Entit Software LLC

Micro Focus Lcc

Gabi Shalev

Elad Benedict

Raz Regev

Oren Gavriel

Ohad Assulin

Shaul Strachan

Itay Ben-Yehuda

Edo Nir

Albert Ribakovsky

Einat Vaintrob Zilber

In some examples, test relevancy prediction for code changes may include ascertaining files for a commit for a build, and for each test of a plurality of tests, determining a score based on a weight assigned to a file of the ascertained files. Test relevancy prediction for code changes may further include ordering each test of the plurality of tests according to the determined score, and identifying, based on the ordering of each test of the plurality of tests, tests from the plurality of tests for which the score exceeds a specified threshold. The identified tests may represent tests that are to be applied to the build.

Test relevancy prediction for code changes

In one implementation, a system for protocol determination includes a monitor engine to collect data relating to interactions with an application. In addition, the system includes a data engine to store loaded scripts to the application and corresponding interactions relating to the loaded scripts. In addition, the system includes a prediction engine to assign a value to each feature of the stored loaded scripts for the application, wherein the value is based on the usage of the application. In addition, the system includes a protocol engine to select a protocol based on the value.

Protocol determination

Techniques for identifying a build commit that caused a test failure are provided. A build which includes a failed test may be identified. For each commit in the build a weighting factor may be calculated for files that have been previously associated with the failed test. The weighting factor may be based on the number of times the file has been associated with the failed test and the total number of tests. A weighting factor may also be calculated for files that have not been previously associated with the failed test based on the number of times the file appears with other files that are associated with the failed test. The weighting factors may be added to create a score for the commit. The scores for the commits in the build may be ordered. The higher the score, the more likely the commit was the cause of the failed test.

Identifying commits associated with failed tests

An example lexical analysis system may include lexical analyzer instructions and training instructions. The lexical analyzer instructions, when executed, may form a universal lexical analyzer that is to lexically analyze an input program by evaluating a function that maps an input tuple of lexemes to an output tuple of tokens based on input probability parameters. The universal lexical analyzer may be configurable for any programming language by inputting a set of probability parameters for the respective programming language as the input probability parameters of the function. The training instructions, when executed, may generate a set of probability parameters for any programming language by: receiving training programs that are in the respective programming language, each comprising a tuple of lexemes with associated tokens, and determining the set of probability parameters for the respective programming language based on lexemes and tokens in the training programs that are in the respective programming language.

Universal lexical analyzers

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Yehud, Israel

Gabi Shalev