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SHF: MEDIUM: Achieving Software Reliability without True Test Oracles

SHF：中：在没有真正的测试预言机的情况下实现软件可靠性

基本信息

批准号：
1161079
负责人：
Gail Kaiser
金额：
$ 89.46万
依托单位：
Columbia University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-01 至 2016-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1161079&HistoricalAwards=false
关键词：
SHF MEDIUM Achieving Software Reliability

项目摘要

Conventional software testing checks whether each output is correct for the set of test inputs. But for some software, it is not known what the correct output should be for some inputs -- yet it is still important to detect coding errors in that software, so they can be fixed. This dilemma arises frequently for machine learning, simulation and optimization applications, often "Programs which were written in order to determine the answer in the first place. There would be no need to write such programs, if the correct answer were known." As these kinds of applications are frequently used in public infrastructure and biomedical research (domains targeted in this research), it is critical to detect and fix errors before a calamity occurs. Fortunately, many such applications reflect 'metamorphic properties' that define a relationship between pairs of inputs and outputs, such that for any previous input i with its already known output o, one can easily derive a test input i' and predict the expected output o'. If the actual output o'' is different from o', then there must be an error in the code. This project investigates methodology for determining the metamorphic properties of software and for devising good test cases from which the secondary tests can be derived. The project extends the inputs/outputs considered in previous work on metamorphic testing to focus on application state, before and after, rather than just functional parameters and results. The research also extends the pairwise relations implied by metamorphic properties to 'semantic similarity' for nondeterministic applications, applied to profiles from numerous executions, since an exact relation cannot be expected to hold for a single pair of test executions. These extensions enable treatment of more sophisticated properties that preliminary experiments have shown to reveal defects that were not detected otherwise.

传统的软件测试检查每个输出对于测试输入集是否正确。但对于某些软件，不知道某些输入的正确输出应该是什么--但检测软件中的编码错误仍然很重要，这样就可以修复它们。这种困境经常出现在机器学习、模拟和优化应用中，通常是“为了首先确定答案而编写的程序”。如果正确答案是已知的，就没有必要编写这样的程序。由于这类应用程序经常用于公共基础设施和生物医学研究（本研究的目标领域），因此在灾难发生之前检测和修复错误至关重要。幸运的是，许多这样的应用反映了定义输入和输出对之间的关系的“变形属性”，使得对于任何先前的输入i及其已知的输出o，可以容易地导出测试输入i“并预测预期输出o”。如果实际输出的o“与o”不同，那么代码中一定有错误。本项目研究用于确定软件的变形属性和设计良好的测试用例的方法，从这些测试用例中可以导出二次测试。该项目扩展了以前在变形测试工作中考虑的输入/输出，以关注应用程序状态，之前和之后，而不仅仅是功能参数和结果。研究还扩展了成对的关系所隐含的变质属性的“语义相似性”的不确定性应用程序，适用于配置文件从众多的执行，因为一个确切的关系不能被期望保持一对测试执行。这些扩展使得能够处理更复杂的特性，初步实验已经显示出能够揭示以其他方式未检测到的缺陷。