缓解学术静态分析器的维护负担
Easing Maintenance of Academic Static Analyzers
AI总结：
本文介绍自2017年以来为简化Mopsa静态分析平台维护而设计的工具与技术，包括自动精度测量、抽象执行观察工具及测试用例缩减，以提升透明度并发现回归。
摘要：
静态分析领域的学术研究会产生软件实现。这些实现开发耗时，其中一些需要维护，以便在实现基础上开展进一步研究。尽管这些过程是必要的，但可能很快变得具有挑战性。本文记录了自2017年以来我们为简化Mopsa维护而提出的工具和技术。Mopsa是一个旨在保证可靠（sound）的静态分析平台。首先，我们描述了一种自动测量精度的方式，该方法不需要通过人工检查结果获得真实缺陷的基线。此外，它提高了分析的透明度，并有助于在持续集成过程中发现回归问题。其次，我们从观察程序具体执行的标准工具中汲取灵感，设计了观察被分析程序自身抽象执行的自定义工具，例如抽象调试器和分析器。最后，我们报告了一些自动化测试用例缩减的案例。
英文摘要：
Academic research in static analysis produces software implementations. These implementations are time-consuming to develop and some need to be maintained in order to enable building further research upon the implementation. While necessary, these processes can be quickly challenging. This article documents the tools and techniques we have come up with to simplify the maintenance of Mopsa since 2017. Mopsa is a static analysis platform that aims at being sound. First, we describe an automated way to measure precision that does not require any baseline of true bugs obtained by manually inspecting the results. Further, it improves transparency of the analysis, and helps discovering regressions during continuous integration. Second, we have taken inspiration from standard tools observing the concrete execution of a program to design custom tools observing the abstract execution of the analyzed program itself, such as abstract debuggers and profilers. Finally, we report on some cases of automated testcase reduction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
