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智能科学技术著作丛书:测试数据进化生成理论及应用

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《智能科学技术著作丛书:测试数据进化生成理论及应用》阐述采用遗传算法生成复杂软件测试数据的原理及应用,主要包括如下4个方面:路径覆盖测试数据进化生成、遗传算法用于测试数据生成的性能提高、语句覆盖可测试性转化及变异测试数据进化生成等;还给出不同方法在基准和工业软件测试中的应用,以及全面的方法对比结果。书后附有基本遗传算法源代码和实验程序。
《智能科学技术著作丛书:测试数据进化生成理论及应用》可供高等院校计算机、自动化等专业的教师及研究生阅读,也可供自然科学和工程技术领域的研究人员及软件测试从业人员参考。
Author Description

巩敦卫,男,1970年3月生,江苏省铜山县人,博士,中国矿业大学信息与电气工程学院教授,博士生导师,江苏省重点学科“控制理论与控制工程”方向带头人,教育部“新世纪优秀人才支持计划”入选者,江苏省“333高层次人才培养工程”培养对象,江苏省“六大人才高峰”高层次人才培养对象,“煤炭青年科技奖”获得者,江苏省自动化学会理事,中国矿业大学学术委员会委员。研究方向为智能优化与控制、基于搜索的软件工程。近年来,主持国家自然科学基金项目5项、江苏省自然科学基金项目2项,以及教育部“新世纪优秀人才支持计划”项目、江苏省“333高层次人才培养工程”项目、江苏省“六大人才高峰”高层次人才项目、高等学校博士学科点专项科研基金项目各1项,此外,还主持中国矿业大学校优秀创新团队建设专项基金项目和培育学科创新能力提升基金项目各1项。研究成果获省部级科技进步一等奖1项、二等奖4项;授权发明专利2项、计算机软件著作权登记1项;出版专著5部、教材1部;发表学术论文170余篇,其中,被SCI、EI等检索130余篇,被同行引用600余次。

姚香娟,女,1975年3月生,河北省赵县人,博士,中国矿业大学理学院副教授,硕士生导师,中国矿业大学优秀青年骨干教师,中国矿业大学优秀创新团队“复杂软件自动测试”核心成员。研究方向为基于进化优化的软件测试数据生成。近年来,主持国家自然科学基金项目1项、江苏省自然科学基金项目1项、中央高校基本科研业务费青年科技基金项目1项,参与国家自然科学基金项目2项、江苏省自然科学基金项目2项,以及江苏省“333高层次人才培养工程”项目、江苏省“六大人才高峰”高层次人才项目、高等学校博士学科点专项科研基金项目各1项。研究成果获省部级科技进步二等奖1项。出版教材1部,发表学术论文20余篇,其中,被SCI检索8篇、EI检索6篇。

张岩,女,1972年5月生,辽宁省本溪县人,博士,牡丹江师范学院计算机科学与技术系副教授。研究方向为多路径覆盖测试数据进化生成理论与方法。主持黑龙江省教育厅项目1项、牡丹江师范学院青年学术骨干资助项目1项,参与国家自然科学基金项目1项、黑龙江省教育厅项目1项、江苏省自然科学基金项目2项。近年来,发表学术论文20余篇,其中,被SCI检索3篇、EI检索6篇。
Catalogue

《智能科学技术著作丛书》序
前言

第1章 测试数据进化生成入门
1.1 软件测试基本概念
1.1.1 软件测试的定义
1.1.2 软件测试的目的
1.1.3 软件测试的方法
1.1.4 软件测试的原则
1.1.5 软件测试的分类
1.2 基于覆盖的测试数据生成方法
1.2.1 测试充分性准则
1.2.2 测试数据生成方法
1.3 测试数据进化生成
1.3.1 问题的转化
1.3.2 遗传算法基本原理
1.3.3 基于遗传算法的测试数据生成
1.4 研究现状及存在的问题
1.4.1 测试数据进化生成研究现状
1.4.2 存在的问题
1.5 主要内容及组织
1.6 本章小结
参考文献

