SBIR Phase I: The Development of an Artificial Analysis (AI) Static Code Analysis Platform to Increase Software Developer Productivity

SBIR 第一阶段：开发人工分析 (AI) 静态代码分析平台以提高软件开发人员的工作效率

• 批准号: 2318738
• 负责人: Massimiliano Genta
• 金额: $ 24.67万
• 依托单位: METABOB INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318738&HistoricalAwards=false
• 关键词: SBIR; Phase; Development; Artificial; Analysis

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to increase the speed and accuracy of software development in a wide range of industries and to make software developers more productive. The technology will decrease the time spent reviewing code by shipping higher quality and defect free code and will further ensure more secure software that is less prone to outside attacks. This SBIR Phase I project develops a cloud-based artificial intelligence (AI)-based static code analysis tool which can find complex and severe problems early in the process of software development. Unlike existing static analysis tools, the tool developed in this project will learn automatically from bug fixes, explain the errors found, and make recommendations on how to fix them. Results will help organizations and developers in the finance, healthcare, and defense industries where code reuse is important for security and compliance reasons. Overall, this project fits well with an increasing trend of organizations integrating more AI into their operations and a growing market for software development tools.This SBIR Phase I project combines the latest advancements in machine learning and natural language processing to develop a new, intelligent way to find and explain software errors. The project focuses on developing a software architecture that enables the analysis of a complete model hierarchy, establishing a technique to effectively and quantitatively evaluate the validity of explanations generated for flagged bugs, and integrating the disparate components into a single analysis framework. The project will consist of three models which will be developed and integrated as part of the overarching system architecture: (1) a code fault detection model utilizing a graph attention network, (2) a generative transformer to build explanations and suggestions, and (3) a graph-to-graph transformer to generate mutations to the code architecture to resolve the flagged bugs. The project will leverage recent advancements in transformer-based and graph-based neural networks and therefore propel the current state of research for efficient code review processes forward.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该小型企业创新研究 (SBIR) 第一阶段项目的更广泛/商业影响是提高各行业软件开发的速度和准确性，并提高软件开发人员的工作效率。该技术将通过交付更高质量和无缺陷的代码来减少审查代码所花费的时间，并将进一步确保更安全的软件，不易受到外部攻击。该SBIR第一阶段项目开发了一种基于云的人工智能（AI）静态代码分析工具，可以在软件开发过程的早期发现复杂而严重的问题。与现有的静态分析工具不同，该项目中开发的工具将自动从错误修复中学习，解释发现的错误，并就如何修复这些错误提出建议。 结果将帮助金融、医疗保健和国防行业的组织和开发人员，在这些行业中，出于安全和合规性原因，代码重用非常重要。总体而言，该项目非常适合组织将更多人工智能集成到其运营中的日益增长的趋势以及不断增长的软件开发工具市场。该 SBIR 第一阶段项目结合了机器学习和自然语言处理的最新进展，以开发一种新的智能方式查找并解释软件错误。该项目的重点是开发一种能够分析完整模型层次结构的软件架构，建立一种有效、定量地评估为标记的错误生成的解释的有效性的技术，并将不同的组件集成到单个分析框架中。该项目将由三个模型组成，这些模型将作为总体系统架构的一部分进行开发和集成：（1）利用图注意力网络的代码错误检测模型，（2）用于构建解释和建议的生成变压器，以及（3） ）一个图到图的转换器，用于生成代码架构的突变，以解决标记的错误。该项目将利用基于变压器和基于图形的神经网络的最新进展，从而推动高效代码审查流程的研究现状向前发展。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，被认为值得支持。优点和更广泛的影响审查标准。