International Business Machines Corporation

Michael E Gildein

Moses J Vaughan

Rajaram B Krishnamurthy

Andrew C M Hicks

Vijai Kalathur

Eli M Dow

Ryan Thomas Rawlins

Michael Terrence Cohoon

Christine D Mikijanic

Erin M Farr

Deborah A Furman

James Paul Gilchrist

Daniel Nicolas Gisolfi

Muneeb Arshad

Bryan P Davidson

Angelo M Quadara

James A O'Connor

Eitan D Farchi

Daniel A Rogers

Orna Raz-Pelleg

Sergey Novikov

Robert Peter Catalano

Tyler Vezio Rimaldi

Christopher G Brooker

Richard D Prewitt, Jr

William George White

Aharon Brodie

Daniel John FitzGerald

Kiera Caponi

Andrew Cm Hicks

Steven P LaFalce

Aspects of the invention include mutating each neural network of a portion of a first array of neural networks, wherein each neural network of the first array of neural networks is configured to select a respective sequence of test cases for testing a computing infrastructure. Causing each neural network of a second array of neural networks to select a respective sequence of test cases for testing the computing infrastructure. Generating a child neural network by performing a crossover operation between a mutated neural network of the portion of the first array and a neural network of the second array of neural networks, the child neural network generating a new sequence of test cases for testing the computing infrastructure.

Reinforcement learning for testing suite generation

A method of analyzing code is provided. The method includes generating an abstract representation of the code, identifying conditional statements in the abstract representation, populating a truth table for each conditional statement that has been identified with all possible outcomes of the conditional statement and assessing the truth table for each conditional statement to identify issues.

Using big code to construct code conditional truth tables

Techniques for vector-based identification of software dependency relationships are described herein. An aspect includes determining a first dependency relationship value between a first code segment and a second code segment. Another aspect includes calculating a magnitude vector based on the first dependency relationship value and a second dependency relationship value corresponding to the first code segment and the second code segment. Another aspect includes determining a relationship score for the first code segment and the second code segment based on the magnitude vector and the first dependency relationship value.

Vector-based identification of software dependency relationships

Techniques for subject matter expert identification for computer software are described herein. An aspect includes receiving a search string. Another aspect includes searching a plurality of library usage data files to determine a selected library corresponding to the search string. Another aspect includes determining a location of a library call corresponding to the selected library in source code. Another aspect includes determining a computer programmer associated with the determined location based on version control metadata corresponding to the source code. Another aspect includes identifying the determined computer programmer as a subject matter expert for the selected library.

Subject matter expert identification for computer software

Techniques for an expertise score vector for software component management are described herein. An aspect includes determining a size and an amount of time corresponding to committed code contributed by a first developer to a first software component. Another aspect includes determining a time per unit of contribution based on the size and amount of time. Another aspect includes updating component mastery metrics corresponding to the first software component in an expertise score vector corresponding to the first developer based on the time per unit of contribution. Another aspect includes assigning the first developer to a developer tier based on the component mastery metrics. Another aspect includes assigning a work item corresponding to the first software component to the first developer based on the developer tier.

Software developer assignment utilizing contribution based mastery metrics

Aspects of the invention include receiving a notification that a first logical grouping of code segments of a software application is being changed. Comparing the first logical grouping to a second logical grouping of code segments from a change history of the software application, where the first logical grouping and the second logical grouping both relate to resolving a common software application issue, and where the first logical grouping and the second logical grouping include at least one common code segment. Identifying a first code segment of the first logical grouping that is different from a second code segment of the second logical grouping. Determining that given the at least one code segment is changed a probability that the second code segment has been changed is greater than an threshold value. Sending a notification based on the difference in the probabilities being greater than a threshold value.

Using false positives to detect incorrectly updated code segments

A method for detecting and localizing a fault in a system under test (SUT) includes generating an initial set of test vectors that provides complete n-wise coverage of the reduced test space. The method further includes generating and executing an initial set of test cases to obtain a first set of execution results. The method further includes determining, based at least in part on the first set of execution results, that one or more test cases failed execution. The method further includes generating a set of new test cases from a selected failing test case. The method further includes executing the set of new test cases to obtain a second set of execution results. The method further includes detecting and localizing the fault based at least in part on the second set of execution results.

Executing tests in deterministic order

Aspects of the invention include receiving, by a processor, a request to copy a code from a source file and receiving, by the processor, a request to paste the code into a destination file. Aspects also include creating, by the processor based at least in part on the request to paste the code, an entry in a database, the entry having an identification of the source file, an identification of the destination file, a location of the code in the source file, and a location of the code in the destination file.

Linking copied code

A method for detecting and localizing a fault in a system under test (SUT) includes generating an initial set of test vectors that provides complete n-wise coverage of the reduced test space. The method further includes generating an initial set of test cases from the initial set of test vectors and executing the initial set of test cases. The method further includes generating a set of new test cases from a selected failing test case, wherein generating the set of new test cases comprises generating, in relation to each attribute in the selected failing test case, a respective subset of new test cases at least in part by changing a respective attribute value for the attribute in the selected failing test case to each other candidate attribute value for the attribute that is not present in any of the one or more test cases that failed execution.

Reduction of testing space for system testing infrastructure using combinatorics

Embodiments include method, systems and computer program products for performing summarization of a recording based on biometric and business process data. Aspects include receiving a recording of a customer service representative performing a task, receiving a biometric data from a sensor configured to monitor the customer service representative during the performance of the task and obtaining a business process data relating to one or more of the task and the customer service representative. Aspects also include identifying one or more portions of interest from the recording based on one or more of the biometric data and the business process data and compiling the one or more portions of interest into a summarization of the task.

Summarization of a recording for quality control

A disposition manager configured to identify a damaged component of a damaged assembly by determining a measured first property value of a set of measured property values associated with the damaged component and obtained by a sensor is not within a range of acceptable first property values according to a specification of the damaged component. The disposition manager further configured to estimate a performance characteristic of the damaged component and a salvage value of the damaged component. The disposition manager further configured to present, to a user interface, the performance characteristic and the salvage value for the damaged component.

Disposition manager for resource recovery

Techniques for parallel data collection and recovery for a failing virtual processing system are disclosed. According to aspects of the present disclosure, an example method includes: detecting that the virtual processing system experiences an irreparable error; saving, by each of a plurality of processors of the physical processing system, a corresponding context and data stored in an allocated portion of a memory of the physical processing system to a data store; selecting one of the plurality of processors as a recovery processor; initializing, by the recovery processor, a pre-determined reserved portion of the memory; initiating, by the recovery processor, a new instance of the virtual processing system on the reserved portion of the memory while each remaining processor of the plurality of processors continues the saving; and dynamically adding each remaining processor of the plurality of processors to the new instance of the virtual processing system.

Parallel data collection and recovery for failing virtual computer processing system

Embodiments include methods, and computing systems, and computer program products for creating and editing documents. Aspects include creating a new document by a user using an interactive content manager system, and selecting one or more existing documents related to new document from a content database by user using a content source selection interface. Aspects also include performing content unit mining on the existing documents to extract a set of existing content units, and on the new document to extract a set of new content units, respectively, by a content unit mining and extraction module. Aspects further include comparing set of existing content units and the set of new content units to obtain a set of updated content units using a content unit comparison module, and updating the new document using the set of updated content units by the user using an interactive content manager module.

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Wappingers Falls, NY, United States of America

Michael E Gildein