Abstract: A method of identifying substantially similar data fields may include accessing a first/second text strings, performing an approximate string-matching algorithm that generates a first score, and comparing the first score to a first threshold. The method also includes performing an exact string-matching algorithm that generates a second score and comparing the second score to a second threshold. The method further includes generating a third score by combining a weighted first score with a weighted second score and comparing the third score to a third threshold. The method also includes determining whether the first text string is substantially similar to the second text string based on the three score comparisons.

Abstract: The present disclosure generally relates to predicting automated software tests for testing units of work delivery in a continuous integration development environment. More particularly, the present disclosure relates to systems and methods for improving the efficiency of code integration by predicting a subset of automated software tests from amongst a set of all available automated software tests, thereby improving testing time and reducing processing loads.

Abstract: The present disclosure generally relates to predicting automated software tests for testing units of work delivery in a continuous integration development environment. More particularly, the present disclosure relates to systems and methods for improving the efficiency of code integration by predicting a subset of automated software tests from amongst a set of all available automated software tests, thereby improving testing time and reducing processing loads.

Abstract: A method of identifying substantially similar data fields may include accessing a first/second text strings, performing an approximate string-matching algorithm that generates a first score, and comparing the first score to a first threshold. The method also includes performing an exact string-matching algorithm that generates a second score and comparing the second score to a second threshold. The method further includes generating a third score by combining a weighted first score with a weighted second score and comparing the third score to a third threshold. The method also includes determining whether the first text string is substantially similar to the second text string based on the three score comparisons.

Abstract: A method of identifying duplicate crash dumps in a computer system may include receiving a first crash dump caused by an application crash, extracting a first function signature of a function that caused the first crash dump, and searching a datastore of crash dumps for function signatures that substantially match the first function signature. The searching may include performing an approximate string-match between each of the function signatures the first function signature and performing an exact string match between each of the function signatures and the first function signature. The searching may also include combining weighted results of the approximate string-match with weighted results of the exact string match to generate match scores for each of the function signatures, and identifying the function signatures that substantially match the first function signature based on the match scores.

Abstract: A method of identifying duplicate crash dumps in a computer system may include receiving a first crash dump caused by an application crash, extracting a first function signature of a function that caused the first crash dump, and searching a datastore of crash dumps for function signatures that substantially match the first function signature. The searching may include performing an approximate string-match between each of the function signatures the first function signature and performing an exact string match between each of the function signatures and the first function signature. The searching may also include combining weighted results of the approximate string-match with weighted results of the exact string match to generate match scores for each of the function signatures, and identifying the function signatures that substantially match the first function signature based on the match scores.

Abstract: Described systems and mechanisms relate to techniques for improving usability, speed, and accuracy of text input to touch-typable devices. A touch-typable device can include a movable keymask comprising of a plurality of cells mapped with one or more keys of text input keypad of the touch-typable device, wherein each of the plurality of cells of the movable keymask superimposes over textual input choices enabling the user to select the desired textual input from the plurality of cells of the movable keymask using the text input keypad of the touch-typable device. The textual input choices can be chosen after every user input from a set of likely input choices, wherein the likely input choices can be selected based on previous textual input sequence, phoneme and syllabary patterns, statistical correlation between the previous textual input sequence and set of textual input choices, dictionary lookups, and usage patterns of said user.