International Business Machines Corporation

Timothy B Snow

George H Champlin-Scharff

Derek M Reedy

Nadiya Kochura

Donald H Randall, Jr

Sabrina Yee

Vinodkumar Raghavan

Janice Marie Girouard

Steven Francis Best

Lorelei M McCollum

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

A tool for automatic pre-detection of potential software product impact according to a statement placed in a software development system, and for automatically recommending for resolutions which accesses a repository of information containing a history of changes and effects of the changes for a software project; using a received a statement in natural language to perform a natural language search of the repository; according to the findings of the search of the repository, using a machine learning model to compose an impact prediction regarding the received statement relative to the findings; and automatically placing an advisory notice regarding to the impact prediction into the software development system, wherein the advisory notice is associated with the received statement.

Automatic pre-detection of potential coding issues and recommendation for resolution actions

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.

Automatically generating testcases

Embodiments of the present invention provide a method, system and computer program product for the automated suggestion of code usage patterns in an IDE. In an embodiment of the invention, a method for automated suggestion of code usage patterns in an IDE is provided. The method includes receiving input of a code snippet for a source code file of a software development project loaded into memory of a host computer for display through an IDE. The method further includes matching terms of the received input to terms in different blocks of code in different source code files of the software development project. The method yet further includes selecting one of the different blocks of code as a code completion choice for insertion into the source code file in the IDE. Finally, the method includes inserting the selected one of the different blocks of code into the source code file in the IDE.

Code suggestion in a software development tool

Techniques for automatically generating testcases

The disclosure provides a solution for delegating email messages for human summaries. In the solution, an email message in an inbox of an account holder can be identified, where the email message has a read status of unread. The email message can be from a sender and can comprise email content. The email message can be sent from the inbox of the account holder to a delegate. The delegate can be associated with an email address corresponding to a human who is not the account holder or the sender. A summary can be received for the email content from the delegate. The summary can be presented in a user interface to the account holder. Responsive to presenting the summary, the read status of the email message can be changed from unread to read.

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Westford, MA, United States of America

Timothy B Snow