Determining impacts of work items on repositories
AssigneeInternational Business Machines Corporation
InventorsThomas Kenneth Monson, Hitheshwar Peddamekala, Evelio Sosa, Emily Otero, Keith Gregory Frost
Life of this patent
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Abstract
A computer-implemented method, according to one approach, includes: receiving a new work item, and extracting topics from the new work item. A trained machine learning model is used to determine a first set of values representing correlation strengths between the new work item and the topics. Moreover, the first set of values are compared to a second set of values, where the second set of values represents correlation strengths between the topics and multiple files. A third set of values representing correlation strengths between the new work item and the multiple files is also generated, and output.
Claims18
A computer-implemented method, comprising:
- receiving a new work item;
- extracting topics from the new work item;
- using a trained machine learning model to determine a first set of values representing correlation strengths between the new work item and the topics;
- causing a set of pull requests (PRs) which are configured to solve the new work item to be produced by a confidence weighting module;
- comparing the first set of values to a second set of values, the second set of values representing correlation strengths between the topics and multiple files;
- generating a third set of values representing correlation strengths between the new work item and the multiple files;
- using the third set of values to identify one or more PRs in the set of PRs to use to satisfy the new work item, wherein the one or more PRs are identified using the confidence weighting module;
- outputting the third set of values, by:
- converting the third set of values into a heatmap, and
- causing the heatmap to be displayed on a monitor;
- in response to determining a correlation strength between the new work item and a given one of the files is in a predetermined range, using the third set of values to preload the given one of the files into cache;
- and
- causing the new work item to be satisfied using the identified one or more PRs and the preloaded one of the files.
A computer program product, comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable by a processor, executable by the processor, or readable and executable by the processor, to cause the processor to:
- receive a new work item;
- extract topics from the new work item;
- use a trained machine learning model to determine a first set of values representing correlation strengths between the new work item and the topics;
- cause a set of pull requests (PRs) which are configured to solve the new work item to be produced by a confidence weighting module;
- compare the first set of values to a second set of values, the second set of values representing correlation strengths between the topics and multiple files;
- generate a third set of values representing correlation strengths between the new work item and the multiple files;
- use the third set of values to identify one or more PRs in the set of PRs to use to satisfy the new work item, wherein the one or more PRs are identified using the confidence weighting module;
- output the third set of values, by:
- converting the third set of values into a heatmap, and
- causing the heatmap to be displayed on a monitor;
- in response to determining a correlation strength between the new work item and a given one of the files is in a predetermined range, use the third set of values to preload the given one of the files into cache;
- and
- cause the new work item to be satisfied using the identified one or more PRs and the preloaded one of the files.
A system, comprising:
- a processor;
- and
- logic integrated with the processor, executable by the processor, or integrated with and executable by the processor, the logic being configured to:
- receive a new work item;
- extract topics from the new work item;
- use a trained machine learning model to determine a first set of values representing correlation strengths between the new work item and the topics;
- cause a set of pull requests (PRs) which are configured to solve the new work item to be produced by a confidence weighting module;
- compare the first set of values to a second set of values, the second set of values representing correlation strengths between the topics and multiple files;
- generate a third set of values representing correlation strengths between the new work item and the multiple files;
- use the third set of values to identify one or more PRs in the set of PRs to use to satisfy the new work item, wherein the one or more PRs are identified using the confidence weighting module;
- output the third set of values, by:
- converting the third set of values into a heatmap, and
- causing the heatmap to be displayed on a monitor;
- in response to determining a correlation strength between the new work item and a given one of the files is in a predetermined range, use the third set of values to preload the given one of the files into cache;
- and
- cause the new work item to be satisfied using the identified one or more PRs and the preloaded one of the files.
Dependencies are inferred from the claim text.