A declarative framework for stateful analysis of execution traces

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• 作者：Florian Wininger ; Naser Ezzati-Jivan ; Michel R. Dagenais
• 关键词：Software debugging ; Declarative debugging ; Execution trace analysis
• 刊名：Software Quality Journal
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：25
• 期：1
• 页码：201-229
• 全文大小：
• 刊物主题：Software Engineering/Programming and Operating Systems; Programming Languages, Compilers, Interpreters; Data Structures, Cryptology and Information Theory; Operating Systems;
• 出版者：Springer US
• ISSN：1573-1367
• 卷排序：25

文摘

With newer complex multi-core systems, it is important to understand an application’s runtime behavior to be able to debug its execution, detect possible problems and bottlenecks and finally identify potential root causes. Execution traces usually contain precise data about an application execution. Their analysis and abstraction at multiple levels can provide valuable information and insights about an application’s runtime behavior. However, with multiple abstraction levels, it becomes increasingly difficult to find the exact location of detected performance or security problems. Tracing tools provide various analysis views to help users to understand their application problems. However, these pre-defined views are often not sufficient to reveal all analysis aspects of the underlying application. A declarative approach that enables users to specify and build their own custom analysis and views based on their knowledge, requirements and problems can be more useful and effective. In this paper, we propose a generic declarative trace analysis framework to analyze, comprehend and visualize execution traces. This enhanced framework builds custom analyses based on a specified modeled state, extracted from a system execution trace and stored in a special purpose database. The proposed solution enables users to first define their different analysis models based on their application and requirements, then visualize these models in many alternate representations (Gantt chart, XY chart, etc.), and finally filter the data to get some highlights or detect some potential patterns. Several sample applications with different operating systems are shown, using trace events gathered from Linux and Windows, at the kernel and user-space levels.