform automation functions on user interface controls in a user interface of an application. Responsive to automation of a given user interface control failing, the user interface automation tool identifies a candidate user interface control that is the same as a user interface control expected in the script using a machine learning model. The user interface automation tool corrects the script to refer to the candidate user interface control to form a corrected script. The user interface automation tool performs a user interface function on the candidate user interface control according the corrected script.

Abstract: According to an aspect, software test failures are analyzed using natural language processing (NLP) and machine learning. A failure is detected during a code build associated with a software product. Each change set since a last successful code build associated with the software product is identified and analyzed using NLP to extract change set features. A software defect origin model is applied to the extracted features in each analyzed change set to detect an origin of the failure. The software defect origin model includes a mathematical description of patterns learned from previously detected failures and their corresponding features.

Abstract: A computer-implemented method, computer program product, and system are provided for determining quality metrics for a question set. In an implementation, a test question set model may be produced based upon calculated quality metrics of a test question set with respect to a test corpus, and including features representing quality metrics. The test question set model may be compared to a baseline question set model based on a distance calculated between one or more projected model features of the baseline question set model and one or more runtime model features of the test question set model. Contents of the test question set may be adjusted based upon the calculated distance.

Abstract: A computer-implemented method is provided for determining quality metrics for a question set. In an implementation, a test question set model may be produced based upon calculated quality metrics of a test question set with respect to a test corpus, and including features representing quality metrics. The test question set model may be compared to a baseline question set model based on a distance calculated between one or more projected model features of the baseline question set model and one or more runtime model features of the test question set model. Contents of the test question set may be adjusted based upon the calculated distance.

Abstract: Releasing pending messages is provided. An electronic message is received via a network from a sending client device. The electronic message includes a request to place the electronic message in a pending file and an authorization to release data. The electronic message is placed in the pending file and the authorization to release data is stored in a storage device. An authorization is received to release the electronic message from the pending file. Then, it is determined whether the authorization to release the electronic message matches the stored authorization to release data. In response to determining that a match is found, the electronic message is released from the pending file. Then, the released electronic message is sent to a receiving client device via the network.

Abstract: A system for releasing pending messages. An electronic message is received via a network from a sending client device. The electronic message includes a request to place the electronic message in a pending file and an authorization to release data. The electronic message is placed in the pending file and the authorization to release data is stored in a storage device. An authorization is received to release the electronic message from the pending file. Then, it is determined whether the authorization to release the electronic message matches the stored authorization to release data. In response to determining that a match is found, the electronic message is released from the pending file. Then, the released electronic message is sent to a receiving client device via the network.

Abstract: The present invention provides a method for process-specific exchange of data between machines in a computer network. Each machine runs a plurality of processes. The method includes providing a plurality of clipboards for at least one machine in the computer network, where each clipboard of the plurality of clipboards corresponds to one of the processes running on the at least one machine; and storing data concerning a process of the plurality of processes onto its corresponding clipboard. Because the method of the present invention is process-based, machines may obtain data concerning certain processes on certain machines without the problem of the latest data on a process being overwritten by another process. Since the clipboards of the present invention may reside on a third party, if one or more of the machines in the network fail or are otherwise unavailable, the data on their clipboards are not lost.