首页> 外文会议> >Maintaining a COTS integrated solution-are traditional static analysis techniques sufficient for this new programming methodology?
【24h】

Maintaining a COTS integrated solution-are traditional static analysis techniques sufficient for this new programming methodology?

机译:维护COTS集成解决方案-传统的静态分析技术足以满足这种新的编程方法要求吗?

获取原文

摘要

As integrating commercial off-the-shelf (COTS) products into new homogeneous systems replaces "traditional" software development approaches, software maintenance problems persist. This approach builds new solutions via "glue code" using visual languages, which tie together client-based office products, server-based "BackOffice" products and web-based services/applications. The resulting collection of distributed object-oriented components are glued together by attaching code snippets written in a visual language to other components and controls, such as a command button on a form. A majority of the code in such an application is pre-generated and self-contained in the individual components being reused and, as a result, is typically difficult to understand and maintain. Our experience shows that, while these approaches actually exacerbate some maintenance problems, such as the introduction of dead code, traditional static analysis techniques may still facilitate common maintenance activities. This work reports on the use of data flow techniques on several medium-sized COTS integrated solutions that have become difficult to maintain. We found that by exploiting semantic information, traditional techniques can be augmented to handle some of the unique maintenance issues of component-based software.
机译:随着将商用现货(COTS)产品集成到新的同类系统中替代了“传统”软件开发方法,软件维护问题仍然存在。这种方法使用可视语言通过“胶水代码”构建新的解决方案,这些语言将基于客户端的办公产品,基于服务器的“ BackOffice”产品和基于Web的服务/应用程序结合在一起。通过将以可视语言编写的代码片段附加到其他组件和控件(例如表单上的命令按钮),可以将分布式的面向对象组件的结果集合粘合在一起。这样的应用程序中的大多数代码是在重新使用的各个组件中预先生成并包含的,因此通常很难理解和维护。我们的经验表明,尽管这些方法实际上加剧了某些维护问题,例如引入无效代码,但传统的静态分析技术仍可能促进常见的维护活动。这项工作报告了数据流技术在几种难以维护的中型COTS集成解决方案上的使用情况。我们发现通过利用语义信息,可以增强传统技术来处理基于组件的软件的某些独特维护问题。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号