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Automatic Generation and Reduction of Random Test Case

Author: CaoQing
Tutor: ZengFanPing
School: University of Science and Technology of China
Course: Computer System Architecture
Keywords: Random test Test Case Program invariants Test Automation
CLC: TP311.53
Type: Master's thesis
Year: 2010
Downloads: 165
Quote: 2
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Abstract


Software as the soul of the computer, plays a pivotal role in the national economy and social life. The problem of software quality directly affect the normal operation of the industry, the software error ranging from inconvenience to the user, while causing huge economic losses and even casualties. Software testing is an important means to guarantee the quality and reliability of software is an integral part of software development. With the increasing demands of the users of the software product quality as well as the increasing maturity of software engineering technology, the growing proportion of software testing throughout the software development cycle. Design test cases is the core issue of software testing, software testing is good or bad depends largely on the quality and quantity of the test cases. Design test cases often need to be done manually, time-consuming and inefficient. Therefore, designed to achieve a high-quality test cases automated generation tool for improving the efficiency and quality of software testing has important research significance and application value. Generate the core issue of this paper, test cases, integration of random testing and the invariant extraction technique, designed to achieve a fully automated test case generation framework tools. On the basis of analysis of the advantages and disadvantages of random testing, focusing on the advantages of a high degree of test automation using random 1) use the default value of technology to improve the coverage of random testing, and support for user-defined default value, scalability; 2) complex data types recursively generate a wider range of applications; 3) the introduction of error test case filtering mechanism to ensure normal execution of test cases; 4) invariants remains constant during program execution logic assertions, is a description of the program attributes. With this feature, the reduction algorithm based on variable changes, effective reduction of randomly generated test cases, and eventually generate test cases appropriate scale; 5) new termination criterion for test case generation, to solve the disadvantages of the previous time as the limiting condition. The final program output includes three parts: the normal test cases for manual troubleshooting or regression testing can reveal a procedural defect error test cases, and reveal the invariants of the program attribute. Proved by experiments, this paper realized random test case generation technique to generate an appropriate scale can no human intervention in the case of a fully automated test cases; prominent invariants technology-based test case reduction algorithm results; output of the error test use cases can reveal errors in the program; significantly improved compared with pure random test case code coverage, with a default value; mutation testing fully reaches not less than the level of similar test case generation tool.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Software Engineering > Software Maintenance
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