Towards a Megamodel to Model Software Evolution Through Transformations

Model Driven Engineering is a promizing approach that could lead to the emergence of a new paradigm for software evolution, namely Model Driven Software Evolution. Models, Metamodels and Transformations are the cornerstones of this approach. Combining these concepts leads to very complex structures which revealed to be very difficult to understand especially when different technological spaces are considered such as XMLWare (the technology based on XML), Grammarware and BNF, Modelware and UML, Dataware and SQL, etc. The concepts of model, metamodel and transformation are usually ill-defined in industrial standards like the MDA or XML. This paper provides a conceptual framework, called a megamodel, that aims at modelling large-scale software evolution processes. Such processes are modeled as graphs of systems linked with well-defined set of relations such as RepresentationOf (μ), ConformsTo (χ) and IsTransformedIn (τ).     

基于软件模式的PIM到PSM的模型变换

模型驱动体系结构(MDA)是一种以模型为中心的新的软件开发模式。MDA的基本思想是将模型主要分为平台无关模型(PIM)和平台相关模型(PSM),然后通过变换规则实现PIM到PSM的变换。文中将软件模式的概念引入到PIM到PSM模型变换中,从而提高模型变换效率和降低模型变换出错率。同时,设计了基于软件模式的PIM到PSM的模型变换方法的框架。     

元模型支持下的模型转换

模型转换是MDA的核心思想,包括模型到代码的转换和模型到模型的转换。文章所提出的模型转换方法用元模型表示转换规则,通过基于图转换的模型转换,最终得到目标模型的元模型表达。元模型支持下的模型转换可以对转换规则进行精确的描述,使转换具有明确的语义,更便于工具实现。     

Automating Support for Software Evolution in UML

Disciplined support for evolution of software artifacts is important in all phases of the software life-cycle. In order to achieve this support, a uniform underlying foundation for software evolution is necessary. While, in the past, reuse contracts have been proposed as such a formalism in a number of different domains, this paper generalises the formalism, and integrates it into the UML metamodel. As such, support for evolution becomes readily available for many kinds of UML models, ranging from requirements to the implementation phase.     

基于SPEM的CMM软件过程元模型

软件企业在实施CMM(capability maturity modelfor software)的过程中面临最主要的障碍是如何将CMM 软件过程模型转换成可实施的、体现组织过程特征的CMM实施过程模型.可以利用模型驱动架构MDA来支持CMM模型转换,其首要问题是建立CMM软件过程元模型.通过分析CMM软件过程,给出了面向CMM的软件过程工程元模型SPEM的扩展策略,提出了一个基于SPEM的CMM软件过程元模型一-SPM-CMM.该元模型既支持CMM软件过程的抽象语法和规则语义,也支持利用UML CASE工具操作CMM软件过程模型.     

软件进化研究综述

软件进化是软件产品交付给客户之后所发生的一系列改进活动,是有目的地从早期版本来产生新版本的过程,是软件工程中的一个重要领域。对软件进化进行了综述性的介绍,讨论了其定义、发展历程及主要研究内容,介绍了指导软件进化的几种典型方法,分析了人们在软件进化过程中面临的一些挑战和问题。     

Modeling Software Evolution by Treating History as a First Class Entity

The histories of software systems hold useful information when reasoning about the systems at hand or about general laws of software evolution. Yet, the approaches developed so far do not rely on an explicit meta-model and do not facilitate the comparison of different evolutions. We argue for the need to define history as a first class entity and propose a meta-model centered around the notion of history. We show the usefulness of our a meta-model by discussing the different analysis it enables.     

基于细粒度结构演化的软件转变

如何对既有的软件系统进行软件结构变化，指导软件转变，达到优化质量属性的目的，是目前软件结构研究的一个重要课题．本文从宏观的角度，提出一种细粒度软件结构演化方法(Fine—Grained Software Architectural Evolution，FGSAE)，给出它的具体步骤(依FGSAE原则，重新设计软件结构；依结构对应关系，进行因子调整)，指导软件转变．     

软件服务的在线演化

软件服务的在线演化技术是当前可信软件研究的一个重要方向,对于实现快速、低成本的成长式可信演化具有重要意义.与离线的演化技术相比,在线演化强调软件系统在结构修改和功能调整期间仍能够持续提供服务.文中在给出软件服务在线演化基本定义、归纳其结构模型和一般性过程模型基础上,提出涵盖演化范畴、演化类型和演化方式等方面的分类模型,...     

Using a Reliability Growth Model to Control Software Inspection

After a software inspection the project manager has to decide whether he can pass a product on to the next software development stage or whether it still contains a substantial number of defects and should be reinspected to further improve its quality. While a substantial number of defects remaining in a product after inspection is a reasonable precondition to schedule a reinspection, it is also important to estimate whether the likely number of defects to be found with a reinspection will lower the defect density under the target threshold. In this work we propose a reliability growth model and two heuristic linear models for software inspection, which estimate the likely number of additional defects to be found during reinspection. We evaluate the accuracy of these models with time-stamped defect data from a large-scale controlled inspection experiment on reinspection. Main findings are: (a) The two best models estimated the defect detection capability for reinspection with good accuracy: over 80% of the estimates had an absolute relative error of under 10%; (b) The reinspection decision correctness based on the estimates of all investigated models, overall around 80% correct decisions, was much better than the trivial models to always or never reinspect; the latter is the default decision in practice.