基于人工智能的软件缺陷预测
摘 要
随着AI技术的不断发展，机器学习算法被广泛应用于软件缺陷预测，以从数据中提取特征和信息，进而预测未来的软件缺陷情况。本论文通过建立一个基于人工智能的软件缺陷预测实验，详细阐述了该实验的设计流程和主要研究结果。介绍了基于机器学习算法的软件缺陷预测实验的设计流程和结果。实验中应用了支持向量机和神经网络等算法进行预测，并通过数据预处理和算法选择提高了模型的准确率和泛化能力。该研究表明了软件缺陷预测技术的应用价值和潜力，可以提高软件质量和节约开发成本，但人工智能技术在该领域应用仍存在挑战和困难，需要进一步解决过度拟合、欠拟合等问题，并选择合适的算法根据实际场景和数据特点进行调整。未来的研究可以在此基础上进行进一步探索和解决，以获得更高效、可靠和精确的预测模型，为软件工程师提供更有力的决策支持。
关键词：人工智能；软件缺陷预测；机器学习算法
Abstract
With the continuous development of AI technology, machine learning algorithms are widely used in software defect prediction, in order to extract features and information from the data, and then predict the future software defects. This paper describes the design process and the main research results. We introduce the design process and results of software defect prediction experiments based on machine learning algorithm. In the experiment, support vector machine and neural network were applied to predict, and the accuracy and generalization ability of the model were improved by data preprocessing and algorithm selection. The study shows the application value of software defect prediction technology and potential, can improve the quality of software and save the development cost, but the artificial intelligence technology applied in this field still has challenges and difficulties, need to further solve the problems such as excessive fitting, under fitting, and choose the appropriate algorithm according to the actual scenario and data characteristics. Future research can be further explored and solved on this basis to obtain more efficient, reliable and accurate prediction models and provide more powerful decision support for software engineers.
Key words: Artificial intelligence; software defects prediction; machine learning algorithm
目 录
中文摘要 I
英文摘要 II
目 录 III
引 言 1
第1章、相关技术介绍 2
1.1、软件缺陷预测技术简介 2
1.2、人工智能技术 2
1.3、机器学习算法 2
第2章、相关工作综述 4
2.1、软件缺陷的定义和类型 4
2.2、软件缺陷预测的方法和技术 4
2.3、人工智能算法在软件缺陷预测中的发展及应用 4
第3章、缺陷预测模型设计 6
3.1、数据集处理 6
3.2、特征工程 6
3.3、模型设计 6
3.4、模型评估与比较 7
第3章、缺陷预测模型设计的实验结果与分析 8
3.1、实验环境及数据集描述 8
3.2、实验结果与分析 8
3.3、结果比较与验证 8
结 论 10
参考文献 11
摘 要
随着AI技术的不断发展，机器学习算法被广泛应用于软件缺陷预测，以从数据中提取特征和信息，进而预测未来的软件缺陷情况。本论文通过建立一个基于人工智能的软件缺陷预测实验，详细阐述了该实验的设计流程和主要研究结果。介绍了基于机器学习算法的软件缺陷预测实验的设计流程和结果。实验中应用了支持向量机和神经网络等算法进行预测，并通过数据预处理和算法选择提高了模型的准确率和泛化能力。该研究表明了软件缺陷预测技术的应用价值和潜力，可以提高软件质量和节约开发成本，但人工智能技术在该领域应用仍存在挑战和困难，需要进一步解决过度拟合、欠拟合等问题，并选择合适的算法根据实际场景和数据特点进行调整。未来的研究可以在此基础上进行进一步探索和解决，以获得更高效、可靠和精确的预测模型，为软件工程师提供更有力的决策支持。
关键词：人工智能；软件缺陷预测；机器学习算法
Abstract
With the continuous development of AI technology, machine learning algorithms are widely used in software defect prediction, in order to extract features and information from the data, and then predict the future software defects. This paper describes the design process and the main research results. We introduce the design process and results of software defect prediction experiments based on machine learning algorithm. In the experiment, support vector machine and neural network were applied to predict, and the accuracy and generalization ability of the model were improved by data preprocessing and algorithm selection. The study shows the application value of software defect prediction technology and potential, can improve the quality of software and save the development cost, but the artificial intelligence technology applied in this field still has challenges and difficulties, need to further solve the problems such as excessive fitting, under fitting, and choose the appropriate algorithm according to the actual scenario and data characteristics. Future research can be further explored and solved on this basis to obtain more efficient, reliable and accurate prediction models and provide more powerful decision support for software engineers.
Key words: Artificial intelligence; software defects prediction; machine learning algorithm
目 录
中文摘要 I
英文摘要 II
目 录 III
引 言 1
第1章、相关技术介绍 2
1.1、软件缺陷预测技术简介 2
1.2、人工智能技术 2
1.3、机器学习算法 2
第2章、相关工作综述 4
2.1、软件缺陷的定义和类型 4
2.2、软件缺陷预测的方法和技术 4
2.3、人工智能算法在软件缺陷预测中的发展及应用 4
第3章、缺陷预测模型设计 6
3.1、数据集处理 6
3.2、特征工程 6
3.3、模型设计 6
3.4、模型评估与比较 7
第3章、缺陷预测模型设计的实验结果与分析 8
3.1、实验环境及数据集描述 8
3.2、实验结果与分析 8
3.3、结果比较与验证 8
结 论 10
参考文献 11
