SHF: Small: Automated Debugging Techniques for Modern Software Systems

SHF：小型：现代软件系统的自动调试技术

• 批准号: 0916605
• 负责人: Alessandro Orso
• 金额: $ 44.52万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-15 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916605&HistoricalAwards=false
• 关键词: SHF; Small; Automated; Debugging; Techniques

Proposal Number: CCF 0916605TITLE: SHF: Small: Automated Debugging Techniques for Modern Software SystemsPI: Alessandro OrsoDebugging, which consists of identifying and removing software faults, is a human-intensive activity responsible for much of the cost of software maintenance. Existing approaches for automated debugging can help lower this cost, but have limitations that hinder their effectiveness and applicability. This project aims to overcome these limitations by developing a family of debugging techniques that (1) target realistic debugging scenarios, in which faults can involve multiple statements and manifest themselves only in specific contexts,(2) apply advanced static and dynamic analysis techniques to automatically reduce the amount of both statements and inputs that developers must examine when investigating a failure, (3) leverage information collected from the field to increase the relevance and effectiveness of the debugging process. These newly defined techniques will be evaluated through rigorous experimentation performed on real software, in real settings, and under realistic assumptions. Debugging tools, infrastructure, and experimental subjects developed within the project will be made freely available to researchers and practitioners to help dissemination and enable further research. By advancing the state of the art of debugging, this research will help developers build more reliable software systems and, ultimately, increase the overall quality of our software infrastructure.

提案编号：CCF 0916605TITLE：SHF：Small：现代软件系统的自动化调试技术PI：Alessandro Orso调试，由识别和排除软件故障组成，是一项人工密集型活动，负责大部分软件维护成本。现有的自动化调试方法可以帮助降低这一成本，但存在阻碍其有效性和适用性的限制。本项目旨在通过开发一系列调试技术来克服这些限制：(1)针对实际调试场景，其中错误可能涉及多个语句，并且仅在特定上下文中显现；(2)应用高级静态和动态分析技术来自动减少开发人员在调查故障时必须检查的语句和输入的数量；(3)利用从现场收集的信息来提高调试过程的相关性和有效性。这些新定义的技术将通过在真实软件、真实环境和现实假设下执行的严格实验进行评估。在该项目中开发的调试工具、基础设施和实验对象将免费提供给研究人员和从业者，以帮助传播和支持进一步的研究。通过提高调试的技术水平，这项研究将帮助开发人员构建更可靠的软件系统，并最终提高我们软件基础设施的整体质量。