SHF: Small: Preponderance of the Evidence for Behavioral Code Similarities

SHF：小：行为准则相似性的证据占优势

• 批准号: 1815494
• 负责人: Gail Kaiser
• 金额: $ 49.66万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815494&HistoricalAwards=false
• 关键词: SHF; Small; Preponderance; Evidence; Behavioral

Code clones are often produced when a software engineer reuses existing software and then tailors it to a new context. A large system may contain a lot of clones that were created from reused software or else from software maintenance where code was modified to fix bugs or adapt to changing requirements. The ability to detect code clones is fundamental to software development, maintenance and evolution. The ability to analyze software retrospectively to find same or similar code is very important to software quality and productivity. Clone detection has been an active area of research for many years. However, clones were considered same/similar if they had same/similar syntax. This project breaks new ground by detecting semantically similar clones, which may be consider same/similar even if they look different, i.e. have different syntax. To be semantically equivalent, clones need to have the same behaviors, which is to say they produce the same execution paths from the same inputs.This project investigates dynamic analysis approaches to identifying behavioral similarities among code elements in the same or different programs, particularly for code that behaves similarly during execution but does not look similar so would be difficult or impossible to detect using static analysis (code clones). While code clone technology is fairly mature, tools for detecting behavioral similarities are relatively primitive. The primary objective is to improve and shape behavioral similarity analysis for practical use cases, concentrating on finding similar code in the same or other codebases that might help developers understand, debug, and add features to unfamiliar code they are tasked to work with. The project seeks to advance knowledge about what it means for code to be behaviorally similar, how dynamic analyses can identify behavioral code similarities, how to drive the executions necessary for these analyses, and how to leverage code whose behavior is reported as highly similar to the code at hand to achieve common software engineering tasks that may be ill-suited to representational code similarities (code clones). The research investigates the utility and scalability of dynamic analyses seeking behavioral similarities in corresponding representations of code executions; guiding input case generation techniques to produce test executions useful for comparing/contrasting code behaviors for particular use cases; and filtering and weighting schemes for adapting the preponderance of the evidence metaphor to choosing the most convincing similarities for the software engineering task.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.

代码克隆通常是在软件工程师重用现有软件，然后将其裁剪到新的上下文时产生的。 一个大型系统可能包含大量的克隆，这些克隆是从重用的软件或软件维护中创建的，在软件维护中修改代码以修复错误或适应不断变化的需求。检测代码克隆的能力是软件开发、维护和演化的基础。回顾性地分析软件以发现相同或相似代码的能力对于软件质量和生产率非常重要。 克隆检测多年来一直是一个活跃的研究领域。然而，如果克隆具有相同/相似的语法，则认为它们相同/相似。该项目通过检测语义相似的克隆来开辟新天地，即使它们看起来不同，也可以被认为是相同/相似的，即具有不同的语法。为了在语义上等价，克隆需要具有相同的行为，也就是说，它们从相同的输入中产生相同的执行路径。本项目研究动态分析方法，以识别相同或不同程序中代码元素之间的行为相似性，特别是对于在执行期间行为相似但看起来不相似的代码，使用静态分析将很难或不可能检测到（代码克隆）。虽然代码克隆技术已经相当成熟，但用于检测行为相似性的工具还相对原始。主要目标是改进和塑造实际用例的行为相似性分析，专注于在相同或其他代码库中找到相似的代码，这可能有助于开发人员理解，调试和添加功能到他们负责处理的不熟悉的代码中。该项目旨在推进对代码行为相似性意味着什么，动态分析如何识别行为代码相似性，如何驱动这些分析所需的执行，以及如何利用其行为与手头代码高度相似的代码来实现可能不适合代表性代码相似性（代码克隆）的常见软件工程任务。该研究调查的实用性和可扩展性的动态分析寻求行为相似性的相应表示的代码执行，指导输入的情况下生成技术，以产生测试执行有用的比较/对比代码行为的特定用例;以及过滤和加权方案，以适应证据隐喻的优势，从而为软件工程任务选择最令人信服的相似性。反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Side Channel Attack on Smartphone Sensors to Infer Gender of the User

对智能手机传感器进行侧信道攻击以推断用户性别

• 发表时间: 2019
• 期刊: 17th ACM Conference on Embedded Networked Sensor Systems (SenSys
• 作者: Singh, Shirish;Shila, Devu Manikantan;Kaiser, Gail
• 通讯作者: Kaiser, Gail

DIRECT : A Transformer-based Model for Decompiled Identifier Renaming

DIRECT：基于 Transformer 的反编译标识符重命名模型

• 发表时间: 2021
• 期刊: 1st Workshop on Natural Language Processing for Programming (NLP4Prog
• 作者: Nitin, Vikram;Saieva, Anthony;Ray, Baishakhi;Kaiser, Gail
• 通讯作者: Kaiser, Gail

Testing DNN Image Classifier for Confusion & Bias Errors

测试 DNN 图像分类器的混淆情况

• 发表时间: 2020
• 期刊: 42nd International Conference on Software Engineering
• 作者: Tian, Yuchi;Zhong, Ziyuan;Ordonez, Vicente;Kaiser, Gail;Ray, Baishakhi
• 通讯作者: Ray, Baishakhi

SABER: Identifying SimilAr BEhavioR for Program Comprehension

• 发表时间: 2020
• 作者: Aditya Sridhar;Guanming Qiao;G. Kaiser

Integrating Parsons Puzzles with Scratch

将 Parsons 谜题与 Scratch 集成

• 发表时间: 2021
• 期刊: 29th International Conference on Computers in Education (ICCE
• 作者: Bender, Jeff;Zhao, Bingpu;Madduri, Lalitha;Dziena, Alex;Liebeskind, Alex;Kaiser, Gail
• 通讯作者: Kaiser, Gail