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

软件工程需要处理越来越重要的一类自适应和自修复的程序。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？S源代码直接修复，优化或增加新的功能。总之，自我修改在发生变化时提供了持续的可用性，并且可以增强系统抵御入侵者的能力。虽然这种自我修改的有机性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，先前的测试结果或开发用于推断其功能、安全性和安全性的保证用例就会失效。然而，运行一个完整的回归测试周期，或者为一个保证案例开发新的证据和论据所需的时间可能需要数小时、数天或数周。这个建议探讨了动态和有效地推理在适应过程中对有机程序及其相关的测试套件和保证用例所做的更改的可行性。考虑到这些程序模拟生命系统，并且许多适应已经通过进化算法实现，提议的工作从大自然中汲取灵感，并使用生物启发技术来实现其目标。考虑到在线、不受监控的系统中漏洞的严重性，该方案既针对传统的功能故障，也针对安全漏洞。更具体地说，该提案将首先探索确定保证的演进时间表的技术，找到必须发生重新验证的间隔。其次，它将模拟有机程序中的交互和测试覆盖率。最后，它将使用受生物学启发的算法来选择、排序和生成回归测试的测试。在本提案中进行的研究结果将形成能够随着时间的推移验证有机程序的正确性和安全性的基础。更广泛的影响包括共同指导本科生、跨学科团队参加国际基因工程机器（iGEM）竞赛，并为其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.