Code reviewer recommendation method based on portrait and code reviewer portrait system
A recommendation method and profiling technology, applied in the field of information processing, can solve problems such as ignoring reviewers contacting the project team community structure, incomplete reviewer portrait analysis, project progress lag, etc., to avoid long-term unmanned review and promote project progress , reducing the effect of switching
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Embodiment 1
[0043] figure 1 It is a flowchart of a portrait-based code reviewer recommendation method in Embodiment 1, and this scheme specifically includes the following steps:
[0044] Step S1, according to the attribute data of the merge request, the historical review data, the user's attribute data, the warehouse's attribute data and other data, extract entities, attributes and relationships between entities, and construct a review knowledge graph.
[0045] When building a review knowledge graph, first collect the data information of merge requests, users, and warehouses, and then extract the entities, attributes, and relationships, and store and build the review knowledge graph. The specific steps are as follows:
[0046] Step S1-1, collecting valid information of the merge request.
[0047] When collecting merge request information, obtain valid data information from the code repository as a data source. First get all the warehouse information you need, and then get all the merge ...
Embodiment 2
[0110] figure 2 A schematic structural diagram of a reviewer portrait system based on a knowledge graph provided in Embodiment 2 of the present invention. The reviewer portrait system includes: a data processing module 210, a knowledge graph construction module 220, a graph query module 230, and an indicator calculation module 240 And graph visualization module 250.
[0111] Among them, the data processing module 210 is used to obtain the relevant data of the merge request by using the crawler, and process the records with null values to obtain the data for constructing the knowledge graph, which includes:
[0112] The data crawling unit is responsible for using the crawler to obtain the attribute data of the merge request, the historical review data, the attribute data of the user, and the attribute data of the warehouse;
[0113] A data cleaning unit, responsible for processing records with null values, in this embodiment, delete records with null values, and not use the...
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