CAREER: Enhancing Deep-Learning-based Code Analyses via Human Intelligence
职业:通过人类智能增强基于深度学习的代码分析
基本信息
- 批准号:2146443
- 负责人:
- 金额:$ 52.71万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-05-01 至 2027-04-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).With the increasing availability of the millions of programs in open-source repositories, many techniques have been proposed to leverage deep-learning models to automatically learn patterns from large code bases to assist various software engineering tasks (e.g., security analysis, bug detection). However the proposed deep-learning models still face many input programs that are beyond a model’s handling capability due to many reasons (e.g., evolution of the program code). Because of the lack of understanding about these inputs, many software-engineering applications in industrial practice still make decisions based on symbolic-reasoning systems where decision logic and rules are hard-coded by a human. Human intelligence (e.g., rules summarized by humans) tends to be simplistic and reductionistic, while deep-learning models can be opaque and overfitted. If one can somehow combine the best of the two worlds, many existing challenges will disappear. Therefore, this proposal seeks to make progress on such a combination. The broad goal of this proposal is to design a general framework that improves deep-learning models’ handling of input programs by incorporating human intelligence. Specifically, two main issues are faced by existing deep-learning models in handling code data: (1) lack of understanding about inherent nature of code data, and (2) lack of domain-specific knowledge of software-engineering tasks. To address these fundamental limitations, this project proposes to design a general, user-driven learning-based framework. In the short term, this project aims to improve the practicality of intelligent code-analysis techniques and facilitate the adoption of deep learning techniques in code analysis. In the long run, this project has the potential to fundamentally transform the learning-based techniques for code analysis in software-engineering applications.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.
该奖项全部或部分由2021年美国救援计划法案(公法117-2)资助。随着开源存储库中数百万程序的可用性不断增加,已经提出了许多技术来利用深度学习模型从大型代码库中自动学习模式,以协助各种软件工程任务(例如,安全分析、bug检测)。 然而,由于许多原因,所提出的深度学习模型仍然面临许多超出模型处理能力的输入程序(例如,程序代码的演变)。 由于缺乏对这些输入的理解,工业实践中的许多软件工程应用仍然基于符号推理系统做出决策,其中决策逻辑和规则由人类硬编码。人类智能(例如,由人类总结的规则)往往是简单化和简化的,而深度学习模型可能是不透明和过度拟合的。 如果人们能以某种方式将两个世界的精华联合收割机结合起来,许多现有的挑战将消失。因此,本提案力求在这一合并方面取得进展。 该提案的总体目标是设计一个通用框架,通过引入人类智能来改进深度学习模型对输入程序的处理。具体来说,现有的深度学习模型在处理代码数据时面临两个主要问题:(1)缺乏对代码数据固有性质的理解,以及(2)缺乏软件工程任务的特定领域知识。为了解决这些基本的限制,本项目提出设计一个通用的,用户驱动的学习为基础的框架。在短期内,该项目旨在提高智能代码分析技术的实用性,并促进在代码分析中采用深度学习技术。从长远来看,该项目有可能从根本上改变软件工程应用程序中基于学习的代码分析技术。该奖项反映了NSF的法定使命,并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。
项目成果
期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
EREBA: Black-box Energy Testing of Adaptive Neural Networks
- DOI:10.1145/3510003.3510088
- 发表时间:2022-02
- 期刊:
- 影响因子:0
- 作者:Mirazul Haque;Yaswanth Yadlapalli;Wei Yang;Cong Liu
- 通讯作者:Mirazul Haque;Yaswanth Yadlapalli;Wei Yang;Cong Liu
DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks
- DOI:10.1145/3551349.3561158
- 发表时间:2022-10
- 期刊:
- 影响因子:0
- 作者:Simin Chen;Mirazul Haque;Cong Liu;Wei Yang
- 通讯作者:Simin Chen;Mirazul Haque;Cong Liu;Wei Yang
NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models
