Deep defect and vulnerability prediction

拟议项目的目标是基于申请者团队的专利技术制造一种产品，用于自动检测软件缺陷和安全漏洞。该产品将帮助开发人员快速、灵活地交付高质量的软件。这项技术被称为深度缺陷和漏洞预测，它预测包含缺陷和安全漏洞的源代码区域，即错误代码区域。一项核心技术是利用深度学习来自动学习语义特征，以构建更准确的预测模型来识别缺陷和安全漏洞。类似于利用统计模型从丰富的天气历史中预测雨天的天气预报系统，这项技术利用深度学习来自动从软件开发历史中学习新的缺陷和漏洞模式。除了研究贡献之外，通过(1)更早地发现更多的缺陷和漏洞(越早被发现，它们的成本越低)，以及(2)提高软件开发生产率，所提出的项目为软件行业带来的潜在成本节约将是巨大的，因为它将显著降低软件开发和维护的成本

The objective of the proposed project is to build a product based on the patented technology from theapplicant's team on detecting software defects and security vulnerabilities automatically. The proposed productwill help developers deliver high-quality software with speed and agility.The technology, referred to as deep defect and vulnerability prediction, predicts source code regions thatcontain defects and security vulnerabilities, i.e., buggy code regions. A core technique is to leverage deeplearning to automatically learn semantic features for building more accurate prediction models to identifydefects and security vulnerabilities. Similar to a weather forecast system that leverages statistical models topredict a rainy day from abundant weather histories, this technology leverages deep learning to automaticallylearn new defect and vulnerability patterns from software development histories. The learned patterns enablethis technology to detect new classes of defects and vulnerabilities.In addition to research contributions, the potential cost savings for the software industry from the proposedproject will be immense because it will significantly reduce the cost of software development and maintenanceby (1) detecting more defects and vulnerabilities earlier (the earlier they are detected, the less they cost), and (2)improving software development productivity