Abstract: Automatically establishing and/or modifying a trust relationship between devices, including mobile devices, in communication, and customizing a user interface workflow based on the trust relationship. Trust relationships are based on numerous proximity-related factors including automatically gathered proximity data, length of time in proximity, and signals detected from a target communication device as well as other nearby communication devices.

Abstract: An approach to generating a context for translating strings for a graphical user interface. The approach includes a computer receiving a string to be translated and associated source code, the string identified by a unique key within the associated source code and the associated source code is markup language. The computer identifies a first logical section of the associated source code corresponding to a unique key of the string and one or more graphical user interface components within the identified logical section of the associated source code. The computer then creates a mockup image presenting the one or more graphical user interface components and the string. The computer then labels, in the created mockup image, each of the one or more graphical user interface components and the first string of the plurality of strings, and identifies one or more additional graphical user interface components.

Abstract: Automatically establishing and/or modifying a trust relationship between devices, including mobile devices, in communication, and customizing a user interface workflow based on the trust relationship. Trust relationships are based on numerous proximity-related factors including automatically gathered proximity data, length of time in proximity, and signals detected from a target communication device as well as other nearby communication devices.

Abstract: An approach to generating a context for translating strings for a graphical user interface. The approach includes a computer receiving a string to be translated and associated source code, the string identified by a unique key within the associated source code and the associated source code is markup language. The computer identifies a first logical section of the associated source code corresponding to a unique key of the string and one or more graphical user interface components within the identified logical section of the associated source code. The computer then creates a mockup image presenting the one or more graphical user interface components and the string. The computer then labels, in the created mockup image, each of the one or more graphical user interface components and the first string of the plurality of strings, and identifies one or more additional graphical user interface components.

Abstract: According to embodiments of the present invention, a first metadata defining how a user interface associated with an application under test should appear is generated using one or more computer processors. An image of the user interface associated with the application under test that is invoked during a test run is captured using one or more one or more computer processors. The captured image is converted, using one or more computer processors, into one or more of a second metadata and text. The first metadata is compared, using one or more computer processors, to the second metadata and the text. In response to the comparison resulting in a difference between the first metadata and the second metadata, the image is stored, using one or more computer processors, and an assessment request is generated using one or more computer processors.

Abstract: According to embodiments of the present invention, a first metadata defining how a user interface associated with an application under test should appear is generated using one or more computer processors. An image of the user interface associated with the application under test that is invoked during a test run is captured using one or more one or more computer processors. The captured image is converted, using one or more computer processors, into one or more of a second metadata and text. The first metadata is compared, using one or more computer processors, to the second metadata and the text. In response to the comparison resulting in a difference between the first metadata and the second metadata, the image is stored, using one or more computer processors, and an assessment request is generated using one or more computer processors.

Abstract: A method for visualization of big data using three dimensional pie charts. The method includes receiving at least a first set of data, comprising information detailing one or more subsets of values, each value having an associated time element. The method includes determining a three dimensional pie chart based on the at least first set of data, which includes at least as first layer comprising a first set of wedges and a second layer comprising a second set of wedges. The method includes displaying the three dimensional pie chart comprising at least the first layer and the second layer.

Abstract: An approach to generating a context for translating strings for a graphical user interface. The approach includes a computer receiving a string to be translated and associated source code, the string identified by a unique key within the associated source code and the associated source code is markup language. The computer identifies a first logical section of the associated source code corresponding to a unique key of the string and one or more graphical user interface components within the identified logical section of the associated source code. The computer then creates a mockup image presenting the one or more graphical user interface components and the string. The computer then labels, in the created mockup image, each of the one or more graphical user interface components and the first string of the plurality of strings, and identifies one or more additional graphical user interface components.

Abstract: In a method for translating text into another language, receiving, by one or more processors, text in a first language. Comparing, by one or more processors, the text to a plurality of resource bundle strings in the first language. Determining, by one or more processors, that the text at least partially matches one or more resource bundle strings of the plurality of resource bundle strings. Determining, by one or more processors, a resource bundle key corresponding to each resource bundle string of the one or more resource bundle strings. Translating, by one or more processors, the text from the first language to a second language using one determined resource bundle key.

Abstract: A method for generating a context for translating strings for a graphical user interface. The method includes receiving a string to be translated and associated source code, the string identified by a unique key within the associated source code. The method includes identifying a first logical section of the associated source code corresponding to a unique key of the string. The method includes identifying one or more graphical user interface components within the identified logical section of the associated source code. The method then includes creating a mockup image presenting the one or more graphical user interface components and the string.

