Code reviewer recommendation method based on collaborative filtering

A technology of collaborative filtering and recommendation methods, applied in the field of recommendation systems, can solve problems such as delaying the development process of software projects, affecting the efficiency of software project development, and failure to process review requests, so as to reduce review time, speed up development efficiency, and save The effect of manpower and time

Active Publication Date: 2018-03-16
BEIHANG UNIV
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Problems solved by technology

However, the existing MCR tools are all review requests that are manually assigned or not assigned; in addition, some existing recommendation methods are limited to mining the explicit relationship between developers and resources while ignoring the implicit relationship between them.
This situation ha

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  • Code reviewer recommendation method based on collaborative filtering
  • Code reviewer recommendation method based on collaborative filtering
  • Code reviewer recommendation method based on collaborative filtering

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[0013] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

[0014] The present invention adopts a collaborative filtering method to establish a latent factor model (LFM). First construct the scoring matrix between users and items, and then get the similarity between items according to the scoring matrix. The similarity between items can serve for the subsequent recommendation method. When estimating the rating of a certain item for a user, first obtain...

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Abstract

The invention provides a code reviewer recommendation method based on collaborative filtering. The method comprises the three parts of data processing, implicit relation mining and logic recommendation, wherein in the data processing part, historical review data is displayed in the form of a matrix; the implicit relation mining part is used for mining an implicit relation implicit in the data according to information of the matrix; and in the logic recommendation part, a general recommendation process is described, and ordering rules are given. Based on realization of the framework, the implicit relation in the data can be effectively utilized to recommend a code reviewer. Through the method, an implicit relation mining mechanism is proposed according to a recommendation scene of the codereviewer. According to the mechanism, a neighborhood model and an implicit factor model in a recommendation system are utilized to serve recommendation work of the code reviewer, and the defects of existing methods in reviewer recommendation are overcome.

Description

technical field [0001] The invention relates to a recommendation system method, in particular to a code reviewer recommendation method based on collaborative filtering. Background technique [0002] Code review is an important software development activity, which can effectively reduce code defects and ensure code quality. In 1976, Fagan proposed a formalized process for code review. Code review activities at the time were done through group meetings. After a long period of practice, researchers have found that this form of code review can indeed bring benefits and reduce the number of software defects, but this form of review activities is too cumbersome and time-consuming. Therefore, many organizations now adopt a lighter code review mechanism. Many companies and open source organizations utilize tools to support code reviews. This new form of code review is called Modern Code Review (MCR). MCR is tool-based, can happen frequently, and is lighter than what Fagan propo...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/334
Inventor 孙海龙刘旭东夏正林丁锦符阳
Owner BEIHANG UNIV
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