SHF: SMALL: Automated Discovery of Cross-Language Program Behavior Inconsistency

在软件行业中，存在数百种编程语言，其中许多编程人员都被期望精通。通常的假设是，一旦程序员掌握了一种语言，他们就可以利用已经学会的概念和知识，轻松地学习另一种编程语言。不幸的是，实证研究发现，由于跨编程语言的概念和表达式之间不精确的不匹配，这个过程容易出错且无效。该项目通过识别和解释不同语言中代码的行为是如何关联的，开发了一些技术来简化新编程语言知识的获取。预期的结果是，程序员将更快地学习新语言，编写更少bug的代码。除了对受过良好教育的程序员的普遍好处之外，教授计算机编程的技术尤其重要，因为编程是数字文化社会的一项关键技能。该项目将开发自动识别两种编程语言之间的缺陷和潜在误解的技术。本项目将调查两个主要的研究任务。第一个任务是开发一种基于动态行为、可能的不变量、观察到的副作用和性能自动识别相似代码簇的方法。行为集群是由多种语言的片段组成的，这些语言在相同的输入上产生相同的输出。从观察到的行为可能的不变量被用来描述相似性和差异性。第二个任务是开发一种技术，以识别当程序员认为代码应该表现相同但实际上并非如此时出现的误解。为了识别误解，该技术利用了代码相似性分析中的行为聚类和特征；看起来相似但在公开（行为）或隐蔽（性能、副作用）方面表现不同的代码是候选代码。该技术将根据出现的概率和可能的影响对误解进行排名。最后，该技术将使用不变量、行为、副作用和性能来形成对行为相似性和差异的自动解释。最后，这些技术和解释将应用于两组真正的程序员：了解一门语言并需要学习一门新语言的转校生，以及使用多种编程语言完成任务的数据科学家。对于学习一门新语言的程序员，无论是学生、专业人士还是业余爱好，本书旨在提高他们掌握编程语言知识的速度和可靠性。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。

In the software industry, hundreds of programming languages exist, many of which programmers are expected to be proficient in. The common assumption has been that once a programmer knows one language, they can leverage concepts and knowledge already learned and easily pick up another programming language. Unfortunately, empirical studies find this process to be error-prone and ineffective due to imprecise mismatches between concepts and expressions across programming languages. This project develops techniques to ease the acquisition of knowledge for new programming languages by identifying and explaining how the behaviors of code in different languages relate. The anticipated result is that programmers will learn new languages faster and write code with fewer bugs. Beyond the general benefit of better-educated programmers, techniques for teaching computer programming are important in particular because programming is a crucial skill for a digitally literate society.This project will develop techniques to automatically identify incapabilities and potential misconceptions between two programming languages. Two main research tasks will be investigated for this project. The first task is to develop an approach for automatically identifying clusters of similar code based on dynamic behavior, likely invariants, observed side effects, and performance. Behavioral clusters are formed from snippets in multiple languages that produce the same outputs on the same inputs. Likely invariants from observed behavior are used to describe similarities and differences. The second task is to develop a technique to identify misconceptions that emerge when a programmer assumes code should behave the same but it does not. To identify misconceptions, the technique leverages the behavior clusters and characterizations from the code similarity analysis; code that looks similar but behaves differently in overt (behavior) or insidious (performance, side effects) ways are candidates. The technique will rank misconceptions based on probability of appearing and likely impact. Finally, the technique will use invariants, behavior, side effects and performance to form automated explanations of behavioral similarities and differences. Finally, these techniques and explanations will be applied for the benefit of two groups of real programmers: transfer students who know one language and need to learn a new one, and data scientists who work with many programming languages to complete their tasks. For programmers learning a new language, in student, professional, or hobby capacities, this work aims to increase the speed and reliability with which they acquire knowledge of the programming language.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.