Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for test cycle optimization using contextual association mapping. In one aspect, a method includes obtaining an artifact that includes a collection of reference items, where each reference item includes a sequence of words, generating candidate tags from each of the reference items based on the sequences of words in the reference items, selecting a subset of the candidate tags as context tags based on an amount that the candidate tags appear in the reference items, obtaining a sample item that includes a sequence of words, identifying a subset of the context tags in the sequence of words in the sample item, and classifying a subset of the reference items as contextually similar to the sample item based the context tags that were identified.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for test cycle optimization using contextual association mapping. In one aspect, a method includes obtaining an artifact that includes a collection of reference items, where each reference item includes a sequence of words, generating candidate tags from each of the reference items based on the sequences of words in the reference items, selecting a subset of the candidate tags as context tags based on an amount that the candidate tags appear in the reference items, obtaining a sample item that includes a sequence of words, identifying a subset of the context tags in the sequence of words in the sample item, and classifying a subset of the reference items as contextually similar to the sample item based the context tags that were identified.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for test cycle optimization using contextual association mapping. In one aspect, a method includes obtaining an artefact that includes a collection of reference items, where each reference item includes a sequence of words, generating candidate tags from each of the reference items based on the sequences of words in the reference items, selecting a subset of the candidate tags as context tags based on an amount that the candidate tags appear in the reference items, obtaining a sample item that includes a sequence of words, identifying a subset of the context tags in the sequence of words in the sample item, and classifying a subset of the reference items as contextually similar to the sample item based the context tags that were identified.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions. In one aspect, a method includes the actions of receiving a log file that includes log records generated from a code base; processing the log file through a pattern mining algorithm to determine a usage pattern; generating a graphical representation based on an analysis of the usage pattern; processing the graphical representation through a machine learning algorithm to select a set of test cases from a plurality of test cases for the code base and to assign a priority value to each of the selected test cases; sequencing the set of test cases based on the priority values; and transmitting the sequenced set of test cases to a test execution engine.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for test cycle optimization using contextual association mapping. In one aspect, a method includes obtaining an artefact that includes a collection of reference items, where each reference item includes a sequence of words, generating candidate tags from each of the reference items based on the sequences of words in the reference items, selecting a subset of the candidate tags as context tags based on an amount that the candidate tags appear in the reference items, obtaining a sample item that includes a sequence of words, identifying a subset of the context tags in the sequence of words in the sample item, and classifying a subset of the reference items as contextually similar to the sample item based the context tags that were identified.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions. In one aspect, a method includes the actions of receiving a log file that includes log records generated from a code base; processing the log file through a pattern mining algorithm to determine a usage pattern; generating a graphical representation based on an analysis of the usage pattern; processing the graphical representation through a machine learning algorithm to select a set of test cases from a plurality of test cases for the code base and to assign a priority value to each of the selected test cases; sequencing the set of test cases based on the priority values; and transmitting the sequenced set of test cases to a test execution engine.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions. In one aspect, a method includes the actions of receiving a log file that includes log records generated from a code base; processing the log file through a pattern mining algorithm to determine a usage pattern; generating a graphical representation based on an analysis of the usage pattern; processing the graphical representation through a machine learning algorithm to select a set of test cases from a plurality of test cases for the code base and to assign a priority value to each of the selected test cases; sequencing the set of test cases based on the priority values; and transmitting the sequenced set of test cases to a test execution engine.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions. In one aspect, a method includes the actions of receiving a log file that includes log records generated from a code base; processing the log file through a pattern mining algorithm to determine a usage pattern; generating a graphical representation based on an analysis of the usage pattern; processing the graphical representation through a machine learning algorithm to select a set of test cases from a plurality of test cases for the code base and to assign a priority value to each of the selected test cases; sequencing the set of test cases based on the priority values; and transmitting the sequenced set of test cases to a test execution engine.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions. In one aspect, a method includes the actions of receiving a log file that includes log records generated from a code base; processing the log file through a pattern mining algorithm to determine a usage pattern; generating a graphical representation based on an analysis of the usage pattern; processing the graphical representation through a machine learning algorithm to select a set of test cases from a plurality of test cases for the code base and to assign a priority value to each of the selected test cases; sequencing the set of test cases based on the priority values; and transmitting the sequenced set of test cases to a test execution engine.