基于本体的构件检索研究
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摘要
基于构件的软件开发方法是软件工业化生产的现实可行途径。基于构件的软件开发,不仅能有效地提高软件生产率,缩短软件产品上市时间,抢占有利的市场契机,还能使软件产品在客户满意度、软件质量上领先于同类产品,提高软件项目的成功率。构件技术已成为软件产业发展的关键技术之一,推行基于构件的软件开发是当前软件生产的世界潮流。
构件的描述和检索是充分发挥构件技术优势、实现软件复用、构筑高质量软件产品的关键技术之一。特别随着构件数量越来越多,其重要性越来越突出。学术界和产业界对此已有较多的研究,但目前的构件描述和检索方法在实践中的效果和人们的期望还有较大的差距,成为制约构件技术发展的瓶颈之一。
基于本体技术的构件描述和检索刚刚兴起,可望取得更好的效果,人们对此寄予比较大的期望,但是目前这方面的研究还很少。本文对此作了比较深入的探讨,首先在结合领域模型和特征领域模型的基础上提出构件的描述模型,并依此模型提出检索构件和推荐相关构件的方法。然后针对用户难以给出精确完整的检索请求的问题,提出辅助用户精化检索请求的方法。最后利用基于支配关系的粗糙集理论,提出一种新的构件检索方法,可进一步处理构件检索中存在的一些问题。
首先,基于本体特征领域模型提出构件的描述和检索方法。提出包含功能属性,非功能属性和质量属性的构件描述模型。在此构件模型和领域模型的基础上给出构件基于功能的索引和关联组织。然后提出构件检索方法和检索到一个构件后辅助用户查找相关构件的方法。实验验证了所作工作可获得较好的检索质量。
其次,针对检索请求经常出现的不完善,提出辅助完善方案。首先辅助用户补充表示功能动作的动词或操作对象的名词并调整检索请求中包含的功能动作;然后挖掘构件描述中项值之间的关联,辅助用户补充检索请求中缺失的项;最后在初步检索后根据检索请求和检索结果中构件描述的不同,给出检索请求进一步完善的方法。分析并实验验证了所提检索请求辅助完善方案可提高检索的质量。
第三,针对影响检索质量的一些问题,提出基于支配关系粗糙集理论的检索模型。把历史用户检索请求中的项看作条件属性(或称指标),反馈的构件评价等级看作决策属性,利用扩展的基于支配粗糙集理论学习规则知识,以后检索中,以规则为主,辅以相似度计算得到检索结果,可获得更好的检索结果。对所提检索方法的有效性作了分析和实验验证。
Component-based software development (CBSD) is an appealing approach for industry-scale software construction. CBSD promises not only improving software productivity, reducing the time-to-market and catching business opportunities, but also differentiate software manufacturer from others in terms of customer satisfactory and software quality, as well as making software projects successful. Component technology has become the cornerstone for software industry makes progress. It has become a worldwide trend to adopt CBSD in software application development process.
The description and retrieval of components is one of the key technologies to actualize the potentiality of component technology for software reuse and construct high quality software products. And it is become more and more important with the increase of the number of components. But current methods for the description and retrieval of components still leave much to hope and become one bottleneck in the development of the component technology.
Based on ontology technology, the description and retrieval of components can obtain more satisfied effect in experience and people place much hope on this. But the study of this is only beginning and there is much work to do and the paper does some attributes for this: a component description model is given based on combining ontology domain and feature domain model; a component retrieval algorithm and an algorithm to retrieve related component based on the description model; a method to assist the user to refine his retrieval requirement during the phased component retrieval; a component retrieval model based on Dominance-based Rough Sets Approach.
First, the paper gives a component description model based on combining domain ontology and feature domain model. The component description model contains function attributes, non-function attributes and QoS attributes. Then how to index and organize components based on the component description model and the ontology feature domain model is given. Based on those, component retrieval algorithm and an algorithm for retrieving related components to the components retrieved are introduced respectly. Experiment demonstrates that the work give good component retrieval result.
Second, a method to assist the user to perfect their retrieval requirement is intro- duced. First, system assists the user to complement verbs represented function action or nons represented business object and adjust the function action comtained in his retrieval requirement. Then through mining the relation between the item values of the retrieval requirement, the system assists the user to complement missing item of the retrieval requirement. Take the corresponding item values of the candidate components found thrugh component index as candidate valus, and return to the user to deal missing items or items with wrong value of the retrieval requirement. Experiment and analysis demonstrate the retrieval requirement refining assistence method can improve the retrieval result.
Third, to deal with some problems in component retrieval, a new component retrieval method is given based on Dominance-Based Rough Set Approach (DRSA). See the items in past component retrieval requirement as condition attributes (or Criterion), and the feedback component grades as decision attributes. The system learns rule knowledge based on DRSA. And in later component retrievals, the system obtains retrieval result based mainly on rule with the auxilirary similarity counting. Experiment and analysis demonstrate the the method can obtain better retrieval result.