第2章 不可执行路径自动检测
2.1 研究动机
2.2 条件语句之间的相关性
2.2.1 条件语句相关性的定义
2.2.2 条件语句相关性的判定
2.3 条件语句谓词表达式取值概率的估计
2.4 不可执行路径的自动检测
2.4.1 不可执行路径的检测
2.4.2 循环体包含条件语句的不可执行路径检测
2.4.3 不可执行路径自动检测的步骤
2.5 实例分析
2.6 在程序测试中的应用
2.6.1 基准程序实验
2.6.2 工业程序实验
2.7 本章小结
参考文献

第3章 多路径覆盖测试数据进化生成
3.1 研究动机
3.2 测试数据生成问题的数学模型
3.3 基于遗传算法的测试数据生成
3.3.1 进化个体适应值计算
3.3.2 进化个体比较
3.3.3 算法终止条件
3.3.4 算法步骤
3.4 在基准程序测试中的应用
3.4.1 实验设计
3.4.2 参数设置
3.4.3 性能指标
3.4.4 实验结果与分析
3.5 本章小结
参考文献

第4章 路径赫夫曼编码表示的多路径覆盖测试数据进化生成
4.1 研究动机
4.2 路径的赫夫曼编码表示
4.3 路径匹配度计算
4.3.1 程序插装
4.3.2 路径匹配度计算
4.3.3 测试数据生成问题的数学模型
4.4 适应度函数设计
4.4.1 进化个体适应值计算
4.4.2 进化个体适应值计算特点
4.4.3 算法步骤
4.5 在基准程序测试中的应用
……
第5章 基于信息共享的多路径覆盖测试数据进化生成
第6章 基于路径分组的很多路径覆盖测试数据进化生成
第7章 很多路径覆盖测试数据进化生成的并行实现
第8章 用于路径覆盖和缺陷检测的测试数据进化生成
第9章 基于搜索空间缩减的路径覆盖测试数据进化生成
第10章 基于适应值调整的路径覆盖测试数据进化生成
第11章 基于优质初始种群的路径覆盖测试数据进化生成
第12章 基于占优关系的语句覆盖测试数据进化生成
第13章 基于占优关系的多语句覆盖测试数据进化生成
第14章 基于路径覆盖的变异测试数据进化生成
附录1 基本遗传算法源代码
附录2 实验程序
Book Abstract

第6章 基于路径分组的很多路径覆盖测试数据进化生成
到目前为止,本书研究的均为同时覆盖多条目标路径的测试数据生成问题。尽管提出的方法能够高效生成覆盖多条目标路径的测试数据,但当目标路径很多时,采用上述方法生成测试数据的效率却很低。原因在于目标路径的增多使得建立的问题的数学模型,要么包含很多目标函数,要么为很多个单目标优化模型。目标函数增多,采用第3章的方法得到的进化个体的适应值向量包含的元素也增多,从而提高了进化个体比较的难度;采用第4章的方法,需要针对所有未被覆盖的目标路径计算每个个体的适应值,大大增加了算法的计算量;采用第5章的方法,需要并行进化的子种群也增多,从而增大了信息共享的复杂度。总之,上述方法难以有效求解覆盖很多目标路径的测试数据生成问题。如果采用合适的方法,将目标路径分组,使得每组包含较少的目标路径,并采用已有的方法,基于每组路径,形成测试数据生成问题的数学模型之后,采用遗传算法生成期望的测试数据,则将能够有效解决很多路径覆盖测试数据生成问题。鉴于此,本章研究很多路径覆盖测试数据生成问题,并提出基于路径分组的测试数据进化生成方法。首先,基于路径相似度将目标路径分成若干组,使得每组包含相对较少的目标路径,这样一来,就把一个大的复杂优化问题分解为多个相对简单的子优化问题;然后,对于每个子优化问题,采用一个子种群进化求解,并在求解过程不断再造、简化需要求解的子优化问题。性能分析表明,采用本章方法能够大大提高期望测试数据生成的概率。此外,5个基准和工业程序测试的结果表明,本章方法能够显著提高测试数据生成的效率。本章主要内容来自文献。
本章内容安排如下:6.1节引出本章研究的动机;6.2节给出基于相似度的路径分组方法;6.3节建立基于路径分组的很多路径覆盖测试数据生成问题的数学模型;6.4节提出通过模型的进化求解生成测试数据的方法;6.5节是本章方法的性能分析;6.6节详细阐述所提方法在基准和工业程序测试中的应用与对比实验及其分析;最后,6.7节全面总结本章取得的成果。
……

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