Collaborative Research: Enhancing the Reliability of Bioinformatics Software by Correlating User Feedback and Execution Data

通过关联用户反馈和执行数据来提高生物信息学软件的可靠性：Andy Podgurski和Wassim masa被忽视的科学数据集和互联网计算服务激增的后果是，科学界越来越依赖于共享数据集的质量和用于分析它们的软件的可靠性。为了使生物信息学软件的开发人员更容易确保其可靠性，本研究旨在开发、评估和改进自动化技术，以帮助开发人员发现部署软件的紧急可靠性问题，了解其性质和意义，并诊断其原因。该方法基于从用户那里获取关于观察到的问题症状的结构化反馈，然后将这些反馈与有关内部程序动态和输入-输出映射的更详细的信息自动关联起来。先进的数据挖掘技术将与动态程序依赖分析相结合，以汇集来自不同用户的问题报告；核实个人报告；根据故障的症状和原因进行分组；并帮助诊断故障的原因。这项提议的工作有可能显著提高成千上万科学家使用的生物信息学应用程序的可靠性。

Proposals 0820217/0819987Enhancing the Reliability of Bioinformatics Software by Correlating User Feedback and Execution DataPIs: Andy Podgurski and Wassim MasriA neglected consequence of the proliferation of scientific data sets and computational services on the Internet is that the scientific community is becoming increasingly dependent on the quality of shared data sets and the reliability of the software used to analyze them. To make it easier for developers of bioinformatics software to ensure its reliability, this research seeks to develop, evaluate, and refine automated techniques to help developers discover emergent reliability problems with deployed software, understand their nature and significance, and diagnose their causes. The approach is based on eliciting structured feedback from users about the problem symptoms observed and then automatically correlating this feedback with far more detailed information about internal program dynamics and input-output mappings. Advanced data mining techniques will be employed in tandem with dynamic program dependence analysis to: pool problem reports from different users; corroborate individual reports; group failures according to their symptoms and causes; and help diagnose the causes of failures. The proposed work has the potential to significantly improve the reliability of bioinformatics applications used by thousands of scientists.