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 for two stage log normalization is provided. The approach retrieves a message format and a plurality of parameters from one or more log files. The approach determines a classification for one or more first sequence files, wherein the one or more first sequence files includes the message format from the one or more log files. The approach determines a classification of error for the one or more first sequence files. The approach determines whether there is a high confidence in the classification of error for the one or more first sequence files. The approach determines whether there is an improvement in confidence in the classification of error from one or more second sequence files, wherein the one or more second sequence files includes the message format and the plurality of parameters from the one or more log files.

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 for two stage log normalization is provided. The approach retrieves a message format and a plurality of parameters from one or more log files. The approach determines a classification for one or more first sequence files, wherein the one or more first sequence files includes the message format from the one or more log files. The approach determines a classification of error for the one or more first sequence files. The approach determines whether there is a high confidence in the classification of error for the one or more first sequence files. The approach determines whether there is an improvement in confidence in the classification of error from one or more second sequence files, wherein the one or more second sequence files includes the message format and the plurality of parameters from the one or more log files.

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 for two stage log normalization is provided. The approach retrieves a message format and a plurality of parameters from one or more log files. The approach determines a classification for one or more first sequence files, wherein the one or more first sequence files includes the message format from the one or more log files. The approach determines a classification of error for the one or more first sequence files. The approach determines whether there is a high confidence in the classification of error for the one or more first sequence files. The approach determines whether there is an improvement in confidence in the classification of error from one or more second sequence files, wherein the one or more second sequence files includes the message format and the plurality of parameters from the one or more log files.

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: An approach for two stage log normalization is provided. The approach retrieves a message format and a plurality of parameters from one or more log files. The approach determines a classification for one or more first sequence files, wherein the one or more first sequence files includes the message format from the one or more log files. The approach determines a classification of error for the one or more first sequence files. The approach determines whether there is a high confidence in the classification of error for the one or more first sequence files. The approach determines whether there is an improvement in confidence in the classification of error from one or more second sequence files, wherein the one or more second sequence files includes the message format and the plurality of parameters from the one or more log files.

Abstract: An approach for two stage log normalization is provided. The approach retrieves a message format and a plurality of parameters from one or more log files. The approach determines a classification for one or more first sequence files, wherein the one or more first sequence files includes the message format from the one or more log files. The approach determines a classification of error for the one or more first sequence files. The approach determines whether there is a high confidence in the classification of error for the one or more first sequence files. The approach determines whether there is an improvement in confidence in the classification of error from one or more second sequence files, wherein the one or more second sequence files includes the message format and the plurality of parameters from the one or more log files.

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: An approach for two stage log normalization is provided. The approach retrieves a message format and a plurality of parameters from one or more log files. The approach determines a classification for one or more first sequence files, wherein the one or more first sequence files includes the message format from the one or more log files. The approach determines a classification of error for the one or more first sequence files. The approach determines whether there is a high confidence in the classification of error for the one or more first sequence files. The approach determines whether there is an improvement in confidence in the classification of error from one or more second sequence files, wherein the one or more second sequence files includes the message format and the plurality of parameters from the one or more log files.