SHF: Small: Dynamic Analysis on Code Fragments

在许多软件工程环境中，软件开发人员希望理解，测试，调试和验证相对较小的代码片段，而不是整个程序。然而，当前可用的程序分析和测试工具仅对完整的程序起作用，在某种意义上，它们可以作为整个程序被编译和执行。当目标是仅针对小的代码片段时，在整个程序的上下文中测试和分析目标代码是非常昂贵和耗时的，这需要配置整个系统，设置执行环境，并找到提供执行整个程序所需的所有数据值的测试输入套件。 如果感兴趣的代码片段的程序属性可以作为独立的代码单元进行检查，则软件开发过程将更加高效和有效。这个研究项目解决了将代码片段转换为可测试单元的技术挑战。如果软件测试和分析可以成功地应用于代码片段，而不是整个程序，那么开发正确代码的整个过程将被简化。该项目侧重于动态程序分析，这是通过在运行时环境中执行程序来执行的计算机软件分析（相对于静态分析，这是对源代码的分析）。该项目将产生算法，工具和数据，可以推进程序分析工具和软件工程实践的艺术状态。结果将通过会议、课堂、开源项目、行业合作和STEM志愿者机会传播。为了使动态程序分析有效，目标程序必须在执行时有足够的测试输入，以产生有趣的（例如，不正确或异常）行为。动态分析是困难的，经常大量的程序路径，和大量的输入，必须进行测试。通常，测试是无效的，因为它们产生了大量的误报，并且经常在程序的某些部分失败，这些部分甚至与理解，调试或验证目标代码的目的无关。 该项目将开发一种方法，采用从原始程序中选择或构建的代码片段，并使用新的语法修补技术生成可编译和可执行的单元，以实现对代码片段的动态分析。该奖项反映了NSF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 many software engineering environments, software developers would like to understand, test, debug and verify a relatively small fragment of code instead of the entire program. However, currently available program analysis and testing tools only work on programs that are whole, in the sense that they can be compiled and executed as a whole program. When the goal is to target only a small code fragment, it is excessively expensive and time-consuming to test and analyze the targeted code in the context of entire programs, which requires configuring a whole system, setting up the execution environment, and finding suites of test inputs that supply all the needed data values to execute the whole program. Software development processes would be much more efficient and effective if a program property for the code fragment of interest could be checked as a standalone unit of code. This research project addresses the technical challenges in converting code fragments into testable units. If software testing and analysis could be applied successfully to code fragments, rather than whole programs, the overall process of developing correct code would be streamlined. This project focuses on dynamic program analysis, which is the analysis of computer software that is performed by executing programs in a run-time environment (as opposed to static analysis, which is analysis of the source code). The project will generate algorithms, tools, and data that can advance the state of the art of program analysis tools and software engineering practice. The results will be disseminated through conferences, classrooms, open source projects, industrial collaborations and STEM volunteer opportunities.For dynamic program analysis to be effective, the target program must be executed with sufficient test inputs to produce interesting (e.g., incorrect or anomalous) behaviors. Dynamic analysis is made difficult by frequently huge numbers of program paths, and the large number of inputs that must be tested. Often, the tests are ineffective because they produce an overwhelming number of false positives and often fail on parts of the program that are not even relevant to the purpose of understanding, debugging or verifying the targeted code. The project will develop an approach to take code fragments, selected or constructed from the original program, and generate compilable and executable units, using new syntactic patching techniques to enable dynamic analysis on code fragments. It will also develop a techniques to search for and select meaningful code fragments on which to operate.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.