DeMoCo: Developer-Centered, Neural Models of Code

DeMoCo：以开发人员为中心的神经代码模型

• 批准号: 492507603
• 负责人: Professor Dr. Michael Pradel
• 金额: --
• 依托单位: Abteilung Software Lab (SOLA)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/492507603?language=en
• 关键词: DeMoCo; Developer; Centered; Neural; Models

Neural software analysis learns predictive models from large code corpora to address challenging software engineering tasks. It has been gaining momentum over recent years, complementing and sometimes even outperforming traditional program analysis. At the core of these techniques are neural models of code, i.e., deep learning models that reason about programs and their properties to make predictions useful to developers. Unfortunately, current neural models of code are mostly driven by what data is easily available, e.g., reading thousands of source code files from the first to the last token each, and they make predictions that are difficult to understand for humans, e.g., by classifying an entire method as buggy without further explanation. As a result, many current techniques achieve impressive accuracy but still remain of limited use to developers. This proposal puts the human developer into the center of neural models of code, shifting from a data-centered paradigm to a developer-centered paradigm. Concretely, we plan to pursue three strands of research, which will (i) increase our understanding of how human reasoning and neural reasoning about programs relate to each other, (ii) design neural models of code that imitate how developers reason about and explore code, and (iii) create models that not only predict properties of code but also explain the predictions to developers. Developer-centered neural models of code will be potentially applicable in a wide spectrum software engineering tasks. As concrete examples, this proposal will apply them to bug detection and fault localization.

神经软件分析从大型代码语料库中学习预测模型，以解决具有挑战性的软件工程任务。近年来，它的势头一直在增强，它补充了传统的程序分析，有时甚至超过了传统的程序分析。这些技术的核心是代码的神经模型，即对程序及其属性进行推理以使预测对开发人员有用的深度学习模型。不幸的是，当前的代码神经模型主要是由容易获得的数据驱动的，例如，从第一个令牌到最后一个令牌每个读取数千个源代码文件，并且它们做出人类难以理解的预测，例如，通过将整个方法归类为有错误而没有进一步解释。因此，目前的许多技术都达到了令人印象深刻的精确度，但对开发人员的使用仍然有限。该建议将人类开发人员置于代码神经模型的中心，从以数据为中心的范例转变为以开发者为中心的范例。具体地说，我们计划进行三个方面的研究，这将(I)增加我们对人类推理和关于程序的神经推理如何相互关联的理解，(Ii)设计模仿开发人员如何推理和探索代码的代码的神经模型，以及(Iii)创建不仅预测代码属性而且向开发人员解释预测的模型。以开发人员为中心的神经模型的代码将潜在地适用于广泛的软件工程任务。作为具体的例子，该提案将把它们应用于错误检测和故障定位。