International Business Machines Corporation

Derek M Reedy

Timothy B Snow

Nadiya Kochura

Donald H Randall, Jr

Vinodkumar Raghavan

George H Champlin-Scharff

VinodKumar Raghavan Nair

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.

Analyzing software test failures using natural language processing and machine learning

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.

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.

System and method for determining quality metrics for a question set

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.

Automatically correcting GUI automation using machine learning

A technique for generating testcases includes receiving a new product specification for an application. A noun-verb pairing is performed on the new product specification. An attempt is made to locate a similar noun-verb pairing in a previous product specification for the application that corresponds to the new noun-verb pairing. In response to locating the similar noun-verb pairing in the previous product specification, a new testcase is generated by modifying an existing testcase that is associated with the similar noun-verb pairing in the previous product specification. In response to not locating the similar noun-verb pairing in the previous product specification, the fact that the new testcase was not generated is indicated.

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Derek M Reedy