CSR: Small: Improving Software Diagnosability via Automatic Log Inferrence and Informative Logging

CSR：小：通过自动日志推断和信息记录提高软件可诊断性

• 批准号: 1017784
• 负责人: Yuanyuan Zhou
• 金额: $ 46.2万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017784&HistoricalAwards=false
• 关键词: CSR; Small; Improving; Software; Diagnosability

Many applications require high reliability and availability. Unfortunately, as software has grown in size and complexity, many software bugs escape from testing into production runs and cause computer failures in real world. When a production run system fails, software engineers are frequently called upon emergency to diagnose and solve the issue within a tight time schedule. Because such errors directly impact customers? business, vendors make diagnosing and fixing them as the highest priority. Since in many cases it is impossible to reproduce production-run failures in house due to various reasons (privacy, execution environments, etc.), the common practice is that customers send back the logs generated by the failed system. Such logs are the sole data source (in addition to source code) for software engineers to troubleshoot the occurred failure. Based on what are in the logs, they manually infer what may have happened to narrow down the root cause.Unfortunately, the above diagnosis process is mostly manual, very often a trial-and-error guess game and therefore is time-consuming, error-prone and also expensive in terms of both labor cost and system down time. Especially because log messages are added in an ad-hoc way, many of them do not provide precise, informative clues to help narrow down the root cause. Furthermore, the rapidly growing size and complexity as well as software aging has greatly affected modern software?s diagnosability.To enable developers to quickly troubleshoot production-rune failures and shorten system downtime, we propose automatic log inference and informative logging to make real-world software more diagnosable. We not only will investigate new diagnosis tools that can analyze logs and source code together to help software engineers narrowing down the possible root causes, but also will explore new ways to automatically enhance software logging to make log messages more effective and efficient for diagnosis. As software has been widely used in our daily life, software reliability is becoming an important issue. Our proposed solutions will allow software engineers to quickly identify root causes and patches to fix the problem, which would significantly reduce the amount of system down time. As such, it benefits both software/system vendors and computer users, especially those financial companies where an hour of down time can result in multiple millions of dollars loss in business.

许多应用程序要求高可靠性和可用性。不幸的是，随着软件在规模和复杂性上的增长，许多软件错误从测试中逃脱到生产运行中，并在现实世界中导致计算机故障。当生产运行系统发生故障时，软件工程师经常被紧急召唤来在紧凑的时间表内诊断和解决问题。因为这样的错误会直接影响客户吗？在商业上，供应商将诊断和修复它们作为最高优先事项。由于许多情况下，由于各种原因(隐私、执行环境等)，无法在内部重现生产运行故障，因此常见的做法是客户将故障系统生成的日志发回。这些日志是软件工程师排除已发生故障的唯一数据来源(除源代码之外)。根据日志中的内容，他们手动推断可能发生了什么，从而缩小了根本原因。遗憾的是，以上诊断过程大多是手动的，经常是反复尝试的猜测游戏，因此既耗时又容易出错，而且在人力成本和系统停机时间方面也很昂贵。特别是因为日志消息是以即席方式添加的，其中许多消息不能提供准确的、信息性的线索来帮助缩小根本原因的范围。此外，快速增长的规模和复杂性以及软件老化已经极大地影响了现代软件的可诊断性。为了使开发人员能够快速排除生产运行故障，缩短系统停机时间，我们提出了自动日志推理和信息日志记录，使现实世界的软件更具可诊断性。我们不仅将研究能够同时分析日志和源代码的新诊断工具，以帮助软件工程师缩小可能的根本原因，而且还将探索自动增强软件日志记录的新方法，以使日志消息更有效和高效地进行诊断。随着软件在日常生活中的广泛应用，软件可靠性成为一个重要的问题。我们建议的解决方案将允许软件工程师快速确定根本原因和修补程序来修复问题，这将显著减少系统停机时间。因此，它使软件/系统供应商和计算机用户都受益，尤其是那些停机一小时可能导致数百万美元业务损失的金融公司。