Toward Robust and Adaptable Deep Learning Models of Code

迈向稳健且适应性强的深度学习代码模型

• 批准号: 576218-2022
• 负责人: Sahraoui, HouariHA
• 金额: $ 1.82万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=757406
• 关键词: Toward; Robust; Adaptable; Deep; Learning

The demand for software has never been so high and is expected to further increase in the years to come. Software systems have become a founding pillar of our heavily digitalized society, and have had a significant impact in numerous domains, including health care, banks, and autonomous vehicles. At the same time, the complexity of software systems tends to increase with time, especially as they must be prone to dealing with intense quantities of data as well as with other software and hardware components. This can have a massive impact on the software developers' efficiency. One solution to cope with these increasing demand and complexity is to use artificial intelligence, specifically deep learning, to automate some complex software engineering tasks. In the past years, deep-learning-based approaches have enabled us to learn potent models of the software world, and successfully use them, for example, to complete the code of a project under development or to search for pieces of code using a natural language query. Nonetheless, these models have limited applications outside the context in which they were trained. The main objective of this project is to make deep learning models more adaptive and robust to the evolution of data and application contexts. Instead of considering data as being stationary, our view is to consider data as streams continuously evolving through time.Many studies are projecting a significant growth in software developers employment from 2020 to 2030. There is currently a global race on developing innovative research-based tools to support software development using artificial intelligence. One of the main objectives is for each country to be able to deal with its shortage of software developers. For a country like Canada, projects like ours is essential to lead this race. Efficient and reliable software development is becoming a key component of fulfilling the short-term and long-term needs of society.

对软件的需求从未如此之高，预计未来几年还会进一步增长。软件系统已经成为我们高度数字化社会的基础支柱，并在许多领域产生了重大影响，包括医疗保健、银行和自动驾驶汽车。与此同时，软件系统的复杂性随着时间的推移而增加，特别是当它们必须倾向于处理大量数据以及其他软件和硬件组件时。这可能对软件开发人员的效率产生巨大的影响。应对这些日益增长的需求和复杂性的一个解决方案是使用人工智能，特别是深度学习，来自动化一些复杂的软件工程任务。在过去的几年里，基于深度学习的方法使我们能够学习软件世界的有效模型，并成功地使用它们，例如，完成正在开发的项目的代码或使用自然语言查询搜索代码片段。尽管如此，这些模型在它们被训练的环境之外的应用是有限的。该项目的主要目标是使深度学习模型对数据和应用环境的演变更具适应性和鲁棒性。我们不认为数据是静止的，而是认为数据是随着时间不断演变的流。许多研究预测，从2020年到2030年，软件开发人员的就业将显著增长。目前，在开发基于研究的创新工具以支持使用人工智能的软件开发方面，全球正在展开一场竞赛。其中一个主要目标是让每个国家都能够解决软件开发人员短缺的问题。对于像加拿大这样的国家来说，像我们这样的项目对于引领这场竞赛至关重要。高效可靠的软件开发正在成为满足社会短期和长期需求的关键组成部分。