EAGER: Bio-inspired Assurance and Regression Testing to Secure Organic Programs

软件工程需要解决一类越来越重要的自适应和自我修复的程序。这些程序感知环境的变化，并通过修改配置、库或程序代码做出反应。此外，程序修复和程序移植的自动化方法改变了程序？的源代码直接修复，优化或添加新的功能。总之，自我修改提供了在变化出现时的持续可用性，并可以加强系统对入侵者的防御。虽然这种自我修改的有机本质是一个强大的范例，但这些程序的整体可靠性和安全性处于危险之中。一旦发生任何调整，先前的测试结果或为推理其功能、安全性和保障性而开发的保证案例将立即失效。然而，需要运行一个完整的回归测试周期，或开发新的证据和论据的保证cases.This建议可能需要几个小时，几天或几周的时间探索推理动态和有效地对有机程序及其相关的测试套件和保证案例的变化在适应的可行性。鉴于这些程序模仿生命系统，并且许多适应性已经通过进化算法实现，因此拟议的工作从自然界中汲取灵感，并使用生物启发技术来实现其目标。传统的功能故障以及安全漏洞都有针对性的建议，在一个在线的，不受监控的系统中的漏洞的关键性。更具体地说，该提案将首先探索确定保证演进时间轴的技术，找出必须进行重新验证的间隔。其次，它将在有机程序中模拟交互和测试覆盖率。最后，它将使用生物启发的算法来选择，排序和生成回归测试的测试。本提案中执行的研究结果将为能够验证有机程序的正确性和安全性奠定基础。 更广泛的影响包括共同指导本科生，跨学科团队参加国际遗传工程机器（iGEM）竞赛，并为他们项目的安全性推理建立保证案例。

Software Engineering needs to address an increasingly significant class of programs that are self-adaptive and self-healing. These programs sense changes to their environment and react by modifying configurations, libraries or program code. Furthermore, automated approaches for program repair and program transplantation change a program?s source code directly to fix, optimize or add new functionality. Together, self-modification provides continual availability in the presence of change and can harden a system against intruders. While this organic nature of self-modification is a powerful paradigm, the overall dependability and security of such programs is at risk. Prior test results or assurance cases developed to reason about their functionality, safety and security are invalid as soon as any adaptation occurs. Yet the time needed to run a full regression testing cycle, or to develop new evidence and arguments for an assurance case can take hours, days or weeks.This proposal explores the feasibility of reasoning dynamically and efficiently about the changes made to organic programs and their associated test suites and assurance cases during adaptation. Given that these programs mimic living systems, and many adaptations are already implemented with evolutionary algorithms, the proposed work draws inspiration from nature and uses bio-inspired techniques to achieve its goals. Both traditional functional faults as well as security vulnerabilities are targeted in this proposal, given the criticality of vulnerabilities in an online, unmonitored system. More specifically the proposal will first explore techniques to identify the evolution timeline for assurance, finding intervals at which re-validation must occur. Second, it will model interactions and test coverage in organic programs. Finally, it will use biologically inspired algorithms to select, order and generate tests for regression testing.The results of the research performed in this proposal will form the foundations for being able to validate the correctness and safety of organic programs over time. The broader impacts include co-mentoring undergraduate, inter-disciplinary teams to compete in the International Genetically Engineered Machine (iGEM) competition, and to build assurance cases for reasoning about the safety of their projects.