SHF: Small: Foundations of Software Testing Representations of Natural Processes

SHF：小：软件测试的基础自然过程的表示

• 批准号: 1909688
• 负责人: Myra Cohen
• 金额: $ 50万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909688&HistoricalAwards=false
• 关键词: SHF; Small; Foundations; Software; Testing

Over the past 20 years, scientific computing has become a staple for exploration and reasoning about natural processes. For instance, researchers in bioinformatics routinely use computational tools to understand the relationship between a genetic sequence and the behavior of an organism with that sequence. Research questions concerning natural processes range from decoding biological pathways to determining if a mutation can lead to cancer. During this same time, novel computation techniques have been investigated, such as programming DNA via chemical reaction networks (CRNs). Many new programming environments, simulation platforms, and tools have been developed to support these new research directions and are now widely used. Since these programs are being employed to advance scientific discovery and to perform critical tasks, there is a need to ensure they behave correctly. This project develops foundations for software testing of these natural computing systems using natural representations. It focuses on developing quality test suites, handling error rates in test outcomes and validating behavior in the absence of known answers. Research topics from this project will be incorporated into courses on software testing and molecular programming. Undergraduate students will participate in this research and encouraged to compete in the International Genetically Engineered Machine competition. Bioinformatics tools, and programming via chemical reaction networks (CRNs), result in simulations of a natural process such as an organism's growth, the interactions between molecules as reactions execute over time, DNA alignment, etc. CRNs themselves are representations of a naturally occurring process (a set of chemical reactions in solution), that can be provably manipulated to perform computations. While these types of abstractions form a powerful and growing computational paradigm, these are encoded as software programs, which simulate the natural processes, and hence they are prone to faults. Thus, they need to be tested to ensure they behave as expected. There are several characteristics that make these representations challenging to validate. First, the inputs and outputs may not be clearly defined as in traditional software systems, and the connection between inputs and execution of paths in the software is often unclear. Second, the use case may determine the expected results, and the expected results may be sets of information, rather than a single property or value. Third, they may compute a result stochastically, which is correct most of the time, yet incorrect within an allowed error. Fourth, these systems are often simulated using complex sets of options that when modified can change both the non-functional behavior as well as the functional answers returned. This project develops foundations for software testing of natural representations. More specifically, it develops techniques for test generation with measurable code and model coverage. It creates testing methods that infer oracles and utilize metamorphic relations. Finally, it designs configuration-aware testing and optimization techniques to guide end-users who depend upon the results of these systems.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.

在过去的20年里，科学计算已经成为探索和推理自然过程的主要手段。 例如，生物信息学的研究人员通常使用计算工具来理解基因序列与具有该序列的生物体的行为之间的关系。关于自然过程的研究问题从解码生物途径到确定突变是否会导致癌症。 在此期间，新的计算技术已经被研究，例如通过化学反应网络（CRN）编程DNA。 许多新的编程环境，仿真平台和工具已经开发出来，以支持这些新的研究方向，现在被广泛使用。由于这些程序被用于推进科学发现和执行关键任务，因此需要确保它们正确运行。这个项目开发的基础，这些自然计算系统的软件测试使用自然表示。它专注于开发高质量的测试套件，处理测试结果中的错误率，并在没有已知答案的情况下验证行为。 该项目的研究课题将纳入软件测试和分子编程课程。 本科生将参与这项研究，并鼓励他们参加国际遗传工程机器竞赛。生物信息学工具和通过化学反应网络（CRN）进行的编程可以模拟自然过程，例如生物体的生长，随着时间的推移反应执行时分子之间的相互作用，DNA对齐等。 虽然这些类型的抽象形成了一个强大的和不断增长的计算范式，这些被编码为软件程序，模拟自然过程，因此它们很容易出错。因此，需要对其进行测试，以确保其行为符合预期。有几个特征使得这些表示难以验证。首先，输入和输出可能不像传统软件系统那样被清楚地定义，并且软件中的输入和路径执行之间的连接通常是不清楚的。其次，用例可以确定预期的结果，并且预期的结果可以是信息集，而不是单个属性或值。第三，他们可能会随机计算出一个结果，这个结果在大多数情况下是正确的，但在允许的误差范围内是不正确的。第四，这些系统通常使用复杂的选项集进行模拟，这些选项在修改时可以改变非功能性行为以及返回的功能性答案。 该项目为自然表示的软件测试奠定了基础。更具体地说，它开发了具有可测量的代码和模型覆盖率的测试生成技术。它创建了推断神谕和利用变形关系的测试方法。最后，它设计了配置感知测试和优化技术，以指导依赖这些系统结果的最终用户。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。

项目成果

Bringing Together Configuration Research: Towards a Common Ground

• DOI: 10.1145/3563835.3568737
• 发表时间: 2022-11
• 期刊: Proceedings of the 2022 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software
• 作者: Paul Gazzillo;Myra B. Cohen

Keeping Secrets: Multi-objective Genetic Improvement for Detecting and Reducing Information Leakage

保守秘密：用于检测和减少信息泄露的多目标遗传改进

• DOI: 10.1145/3551349.3556947
• 发表时间: 2022
• 期刊: Proceedings of the 37th IEEE/ACM International Conference on Software Engineering (ASE
• 作者: Mesecan, Ibrahim;Blackwell, Daniel;Clark, David;Cohen, Myra B.;Petke, Justyna
• 通讯作者: Petke, Justyna

HyperGI: Automated Detection and Repair of Information Flow Leakage

HyperGI：信息流泄露的自动检测与修复

• DOI: 10.1109/ase51524.2021.00168
• 发表时间: 2021
• 期刊: IEEEACM International Conference on Automated Software Engineering
• 作者: Mesecan, I;Blackwell, D;Clark, D.;Cohen, M.B.;Petke, J.
• 通讯作者: Petke, J.

Reactamole: Functional Reactive Molecular Programming

Reactamole：功能反应分子编程

• 发表时间: 2021
• 期刊: 27th International Conference on DNA Computing and Molecular Programming (DNA 27
• 作者: Klinge, Titus H.;Lathrop, James I.;Osera, Peter-Michael;Rogers, Allison
• 通讯作者: Rogers, Allison

CORE: Comparable, Open, Reliable, Extensible Software: An Experience Report of Four Flux Balance Analysis Tools (Poster)

核心：可比、开放、可靠、可扩展的软件：四种磁通平衡分析工具的体验报告（海报）

• 发表时间: 2023
• 期刊: and Health Informatics (ACM BCB
• 作者: Alexis L. Marsh, Myra B.