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

当软件工程师重用现有软件，然后将其定制为新的上下文时，通常会产生代码克隆。大型系统可能包含许多克隆，这些克隆是由重用的软件创建的，或者是由修改代码以修复错误或适应不断变化的需求的软件维护创建的。检测代码克隆的能力是软件开发、维护和发展的基础。回顾性分析软件以发现相同或相似代码的能力对软件质量和生产力非常重要。克隆检测多年来一直是一个活跃的研究领域。但是，如果克隆具有相同/相似的语法，则认为它们相同/相似。这个项目通过检测语义上相似的克隆而开辟了新天地，即使它们看起来不同，也可能被认为是相同/相似的，即具有不同的语法。为了在语义上等效，克隆需要具有相同的行为，也就是说它们从相同的输入产生相同的执行路径。该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，特别是对于在执行过程中行为相似但看起来不相似的代码，因此使用静态分析（代码克隆）很难或不可能检测到。虽然代码克隆技术相当成熟，但用于检测行为相似性的工具却相对原始。主要目标是为实际用例改进和塑造行为相似性分析，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、调试和向他们任务处理的不熟悉的代码添加特性。该项目旨在推进以下方面的知识：代码行为相似意味着什么，动态分析如何识别行为代码相似，如何驱动这些分析所需的执行，以及如何利用行为报告为与手头代码高度相似的代码来实现可能不适合表示代码相似（代码克隆）的通用软件工程任务。该研究探讨了动态分析在代码执行的相应表示中寻求行为相似性的效用和可扩展性；指导输入用例生成技术，以生成对比较/对比特定用例的代码行为有用的测试执行；以及过滤和加权方案，以适应证据隐喻的优势，为软件工程任务选择最令人信服的相似性。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.