软件质量测评模型与测试数据生成方法研究
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摘要
软件产业是经济社会发展的基础性、战略性和先导性产业,在推动信息化与工业化融合、促进产业结构调整、转变经济发展方式、维护国家安全等方面发挥着重要作用。软件质量相关内容的研究,对于提高软件产品质量,促进软件产业健康有序发展具有十分重要的战略意义和现实意义。软件质量测评模型与测试数据自动生成方法的研究属于软件质量相关研究的关键技术问题,已经成软件工程领域的研究热点,具有重大的应用价值和理论意义。
在分析国内外软件行业及现状的基础上,通过国内外软件质量相关技术现状的研究,确定了研究的出发点,提出的研究方法和技术路线。论文从软件质量的内涵及其属性入手,介绍了软件度量方法,并在此基础上对典型的软件质量模型进行了分析和讨论。围绕软件质量关键性技术问题,探讨解决软件质量测评模型建立中的关键技术和基于智能优化理论的测试数据自动生成方法,主要研究内容包括:第一,在研究测试数据生成原理与方法的基础上,提出了一种基于遗传-粒子群混合算法的测试数据自动生成方法。利用粒子群算法中的个体更新方式代替传统遗传算法中的变异操作,通过种群“产优率”来影响个体更新速度。该方法能够保持种群多样性,具有较强的全局寻优能力,具有很好的测试数据生成效率。第二,在研究软件质量预测原理与方法的基础上,提出了基于粒子群-BP网络的软件质量预测模型,并进行了软件质量的可靠性预测实验验证与分析。该模型能够迅速地预测软件的质量,可以避免局部最值的出现,克服了参数凭专家经验而确定的不足,可以正确地反映软件内部属性和外部属性之间的关系。第三,在研究软件质量评价原理与方法的基础上,充分考虑了用户满意度对软件质量的影响,构建了基于非对称模糊三角数模糊回归的软件质量评价模型,探讨了在软件评价中如何将用户的需求与软件特性的关系定量的表达出来。
The software industry is the fundamental, strategic and leading industry of the economic and social development. It plays an important role in promoting the convergence of information and industrialization, encouraging the industrial structure adjustment, transforming the mode of economic development and maintaining the state security. The research of software quality has promoted strategic and realistic significance in enhancing the quality of software products and in promoting the healthy and orderly development of the software industry. The modeling software quality prediction and evaluation and the method of automatic test data generation belong to the key technology of the related research of software quality, and become research focus in field of software engineering, which is provided with great theoretical significance and application value.
The paper has set the study goals, research methods and technology roadmap based on the analyses of software quality in software industry and software quality-related technologies both domestically and abroad. Then the connotations and properties of software quality and software metric methods have been introduced, the typical software quality models have been discussed. This paper has carried out more in-depth research on the key technical issue related to software quality and discussed the key technologies in modeling software quality prediction and evaluation and the method of automatic test data generation based on intelligent optimization theory. The main contents of this paper are as follows:
Firstly, On the basis of studying the principles and methods on generating test data, the method about automatic test data generation based on genetic-particle swarm hybrid algorithm has been proposed. The proposed method has replaced the style in genetic algorithm with the individual update method of particle swarm optimization, possessing both the merit of genetic algorithm on strong global searching and particle swarm optimization on strong local searching ability. The experimental results have shown that this method can keep the group diversity, avoid the premature convergence in evolution and have strong ability of global optimization and well efficiency of test data generation.
Secondly, Prediction models are often used to find the nonlinear relationship between metric data and quality factors, on the basis of studying software quality prediction principles and methods, software quality prediction model base on PSO-BP network has been raised. It has indicated that the proposed model can predict software quality quickly and avoid the emergence of local best value and overcome the lack of parameter evaluated by the expertise, and can accurately reflect the relationship of the internal and external properties of software quality.
