International Business Machines Corporation

Vinodkumar Raghavan

Nadiya Kochura

Timothy B Snow

Donald H Randall, Jr

Jennifer G Becker

Kenneth Lee McClamroch

Peter Sun

Derek M Reedy

Rajesh Pravin Thakkar

Kurian John

Shruti Ujjwal

Edwin Charles Berry

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.

Analyzing software test failures using natural language processing and machine learning

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

Disclosed is a verification method and system for a localized computer software application, the method and system comprising identifying, for a current graphical object generated by a graphical user interface of the localized application, a resource corresponding to the current graphical object; comparing the content of the current graphical object with the identified resource; and generating an error event when the content does not match the identified resource.

Verification of software applications

A set of user-configured testing parameters for a software application under test can be received by a test execution plan generation tool. At least one testing objective for the software application can be defined by user-configurable testing parameters. A test execution plan can be automatically generated utilizing the user-configured testing parameters and a predefined test execution plan data model. The predefined test execution plan data model can include testing metadata representing software testing domain data for a software testing system being used to evaluate the software application under test. The testing metadata can specify details of finite shared resources of a test center shared by all projects under test. The generated test execution plan can detail specific allocation of the finite shared resources dedicated towards at least one phase of testing the software application to satisfy the at least one testing objective for the software application.

Automated test execution plan generation

Embodiments of the present invention provide a method, system and computer program product for translation verification of source strings for controls in a target application graphical user interface (GUI). In an embodiment of the invention, a method for translation verification of source strings for controls in a target application GUI can include loading a target GUI for an application under test in a functional testing tool executing in memory by a processor of a computing system, retrieving different translated source strings in a target spoken language for respectively different control elements of the target GUI and, determining a score for each one of the translated source strings. Thereafter, an alert can be provided in the functional testing tool for each translated source string corresponding to a determined score failing to meet a threshold value, such as a score that falls below a threshold value, or a score that exceeds a threshold value.

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Woburn, MA, United States of America

Vinodkumar Raghavan