构件的描述和检索是充分发挥构件技术优势、实现软件复用、构筑高质量软件产品的关键技术之一。特别随着构件数量越来越多,其重要性越来越突出。学术界和产业界对此已有较多的研究,但目前的构件描述和检索方法在实践中的效果和人们的期望还有较大的差距,成为制约构件技术发展的瓶颈之一。
基于本体技术的构件描述和检索刚刚兴起,可望取得更好的效果,人们对此寄予比较大的期望,但是目前这方面的研究还很少。本文对此作了比较深入的探讨,首先在结合领域模型和特征领域模型的基础上提出构件的描述模型,并依此模型提出检索构件和推荐相关构件的方法。然后针对用户难以给出精确完整的检索请求的问题,提出辅助用户精化检索请求的方法。最后利用基于支配关系的粗糙集理论,提出一种新的构件检索方法,可进一步处理构件检索中存在的一些问题。
首先,基于本体特征领域模型提出构件的描述和检索方法。提出包含功能属性,非功能属性和质量属性的构件描述模型。在此构件模型和领域模型的基础上给出构件基于功能的索引和关联组织。然后提出构件检索方法和检索到一个构件后辅助用户查找相关构件的方法。实验验证了所作工作可获得较好的检索质量。
其次,针对检索请求经常出现的不完善,提出辅助完善方案。首先辅助用户补充表示功能动作的动词或操作对象的名词并调整检索请求中包含的功能动作;然后挖掘构件描述中项值之间的关联,辅助用户补充检索请求中缺失的项;最后在初步检索后根据检索请求和检索结果中构件描述的不同,给出检索请求进一步完善的方法。分析并实验验证了所提检索请求辅助完善方案可提高检索的质量。
第三,针对影响检索质量的一些问题,提出基于支配关系粗糙集理论的检索模型。把历史用户检索请求中的项看作条件属性(或称指标),反馈的构件评价等级看作决策属性,利用扩展的基于支配粗糙集理论学习规则知识,以后检索中,以规则为主,辅以相似度计算得到检索结果,可获得更好的检索结果。对所提检索方法的有效性作了分析和实验验证。
Component-based software development (CBSD) is an appealing approach for industry-scale software construction. CBSD promises not only improving software productivity, reducing the time-to-market and catching business opportunities, but also differentiate software manufacturer from others in terms of customer satisfactory and software quality, as well as making software projects successful. Component technology has become the cornerstone for software industry makes progress. It has become a worldwide trend to adopt CBSD in software application development process.
The description and retrieval of components is one of the key technologies to actualize the potentiality of component technology for software reuse and construct high quality software products. And it is become more and more important with the increase of the number of components. But current methods for the description and retrieval of components still leave much to hope and become one bottleneck in the development of the component technology.
Based on ontology technology, the description and retrieval of components can obtain more satisfied effect in experience and people place much hope on this. But the study of this is only beginning and there is much work to do and the paper does some attributes for this: a component description model is given based on combining ontology domain and feature domain model; a component retrieval algorithm and an algorithm to retrieve related component based on the description model; a method to assist the user to refine his retrieval requirement during the phased component retrieval; a component retrieval model based on Dominance-based Rough Sets Approach.
First, the paper gives a component description model based on combining domain ontology and feature domain model. The component description model contains function attributes, non-function attributes and QoS attributes. Then how to index and organize components based on the component description model and the ontology feature domain model is given. Based on those, component retrieval algorithm and an algorithm for retrieving related components to the components retrieved are introduced respectly. Experiment demonstrates that the work give good component retrieval result.
Second, a method to assist the user to perfect their retrieval requirement is intro- duced. First, system assists the user to complement verbs represented function action or nons represented business object and adjust the function action comtained in his retrieval requirement. Then through mining the relation between the item values of the retrieval requirement, the system assists the user to complement missing item of the retrieval requirement. Take the corresponding item values of the candidate components found thrugh component index as candidate valus, and return to the user to deal missing items or items with wrong value of the retrieval requirement. Experiment and analysis demonstrate the retrieval requirement refining assistence method can improve the retrieval result.
Third, to deal with some problems in component retrieval, a new component retrieval method is given based on Dominance-Based Rough Set Approach (DRSA). See the items in past component retrieval requirement as condition attributes (or Criterion), and the feedback component grades as decision attributes. The system learns rule knowledge based on DRSA. And in later component retrievals, the system obtains retrieval result based mainly on rule with the auxilirary similarity counting. Experiment and analysis demonstrate the the method can obtain better retrieval result.
引文
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