SHF: Small: RUI: Generating High Quality Trace Links through Intelligent Composition of Tracing Features

SHF：小：RUI：通过跟踪特征的智能组合生成高质量的跟踪链接

• 批准号: 1319680
• 负责人: Jane Huang
• 金额: $ 49.92万
• 依托单位: DePaul University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319680&HistoricalAwards=false
• 关键词: SHF; Small; RUI; Generating; Quality

Software traceability serves a critical role in ensuring that software systems operate correctly. It is used to support a wide variety of software engineering activities such as change management, compliance verification, and safety analysis. Unfortunately current practices fall far short of delivering cost-effective traceability, primarily because creating and managing trace links in large and/or complex systems is time-consuming, arduous, and error-prone. These problems were highlighted in a recent report entitled 'Critical Code: Software Producibility for Defense' commissioned by the Department of Defense. The report stressed the need for the research community to develop cost-effective and accurate traceability solutions. While state of the art tracing techniques offer significant promise for reducing the cost and effort of tracing, they fall short of meeting industrial needs primarily because the quality of the generated links is imprecise. This work will investigate ways to integrate techniques from feature modeling, product line development, artificial intelligence and machine learning to deliver a dynamically configurable trace infrastructure. The framework will then be used to investigate and integrate a broad set of novel tracing techniques which are expected to significantly improve the quality of generated trace links.The results of the project will contribute towards the development of software intensive systems, especially safety-critical ones in which traceability is mandatory. Technology transfer will be facilitated by delivering solutions on the TraceLab platform, disseminated via the Center of Excellence for Software Traceability (CoEST.org) and through training materials targeted at industrial users. Ongoing research opportunities will be provided for a diverse group of undergraduate and graduate students, and pedagogical materials will be developed and made publicly available for use in a variety of courses on requirements engineering, software engineering and software architecture.

软件可追溯性在确保软件系统正确运行方面起着关键作用。它用于支持各种各样的软件工程活动，如变更管理、合规性验证和安全性分析。不幸的是，目前的实践远远不能提供经济有效的可跟踪性，主要是因为在大型和/或复杂的系统中创建和管理跟踪链接是耗时、费力和容易出错的。国防部最近委托编写的一份题为“关键代码：国防软件可生产性”的报告强调了这些问题。该报告强调，研究界需要开发具有成本效益和准确的可追溯性解决方案。 虽然最先进的追踪技术提供了减少追踪成本和工作量的重大承诺，但它们不能满足工业需求，主要是因为所生成的链接的质量不精确。这项工作将研究如何集成功能建模，产品线开发，人工智能和机器学习技术，以提供一个动态可配置的跟踪基础设施。 然后，该框架将被用来调查和整合广泛的一套新的跟踪技术，预计将显着提高质量生成的跟踪links.The项目的结果将有助于对软件密集型系统的开发，特别是安全关键的可追溯性是强制性的。将通过在TraceLab平台上提供解决方案，通过软件可追溯性卓越中心（CoEST.org）和针对工业用户的培训材料传播，促进技术转让。将为各类本科生和研究生提供持续的研究机会，并将编写教学材料，供需求工程、软件工程和软件架构等各种课程使用。