- DOI:10.1109/cvpr52688.2022.01493
- 发表时间:2022-03
- 期刊:
- 影响因子:0
- 作者:Simin Chen;Zihe Song;Mirazul Haque;Cong Liu;Wei Yang
- 通讯作者:Simin Chen;Zihe Song;Mirazul Haque;Cong Liu;Wei Yang
NMTSloth: understanding and testing efficiency degradation of neural machine translation systems
- DOI:10.1145/3540250.3549102
- 发表时间:2022-10
- 期刊:
- 影响因子:0
- 作者:Simin Chen;Cong Liu;Mirazul Haque;Zihe Song;Wei Yang
- 通讯作者:Simin Chen;Cong Liu;Mirazul Haque;Zihe Song;Wei Yang
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Wei Yang其他文献
The Development of Tissue Engineering Skin
组织工程皮肤的研究进展
- DOI:
10.1360/n052014-00276 - 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Wei Yang;Z. Cui - 通讯作者:
Z. Cui
High areal capacity flexible sulfur cathode based on multi-functionalized super-aligned carbon nanotubes
基于多功能超排列碳纳米管的高面积容量柔性硫阴极
- DOI:
10.1007/s12274-019-2356-1 - 发表时间:
2019 - 期刊:
- 影响因子:9.9
- 作者:
Jia Lujie;Wang Jian;Chen Zijin;Su Yipeng;Zhao Wei;Wang Datao;Wei Yang;Jiang Kaili;Wang Jiaping;Wu Yang;Li Jia;Duan Wenhui;Fan Shoushan;Zhang Yuegang - 通讯作者:
Zhang Yuegang
Diffuse Reflectance Distribution for Different Source Approximations
不同光源近似值的漫反射率分布
- DOI:
- 发表时间:
2014 - 期刊:
- 影响因子:0
- 作者:
X. Zhang;Yan Li;Wen;Wei Yang - 通讯作者:
Wei Yang
Driver pre-accident behavior pattern recognition based on dynamic radial basis function neural network
基于动态径向基函数神经网络的驾驶员事故前行为模式识别
- DOI:
10.1109/tmee.2011.6199209 - 发表时间:
2011 - 期刊:
- 影响因子:0
- 作者:
Jianqiang Gong;Wei Yang - 通讯作者:
Wei Yang
Race modifies the association between adiposity and inflammation in patients with chronic kidney disease: Findings from the chronic renal insufficiency cohort study: Adiposity and Inflammation
种族改变了慢性肾病患者肥胖与炎症之间的关联:慢性肾功能不全队列研究的结果:肥胖与炎症
- DOI:
- 发表时间:
2014 - 期刊:
- 影响因子:0
- 作者:
M. Wing;Wei Yang;V. Teal;S. Navaneethan;Kaixiang Tao;A. Ojo;Nicolas N. Guzman;M. Reilly;Melanie Wolman;S. Rosas;Magda Cuevas;Michael J. Fischer;E. Lustigova;S. Master;D. Xie;D. Appleby;M. Joffe;J. Kusek;H. Feldman;D. Raj - 通讯作者:
D. Raj
Wei Yang的其他文献
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{{ truncateString('Wei Yang', 18)}}的其他基金
Collaborative Research: CCRI: Planning-C: An Infrastructure and Dataset for Research in Android Testing & Analysis
合作研究:CCRI:Planning-C:Android 测试研究的基础设施和数据集
- 批准号:
2235137 - 财政年份:2023
- 资助金额:
$ 52.71万 - 项目类别:
Standard Grant
EAGER: Free Energy Sampling of Biomolecular Dynamics at Biological Timescales
EAGER:生物时间尺度上生物分子动力学的自由能采样
- 批准号:
1839694 - 财政年份:2018
- 资助金额:
$ 52.71万 - 项目类别:
Standard Grant
Who will care for you when you get old? A study of inequities in health and long-term care among the elderly in rural China
当你老了谁来照顾你?
- 批准号:
ES/N002717/1 - 财政年份:2016
- 资助金额:
$ 52.71万 - 项目类别:
Research Grant
Who will care for you when you get old? A study of inequities in health and long-term care among the elderly in rural China
当你老了谁来照顾你?
- 批准号:
ES/N002717/2 - 财政年份:2016
- 资助金额:
$ 52.71万 - 项目类别:
Research Grant
Achieving Long Timescale Sampling in Biomolecular Simulations
在生物分子模拟中实现长时间尺度采样
- 批准号:
1158284 - 财政年份:2012
- 资助金额:
$ 52.71万 - 项目类别:
Standard Grant
Achieving Long Timescale Sampling in Biomolecular Simulations
在生物分子模拟中实现长时间尺度采样
- 批准号:
0919983 - 财政年份:2009
- 资助金额:
$ 52.71万 - 项目类别:
Standard Grant
A Workshop in Plasticity and Commemorative Volume in Honor of Professor E.H. Lee
可塑性研讨会及纪念E.H.教授纪念册
- 批准号:
9019931 - 财政年份:1990
- 资助金额:
$ 52.71万 - 项目类别:
Standard Grant
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