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

在过去的20年里，科学计算已经成为对自然过程进行探索和推理的主要手段。例如，生物信息学的研究人员经常使用计算工具来理解基因序列和具有该序列的有机体的行为之间的关系。关于自然过程的研究问题范围从破译生物途径到确定突变是否会导致癌症。在此期间，人们研究了新的计算技术，例如通过化学反应网络(CRN)对DNA进行编程。为了支持这些新的研究方向，已经开发了许多新的编程环境、仿真平台和工具，并且现在得到了广泛的应用。由于这些程序被用来推进科学发现和执行关键任务，因此有必要确保它们正确运行。该项目为使用自然表示法对这些自然计算系统进行软件测试奠定了基础。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。这个项目的研究主题将被纳入软件测试和分子编程的课程。本科生将参与这项研究，并鼓励他们参加国际基因工程机器大赛。生物信息学工具和通过化学反应网络(CRN)的编程，导致模拟自然过程，如有机体的生长、随着时间的推移反应执行时分子之间的相互作用、DNA比对等。CRN本身是自然发生过程(溶液中的一组化学反应)的表示，可以被证实地操纵以执行计算。虽然这些类型的抽象形成了一个强大且不断增长的计算范例，但它们被编码为模拟自然过程的软件程序，因此它们容易出错。因此，需要对它们进行测试，以确保它们的行为符合预期。有几个特征使这些表示具有挑战性，难以验证。首先，输入和输出可能不像在传统软件系统中那样被明确定义，并且软件中输入和执行路径之间的联系往往不清楚。其次，用例可以确定预期结果，而预期结果可以是多组信息，而不是单个属性或值。第三，他们可能会随机计算一个结果，这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来模拟，当修改这些选项集时，可以更改非功能行为以及返回的功能答案。该项目为自然表示的软件测试奠定了基础。更具体地说，它开发了使用可测量的代码和模型覆盖率生成测试的技术。它创造了推断先知和利用变质关系的测试方法。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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.