Improving Test Suites Via Generated Specifications

通过生成的规范改进测试套件

• 批准号: 0234651
• 负责人: Michael Ernst
• 金额: $ 40万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-15 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0234651&HistoricalAwards=false
• 关键词: Improving; Test; Suites; Via; Generated

HDCCSR Proposal 0234651 Improving test suites via generated behavioral abstractions Michael ErnstTesting is a key to detecting bugs and increasing software reliability, but generating good test suites is difficult. This project investigates a novel technique for using behavioral differences to improve test suites. The technique compares dynamically generated behavioral abstractions of executions and selects the more complete one -- that is, the test suite that exercises more of the program's semantic behavior. Whereas previous automatic techniques compare dynamic (run-time) behavior to the static structure and text of the program, this new technique considers behavior, which complements structural techniques and is arguably more important.The technique is applied to generating, augmenting, and minimizing test suites. Users provide the program to be tested and a technique for generating test cases (via a grammar, random generation, traces collected from users, or otherwise). Users never need to provide a specification. However, the technique generates a behavioral abstraction that is syntactically identical to a formal specification and that, after being checked and perhaps augmented by a human, can beused as a specification. The efficacy of these techniques is investigated and improved, and they are applied in substantial case studies.

HDCCSR提案0234651通过生成的行为抽象改进测试套件Michael Ernst测试是检测错误和提高软件可靠性的关键，但生成好的测试套件是困难的。 这个项目研究了一种使用行为差异来改进测试套件的新技术。 该技术比较动态生成的执行的行为抽象，并选择更完整的一个--也就是说，测试套件执行更多的程序语义行为。 以前的自动化技术将动态（运行时）行为与程序的静态结构和文本进行比较，而这种新技术考虑了行为，它补充了结构化技术，并且可以说是更重要的。 用户提供要测试的程序和生成测试用例的技术（通过语法，随机生成，从用户收集的痕迹，或其他方式）。 用户永远不需要提供规范。 然而，该技术生成了一个行为抽象，它在语法上与正式规范相同，并且在被人类检查并可能被人类增强之后，可以被用作规范。 这些技术的有效性进行了调查和改进，并将其应用于大量的案例研究。