Fully Automated Software Logging

全自动软件记录

• 批准号: RGPIN-2018-04932
• 负责人: Yuan, Ding
• 金额: $ 2.48万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=685251
• 关键词: Fully; Automated; Software; Logging

This research addresses a fundamental problem in computer science. Programmers today spend 60% of their time in debugging; software industry spends $153 billion on failure diagnosis annually. When failures bring software down, especially systems software (e.g., operating system) that hosts all applications, no useful work can be produced. This research aims to speed-up the postmortem failure diagnosis process on systems software to minimize software's downtime. ******When software systems fail in production environments, log data is often the only information available to programmers for postmortem diagnosis. Programmers place log printing statements in software programs (e.g., using printf). At runtime, these statements output diagnostic information to log file. What makes these logs so valuable is their ubiquity and commercial acceptance. It is an industry-standard practice to request logs when a customer reports a failure and, since their data typically focuses narrowly on issues of system health, logs are generally considered far less sensitive than other data sources. Moreover, since logs are typically human readable, they can be inspected by a customer to establish their acceptability. Many software vendors even allow logs to be transmitted automatically and without review. ******Consequently, the quality of the log data is of critical importance to postmortem debugging. Indeed, if the log is uninformative, or even worse, misleading, postmortem debugging can end up being a wild goose chase. Unfortunately, little attention has been paid to the quality of log. While logging is pervasive it is every programmer's everyday task to decide where to log in the program -- there is very little guideline or established best practices on logging. Worse, programmers often ignore the quality of logging code when they are under time pressure of software release. As a result, it is frequently the case that when a failure occurs, the log does not contain any failure-related information, a situation referred colloquially as “debugging in the dark”.******This research proposes to fully automate software logging. It addresses the fundamental problem of where to place logging statements in the program. This research proposes a series of algorithms that are capable of automatically placing logging statements at optimal program locations. It has three specific objectives: (1) logging for failure diagnosis purpose, (2) logging for performance profiling, and (3) logging for security auditing. The key challenge is to measure how informative is logging statements being placed, and to trade-off informativeness with the performance cost of logging. The core idea is to use Information Theory to measure the entropy of the software, and calculate how different logging placement strategies can reduce this entropy.

这项研究解决了计算机科学中的一个基本问题。如今，程序员 60% 的时间都花在调试上；软件行业每年在故障诊断上花费 1530 亿美元。当故障导致软件瘫痪，尤其是承载所有应用程序的系统软件（例如操作系统）时，就无法产生有用的工作。这项研究旨在加快系统软件的事后故障诊断过程，以最大限度地减少软件的停机时间。 ******当软件系统在生产环境中出现故障时，日志数据通常是程序员可用于事后诊断的唯一信息。程序员将日志打印语句放置在软件程序中（例如，使用 printf）。在运行时，这些语句将诊断信息输出到日志文件。这些日志之所以如此有价值，是因为它们的普遍性和商业接受度。当客户报告故障时请求日志是一种行业标准做法，并且由于他们的数据通常只关注系统运行状况问题，因此日志通常被认为远不如其他数据源敏感。此外，由于日志通常是人类可读的，因此客户可以检查它们以确定其可接受性。许多软件供应商甚至允许日志自动传输且无需审核。 ******因此，日志数据的质量对于事后调试至关重要。事实上，如果日志没有提供任何信息，或者更糟糕的是，具有误导性，事后调试可能最终会变成一场徒劳的追逐。不幸的是，人们很少关注日志的质量。虽然日志记录很普遍，但决定在程序中登录的位置是每个程序员的日常任务——关于日志记录的指南或已建立的最佳实践很少。更糟糕的是，当程序员面临软件发布的时间压力时，他们常常忽视记录代码的质量。因此，经常出现的情况是，当发生故障时，日志不包含任何与故障相关的信息，这种情况通俗地称为“在黑暗中调试”。 *****本研究建议完全自动化软件日志记录。它解决了在程序中放置日志语句的基本问题。这项研究提出了一系列能够自动将日志语句放置在最佳程序位置的算法。它具有三个具体目标：(1) 用于故障诊断目的的日志记录，(2) 用于性能分析的日志记录，以及 (3) 用于安全审计的日志记录。关键的挑战是衡量日志语句的信息量，并在信息量与日志记录的性能成本之间进行权衡。其核心思想是利用信息论来测量软件的熵，并计算不同的日志放置策略如何降低这个熵。