Abstract: Automatically establishing and/or modifying a trust relationship between devices, including mobile devices, in communication, and customizing a user interface workflow based on the trust relationship. Trust relationships are based on numerous proximity-related factors including automatically gathered proximity data, length of time in proximity, and signals detected from a target communication device as well as other nearby communication devices.

Abstract: Automatically establishing and/or modifying a trust relationship between devices, including mobile devices, in communication, and customizing a user interface workflow based on the trust relationship. Trust relationships are based on numerous proximity-related factors including automatically gathered proximity data, length of time in proximity, and signals detected from a target communication device as well as other nearby communication devices.

Abstract: An approach to generating a context for translating strings for a graphical user interface. The approach includes a computer receiving a string to be translated and associated source code, the string identified by a unique key within the associated source code and the associated source code is markup language. The computer identifies a first logical section of the associated source code corresponding to a unique key of the string and one or more graphical user interface components within the identified logical section of the associated source code. The computer then creates a mockup image presenting the one or more graphical user interface components and the string. The computer then labels, in the created mockup image, each of the one or more graphical user interface components and the first string of the plurality of strings, and identifies one or more additional graphical user interface components.

Abstract: An approach to generating a context for translating strings for a graphical user interface. The approach includes a computer receiving a string to be translated and associated source code, the string identified by a unique key within the associated source code and the associated source code is markup language. The computer identifies a first logical section of the associated source code corresponding to a unique key of the string and one or more graphical user interface components within the identified logical section of the associated source code. The computer then creates a mockup image presenting the one or more graphical user interface components and the string. The computer then labels, in the created mockup image, each of the one or more graphical user interface components and the first string of the plurality of strings, and identifies one or more additional graphical user interface components.

Abstract: A method for dynamically adjusting an actual wait period associated with an operating system call, wherein the operating system call suspends execution of at least one thread in a plurality of threads associated with an operating environment is provided. The method may include determining a utilization factor function associated with the operating environment. The method may also include selecting at least one performance counter within a plurality of performance counters associated with the operating environment. The method may further include computing a utilization factor based on the determined utilization factor function and the selected at least one performance counter. Additionally, the method may include intercepting an operating system call, wherein the operating system call includes a requested wait period parameter.

Abstract: According to embodiments of the present invention, a first metadata defining how a user interface associated with an application under test should appear is generated using one or more computer processors. An image of the user interface associated with the application under test that is invoked during a test run is captured using one or more one or more computer processors. The captured image is converted, using one or more computer processors, into one or more of a second metadata and text. The first metadata is compared, using one or more computer processors, to the second metadata and the text. In response to the comparison resulting in a difference between the first metadata and the second metadata, the image is stored, using one or more computer processors, and an assessment request is generated using one or more computer processors.

Abstract: A computer identifies lines of code of a product program that have been modified after an initial test of the product program. The computer determines the overlap between lines of code that have been modified and a mapped test case. The computer determines a weighted value for the mapped test case based on two or more of, an environment of the test case, the degree of the overlap, a time the test case was last executed, a time the test case takes to execute, and a priority of a defect. The environment of the test case is configured to replicate a working environment where the product program is to be deployed and includes an operating system, a hardware configuration, and the configuration of the operating system.

Abstract: A computer identifies lines of code of a product program that have been modified after an initial test of the product program. The computer determines the overlap between lines of code that have been modified and a mapped test case. The computer determines a weighted value for the mapped test case based on two or more of, an environment of the test case, the degree of the overlap, a time the test case was last executed, a time the test case takes to execute, and a priority of a defect. The environment of the test case is configured to replicate a working environment where the product program is to be deployed and includes an operating system, a hardware configuration, and the configuration of the operating system.

Abstract: An approach to generating a context for translating strings for a graphical user interface. The approach includes a computer receiving a string to be translated and associated source code, the string identified by a unique key within the associated source code and the associated source code is markup language. The computer identifies a first logical section of the associated source code corresponding to a unique key of the string and one or more graphical user interface components within the identified logical section of the associated source code. The computer then creates a mockup image presenting the one or more graphical user interface components and the string. The computer then labels, in the created mockup image, each of the one or more graphical user interface components and the first string of the plurality of strings, and identifies one or more additional graphical user interface components.

Abstract: In an approach to determining a classification of an error in a computing system, a computer receives a notification of an error during a test within a computing system. The computer then retrieves a plurality of log files created during the test from within the computing system and determines data containing one or more error categorizations. The computer determines a classification of the error, based, at least in part, on the plurality of log files and the data containing one or more error categorizations.