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

这个小型企业创新研究(SBIR)第一阶段项目的更广泛的商业影响是提高广泛行业中软件开发的速度和准确性，并使软件开发人员更有效率。这项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太容易受到外部攻击。SBIR一期项目开发了一个基于云的人工智能(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和严重的问题。与现有的静态分析工具不同，本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，并就如何修复它们提出建议。结果将帮助金融、医疗保健和国防行业的组织和开发人员，这些行业的代码重用出于安全和合规性原因非常重要。总体而言，这个项目很好地适应了组织将更多的人工智能集成到他们的运营中的日益增长的趋势和软件开发工具日益增长的市场。这个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，开发了一种新的智能方法来发现和解释软件错误。该项目专注于开发能够分析完整模型层次结构的软件体系结构，建立一种技术来有效和定量地评估为标记的错误生成的解释的有效性，并将不同的组件集成到单个分析框架中。该项目将包括三个模型，它们将作为总体系统体系结构的一部分进行开发和集成：(1)利用图形注意网络的代码故障检测模型，(2)用于构建解释和建议的生成性转换器，以及(3)用于对代码体系结构产生突变以解决标记的错误的图形到图形转换器。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过程的研究现状。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.