Method for enhancing classification-based software demand tracking link recovery through knowledge learning and electronic device

Abstract

The invention provides a method for enhancing classification-based software demand tracking link recovery through knowledge learning and an electronic device. The method comprises the steps: after text and structure information contained in a software demand and code file with a tracking relation to be determined is preprocessed, constructing a demand-code knowledge graph and a code dependency graph; modeling the structure and text information of the demand-code knowledge graph, and performing learning to obtain vectors of demand and code entities; modeling the relation of the triple in the demand-code knowledge graph to obtain a relation feature vector; and mining the code dependency graph, extracting inference rules for discovering R2C links between potential demands and codes, and expanding the training data scale. According to the method, an effective prediction model can still be obtained when the training data is less, so that the recovery accuracy of the R2C link is ensured, the recovery cost is reduced, and the workload of manually annotating the data is reduced.

Description

technical field [0001] The invention belongs to the field of computer technology, and in particular relates to a classification-based software requirement tracking link recovery method and an electronic device enhanced through knowledge learning. Background technique [0002] Requirements-to-Code (requirements to code, R2C) is a tracking relationship between software requirements and codes. It builds a logical abstraction bridge between requirements written in natural language description and source code written in programming language. R2C links can help developers better understand the logic and purpose of source code, and help locate codes affected by changes in requirements, thereby greatly reducing the maintenance cost of software projects. R2C links can also be used to automatically select relevant test cases for execution, which makes automated testing projects more efficient. However, R2C links are often lost or wrongly linked in actual work, and most R2C links are ...

AI Technical Summary

Problems solved by technology

[0004] Most of the existing automated requirements tracing recovery methods focus on the extraction of text information and the utilization of explicit direct code dependencies, and these existing automated R2C recovery methods cannot capture the complete semantics between requirements and codes, because they ignore the difficult The captured structural semantics containing contextual information lead to the lack of semantics between requirements and code; in order to obtain better recovery performance, a large amount of labeled data is required to train an effective prediction model, and obtaining labeled data is very time-consuming and laborious things, so this limits their application in practical work

At present, there is no publicly available requirement tracking recovery method that can simultaneously capture the textual and structural semantics between requirements and code, and use a small amount of labeled data to achieve high performance.

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