基于深度学习的软件缺陷检测
摘 要
随着软件系统规模和复杂度的不断增长，软件缺陷检测成为确保软件质量的关键环节。传统基于规则和统计的方法在面对复杂多变的代码结构时存在局限性，难以满足现代软件开发的需求。为此，本研究提出一种基于深度学习的软件缺陷检测方法，旨在通过神经网络模型自动学习代码特征，提高缺陷检测的准确性和效率。该方法首先对源代码进行预处理，将其转换为适合深度学习模型输入的形式，包括词法分析、语法解析等步骤；然后构建卷积神经网络与循环神经网络相结合的混合模型，利用卷积层提取局部特征，循环层捕捉长距离依赖关系，从而实现对代码片段的有效表征；最后通过大规模开源项目数据集进行训练与验证。实验结果表明，所提方法在多个评估指标上均优于传统方法，F1值达到0.85以上，能够有效识别不同类型缺陷。
关键词：软件缺陷检测 深度学习 卷积神经网络
Abstract
With the increasing scale and complexity of software systems, software defect detection becomes a key link in ensuring software quality. Traditional rules-based methods have limitations in the face of complex and changeable code structure, which is difficult to meet the needs of modern software development. To this end, this study proposes a deep learning-based software defect detection method, aiming to improve the accuracy and efficiency of defect detection by automatically learning code features through the neural network model. This method first preprocesses the source code and transforms it into a form suitable for deep learning model input, including lexical analysis and syntax resolution, then builds a hybrid model combining convolutional neural network and recurrent neural network to extract local features using convolution layer to capture long distance dependence, and verifies large-scale open source project data set. The experimental results show that the proposed method is better than the traditional method in multiple evaluation indexes, with the F1 value above 0.85, which can effectively identify different types of defects.
Keyword:Software Defect Detection Deep Learning Convolutional Neural Network
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2深度学习基础理论 2
2.1深度学习基本概念 2
2.2常用深度学习模型 3
2.3模型在缺陷检测中的适用性 3
3软件缺陷检测技术 4
3.1传统缺陷检测方法 4
3.2深度学习检测流程 4
3.3检测算法性能评估 5
4实验设计与结果分析 6
4.1实验数据集构建 6
4.2实验方案设计 6
4.3结果分析与讨论 7
结论 7
参考文献 9
致谢 10
摘 要
随着软件系统规模和复杂度的不断增长，软件缺陷检测成为确保软件质量的关键环节。传统基于规则和统计的方法在面对复杂多变的代码结构时存在局限性，难以满足现代软件开发的需求。为此，本研究提出一种基于深度学习的软件缺陷检测方法，旨在通过神经网络模型自动学习代码特征，提高缺陷检测的准确性和效率。该方法首先对源代码进行预处理，将其转换为适合深度学习模型输入的形式，包括词法分析、语法解析等步骤；然后构建卷积神经网络与循环神经网络相结合的混合模型，利用卷积层提取局部特征，循环层捕捉长距离依赖关系，从而实现对代码片段的有效表征；最后通过大规模开源项目数据集进行训练与验证。实验结果表明，所提方法在多个评估指标上均优于传统方法，F1值达到0.85以上，能够有效识别不同类型缺陷。
关键词：软件缺陷检测 深度学习 卷积神经网络
Abstract
With the increasing scale and complexity of software systems, software defect detection becomes a key link in ensuring software quality. Traditional rules-based methods have limitations in the face of complex and changeable code structure, which is difficult to meet the needs of modern software development. To this end, this study proposes a deep learning-based software defect detection method, aiming to improve the accuracy and efficiency of defect detection by automatically learning code features through the neural network model. This method first preprocesses the source code and transforms it into a form suitable for deep learning model input, including lexical analysis and syntax resolution, then builds a hybrid model combining convolutional neural network and recurrent neural network to extract local features using convolution layer to capture long distance dependence, and verifies large-scale open source project data set. The experimental results show that the proposed method is better than the traditional method in multiple evaluation indexes, with the F1 value above 0.85, which can effectively identify different types of defects.
Keyword:Software Defect Detection Deep Learning Convolutional Neural Network
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2深度学习基础理论 2
2.1深度学习基本概念 2
2.2常用深度学习模型 3
2.3模型在缺陷检测中的适用性 3
3软件缺陷检测技术 4
3.1传统缺陷检测方法 4
3.2深度学习检测流程 4
3.3检测算法性能评估 5
4实验设计与结果分析 6
4.1实验数据集构建 6
4.2实验方案设计 6
4.3结果分析与讨论 7
结论 7
参考文献 9
致谢 10
