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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Context-Based Analytics - Establishing Explicit Links between Runtime Traces and Source Code
Organization Unit
Authors
  • Jürgen Cito
  • Fábio Oliveira
  • Philipp Leitner
  • Priya Nagpurkar
  • Harald Gall
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published electronically before print/final form (Epub ahead of print)
Language
  • English
Event Title International Conference on Software Engineering (ICSE)
Event Type conference
Event Location Buenos Aires, Argentina
Event Start Date May 20 - 2017
Event End Date May 28 - 2017
Place of Publication New York
Abstract Text Diagnosing problems in large-scale, distributed applications running in cloud environments requires investigating different sources of information to reason about application state at any given time. Typical sources of information available to developers and operators include log statements and other runtime information collected by monitors, such as application and system metrics. Just as importantly, developers rely on information related to changes to the source code and configuration files (program code) when troubleshooting. This information is generally scattered, and it is up to the troubleshooter to inspect multiple implicitly-connected fragments thereof. Currently, different tools need to be used in conjunction, e.g., log aggregation tools, source-code management tools, and runtime-metric dashboards, each requiring different data sources and workflows. Not surprisingly, diagnosing problems is a difficult proposition. In this paper, we propose Context-Based Analytics, an approach that makes the links between runtime information and program-code fragments explicit by constructing a graph based on an application-context model. Implicit connections between information fragments are explicitly represented as edges in the graph. We designed a framework for expressing application-context models and implemented a prototype. Further, we instantiated our prototype framework with an application-context model for two real cloud applications, one from IBM and another from a major telecommunications provider. We applied context-based analytics to diagnose two issues taken from the issue tracker of the IBM application and found that our approach reduced the effort of diagnosing these issues. In particular, context-based analytics decreased the number of required analysis steps by 48% and the number of needed inspected traces by 40% on average as compared to a standard diagnosis approach.
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