Outline

  • Abstract
  • Keywords
  • 1 Introduction
  • 2 Evaluation of Test Case Prioritization
  • 2.1 Some Existing Evaluation Function
  • 2.2 Average Percentage of Test-Points Coverage (aptc)
  • 3 Test Case Prioritization Based on Genetic Algorithm
  • 3.1 Process of Genetic Algorithm
  • 3.2 Design of Representation
  • 3.3 Design of Fitness Function
  • 3.4 Design of Selection
  • 3.5 Design of Crossover
  • 3.6 Design of Mutation
  • 4 Simulation Experiment
  • 5 Conclusion
  • References

رئوس مطالب

  • چکیده
  • کلید واژه ها
  • 1 مقدمه
  • 2 ارزیابی اولویت بندی مورد آزمایشی
  • 2.1 برخی توابع ارزیابی موجود
  • 2.2 درصد میانگین پوشش نقاط آزمایش (APTC)
  • 3 اولویت بندی مورد آزمایشی براساس الگوریتم ژنتیک
  • 3.1 فرآیند الگوریتم ژنتیک
  • 3.2 طرح نمایش
  • 3.3 طراحی تابع انطباق
  • 3.4 طرح انتخاب
  • 3.5 طرح تقابل
  • 3.6 طرح جهش
  • 4 آزمایش شبیه سازی
  • 5 نتیجه گیری

Abstract

By optimizing the execution order of test cases, test case prioritization techniques can effectively improve the efficiency of software testing. Test case prioritization is becoming a hot topic in software testing research. Combining genetic algorithm with test-points coverage, this paper obtains some meaningful research results in test case prioritization, especially for the functional testing. Firstly, presents two new test case prioritization evaluations APTC and its improvement APRC_C. As focused on test-points coverage, these evaluations are more suitable for black-box testing. Then, proposes a test case prioritization method based on genetic algorithm, whose representation, selection, crossover and mutation are designed for black-box testing. Finally, verifies the proposed method by experiments data. The experimental results show that the proposed method can achieve desired effect.

Keywords: - - - - -

Conclusions

The use of genetic algorithms in test case prioritization, can effectively reduce the blindness in test cases executed order and so improve the efficiency of software testing.

This paper proposed a new test case prioritization evaluation APRC and its improvement APRC_C for functional testing. As focused on test-points coverage, these evaluations are more suitable for black-box testing.

In addition, this paper presented an automated test case prioritization method using of genetic algorithms which adopted APTC or APTC_C as fitness function. The designs of representation, selection, crossover and mutation in GA are aimed at blackbox testing. We gave the specific steps of the method and validated it by experimental data.

The experimental results show that the proposed method can achieve expected results. It provides an effective technical approach to the test case prioritization problem. In the future, we will do further research in test-points automatically conversation and applications of GA in the automated generation of black-box test cases.

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