Software defect prediction method and terminal based on bidirectional long short-term memory neural network

Abstract

The invention discloses a software defect prediction method and terminal based on a bidirectional long short-term memory neural network, and the method comprises the steps: enabling an abstract syntax tree of a source code file and a source code to correspond to code change information between different versions through the bidirectional long short-term memory neural network; screening and extracting an abstract syntax tree point node sequence and a code change node sequence, connecting and constructing a combined sequence, inputting the combined sequence into a Word2Vec word embedding model, encoding the combined sequence into a word vector, fusing semantic features and traditional features by utilizing traditional measurement features provided by a PROMISE library and combining a gating fusion strategy to form combined features, and constructing a word vector; and inputting the combined features and the corresponding labels into a classifier to train a defect prediction model. According to the method, richer code semantic features are extracted from a source code abstract syntax tree and code change data, traditional features provided by a PROMISE storage library are combined, a classifier model is better helped to learn the semantic features, and a more accurate defect prediction result is obtained.

Description

technical field [0001] The invention belongs to the technical field of file-level software defect prediction, and in particular relates to a software defect prediction method and terminal based on a bidirectional long-short-term memory neural network. Background technique [0002] With the continuous increase of software size and complexity, software defect prediction technology has received extensive attention in recent years. The results given by the defect prediction model can help developers or testers determine whether a software module is defective in the early stages of the software development life cycle, so that they can better allocate test resources and arrange the test process more efficiently, thereby improving software products. the quality of. It is generally believed that fixing defects after deployment is more expensive than dealing with them during development. Therefore, it is very important to improve the defect prediction ability. The important compon...

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Problems solved by technology

[0005] Aiming at the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide a software defect prediction method and terminal based on a bidirectional long-short-term memory neural network, so as to solve the single and insufficient representation of defect code information in the prior art. Problem: The present invention enriches code semantic features by combining code change information and abstract syntax tree information, and at the same time combines traditional measurement features to supplement information that cannot be represented by semantic features, so as to achieve more accurate defect prediction results

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