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博碩士論文 etd-0713106-201239 詳細資訊
Title page for etd-0713106-201239
論文名稱
Title
針對自動化產生測試資料的混合式基因演算法
A hybrid genetic algorithm for automatic test data generation
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
82
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2006-06-23
繳交日期
Date of Submission
2006-07-13
關鍵字
Keywords
軟體測試、結構測試、混合式基因演算法
software testing, structural testing, memetic algorithm, hybrid genetic algorithm
統計
Statistics
本論文已被瀏覽 5970 次,被下載 2344
The thesis/dissertation has been browsed 5970 times, has been downloaded 2344 times.
中文摘要
自動產生軟體測試資料近年來成為一個熱門的研究領域。許多方法也被提出用來解決這個問題,其中又以基因演算法(GeneticAlgorithm)最為盛行。然而它是否為最好的方法還不得而之。本論文提出一個混合式(Hybrid)的基因演算法來解決這個問題,它將傳統的基因演算法搭配上一個局部搜(Local Search)演算法,以提升基因演算法的效率,此混合機制在文獻中常被稱作Memetic Algorithm。本論文提出此方法的動機在於,從許多實際的程式中觀察到,測試資料產生的問題以最佳化的觀點來看並不是太過複雜,以至於基因演算法不見得會是適當的方法。從實驗結果中証實,我們所提出的混合式的基因演算法在許多例子上都表現得比目前已知的方法好。
Abstract
Automatic test data generation is a hot topic in recent software testing research. Various techniques have been proposed with different emphases. Among them, most of the methods are based on Genetic Algorithms (GA). However, whether it is the best Metaheuristic method for such a problem remains unclear. In this paper, we choose to use another approach which arms a GA with an intensive local searcher (the so-called Memetic Algorithm (MA) according to the recent terminology). The idea of incorporating local searcher is based on the observations from many real-world programs. It turns out the results outperform many other known Metaheuristic methods so far. We argue the needs of local search for software testing in the discussion of the paper.
目次 Table of Contents
1. Introduction... 9
2. Related Works... 12
2.1 Structural Testing... 12
2.1.1 Basic Concepts... 12
2.2.2 Generate Structural Test Data for Test Data Adequacy Criteria... 14
2.2 Overview of Genetic Algorithm (GA)... 17
2.2.1 Terminologies and Basic Concepts... 18
2.2.2 Coding Strategies in GA... 19
2.2.3 The Framework of Pure GA... 21
2.3 Evolutionary Testing (ET)... 28
3. The Proposed Memetic Algorithm (MA)... 37
3.1 Steady-State GA (SSGA)... 37
3.2 The Weakness of GA in the Problem of Branch Coverage... 40
3.3 Sequential Search... 43
3.4 The complete MA... 46
3.5 Test Data Generator System with MA... 48
4. Empirical Study and Discussion... 52
4.1 Benchmark Testing Programs... 52
4.2 Result Comparisons of MA to SS and EDA... 53
4.3 Result Comparisons of MA to SSGA and pure GA... 57
5. Conclusion... 60
6. References... 61
Appendix A: Source code of Trangle1 and the corresponding CFG... 66
Appendix B: Source code of Trangle3 and the corresponding CFG... 69
Appendix C: Source code of Trangle4 and the corresponding CFG... 72
Appendix D: Source code of Atof and the corresponding CFG... 75
Appendix E: Source code of Remainder and the corresponding CFG... 78
Appendix F: Source code of ComplexBranch and the corresponding CFG... 80
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