Thirdly, on the basis of studying software quality evaluation principles and methods, the software quality evaluation model based on fuzzy regression with asymmetric triangular fuzzy numbers has been put forward, which has taken fully into account the impact of customer satisfaction. This model has investigated software quality evaluation by modeling the relationship between the customer requirements and software features so as to produce high-quality software.
在分析国内外软件行业及现状的基础上,通过国内外软件质量相关技术现状的研究,确定了研究的出发点,提出的研究方法和技术路线。论文从软件质量的内涵及其属性入手,介绍了软件度量方法,并在此基础上对典型的软件质量模型进行了分析和讨论。围绕软件质量关键性技术问题,探讨解决软件质量测评模型建立中的关键技术和基于智能优化理论的测试数据自动生成方法,主要研究内容包括:第一,在研究测试数据生成原理与方法的基础上,提出了一种基于遗传-粒子群混合算法的测试数据自动生成方法。利用粒子群算法中的个体更新方式代替传统遗传算法中的变异操作,通过种群“产优率”来影响个体更新速度。该方法能够保持种群多样性,具有较强的全局寻优能力,具有很好的测试数据生成效率。第二,在研究软件质量预测原理与方法的基础上,提出了基于粒子群-BP网络的软件质量预测模型,并进行了软件质量的可靠性预测实验验证与分析。该模型能够迅速地预测软件的质量,可以避免局部最值的出现,克服了参数凭专家经验而确定的不足,可以正确地反映软件内部属性和外部属性之间的关系。第三,在研究软件质量评价原理与方法的基础上,充分考虑了用户满意度对软件质量的影响,构建了基于非对称模糊三角数模糊回归的软件质量评价模型,探讨了在软件评价中如何将用户的需求与软件特性的关系定量的表达出来。
The software industry is the fundamental, strategic and leading industry of the economic and social development. It plays an important role in promoting the convergence of information and industrialization, encouraging the industrial structure adjustment, transforming the mode of economic development and maintaining the state security. The research of software quality has promoted strategic and realistic significance in enhancing the quality of software products and in promoting the healthy and orderly development of the software industry. The modeling software quality prediction and evaluation and the method of automatic test data generation belong to the key technology of the related research of software quality, and become research focus in field of software engineering, which is provided with great theoretical significance and application value.
The paper has set the study goals, research methods and technology roadmap based on the analyses of software quality in software industry and software quality-related technologies both domestically and abroad. Then the connotations and properties of software quality and software metric methods have been introduced, the typical software quality models have been discussed. This paper has carried out more in-depth research on the key technical issue related to software quality and discussed the key technologies in modeling software quality prediction and evaluation and the method of automatic test data generation based on intelligent optimization theory. The main contents of this paper are as follows:
Firstly, On the basis of studying the principles and methods on generating test data, the method about automatic test data generation based on genetic-particle swarm hybrid algorithm has been proposed. The proposed method has replaced the style in genetic algorithm with the individual update method of particle swarm optimization, possessing both the merit of genetic algorithm on strong global searching and particle swarm optimization on strong local searching ability. The experimental results have shown that this method can keep the group diversity, avoid the premature convergence in evolution and have strong ability of global optimization and well efficiency of test data generation.
Secondly, Prediction models are often used to find the nonlinear relationship between metric data and quality factors, on the basis of studying software quality prediction principles and methods, software quality prediction model base on PSO-BP network has been raised. It has indicated that the proposed model can predict software quality quickly and avoid the emergence of local best value and overcome the lack of parameter evaluated by the expertise, and can accurately reflect the relationship of the internal and external properties of software quality.
Thirdly, on the basis of studying software quality evaluation principles and methods, the software quality evaluation model based on fuzzy regression with asymmetric triangular fuzzy numbers has been put forward, which has taken fully into account the impact of customer satisfaction. This model has investigated software quality evaluation by modeling the relationship between the customer requirements and software features so as to produce high-quality software.
引文
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