Advancing Logging Practices in Software Engineering

尽管软件开发取得了进步，但软件系统的复杂性日益增加，使得生产无故障软件变得困难。残留故障通常在系统执行过程中出现，导致严重的软件故障、安全漏洞和性能下降。为了诊断故障的根本原因，软件开发人员和系统管理员经常求助于对系统执行期间生成的日志进行分析。像Ericsson、谷歌和Microsoft这样的大公司收集日志来分析、修复和防止故障。像AlienVault和ArcSight这样的安全提供商也使用日志来检测安全威胁和恶意行为。尽管日志记录长期以来一直被认为是软件开发的一个重要方面，但它仍然主要是一种特殊的实践。没有日志记录的标准、指导方针或公认的最佳实践。此外，软件开发人员继续在没有自动指导的情况下将日志插入系统。缺乏系统和自动化的日志记录方法对日志的质量产生了巨大的影响，常常阻碍了可行的分析。由于日志数据的庞大数量和可变性，日志分析变得更加复杂。其直接后果是，许多故障诊断任务非常具有挑战性，实施起来也非常昂贵，这将危及社会在卫生、电信、公共安全和国防等领域所依赖的关键系统的可靠性和安全性。这项研究计划的最终目标是促进软件工程中日志记录的实践。该领域的许多现有研究工作都致力于在日志生成后对其进行分析。对伐木实践的研究少得多。未来五年，我们将重点关注四个重点研究方向。首先，我们将通过研究该领域的当前实践，确定系统的方法和最佳的测井实践。其次，我们将开发一个上下文感知的推荐系统，自动向开发人员提供日志建议。在第三个项目中，我们将通过降低日志的复杂性来简化日志的分析。最后，我们将生成基于系统设计和体系结构支持更好的日志记录决策的技术。这项研究计划的结果有望对软件行业产生实际影响。改进软件开发人员的日志记录实践将产生卓越的故障诊断技术，同时降低相关成本。这项研究的另一个重要影响是它的商业化潜力，这是由工业对物联网、云计算和安全等领域更好的日志工具的强烈需求驱动的。此外，拟议的计划将培训几名hqp，他们将为加拿大信息通信技术部门的发展做出贡献，这是加拿大在世界经济中的竞争力的重要推动者。

Despite the advances in software development, the increasing complexity of software systems makes it difficult to produce fault free software. The residual faults often manifest themselves during system execution causing severe software failures, security breaches, and performance degradation. To diagnose the root causes of failures, software developers and system administrators frequently resort to the analysis of logs, generated during system executions. Major companies like Ericsson, Google, and Microsoft collect logs to analyze, fix, and prevent failures. Logs are also used by security providers like AlienVault and ArcSight to detect security threats and malicious behaviors.Although logging has long been recognized as an important aspect of software development, it remains predominantly an ad hoc practice. There are no standards, guidelines, or recognized best practices for logging. In addition, software developers continue to insert logs into a system without automated guidance. The lack of systematic and automated approaches for logging has a tremendous impact on the quality of logs, often hindering viable analysis. Log analysis is further complicated by the sheer volume and variability of log data. As a direct consequence, many failure diagnosis tasks are very challenging and expensive to implement, putting at risk the reliability and security of critical systems on which society depends in areas like health, telecom, public safety, and defence.The ultimate goal of this research program is to advance the practice of logging in software engineering. Much of the existing research effort in the field is devoted to the analysis of logs after they are generated. Considerably less attention is devoted to the study of the practice of logging. Over the next five years, we will focus on four priority research directions. First, we will identify systematic approaches and best practices for logging by studying the current practices in the field. Second, we will develop a context-aware recommendation system to automatically provide logging suggestions to developers. In the third project, we will facilitate the analysis of logs by reducing their complexity. Finally, we will produce techniques to support better logging decisions based on system design and architecture.The outcomes of this research program are expected to have a real impact on the software industry. Improving logging practices of software developers will result in superior failure diagnosis techniques while reducing the associated costs. Another important impact of this research is its potential for commercialization driven by the strong need from industry to have better logging tools in areas like IoT, cloud computing, and security. Furthermore, the proposed program will train several HQPs who will contribute to the growth of Canada's ICT sectoran important enabler to Canada's competitiveness in the world economy.