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Combined static and dynamic analysis for inferring program dependencies using a pattern language
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Source IBM Centre for Advanced Studies Conference archive
Proceedings of the 2006 conference of the Center for Advanced Studies on Collaborative research table of contents
Toronto, Ontario, Canada
SESSION: Program comprehension table of contents
Article No. 3  
Year of Publication: 2006
Authors
Inbal Ronen  IBM Haifa Research Lab, Haifa, Israel
Nurit Dor  Panaya Inc., Raanana, Israel
Sara Porat  IBM Haifa Research Lab, Haifa, Israel
Yael Dubinsky  IBM Haifa Research Lab, Haifa, Israel
Sponsors
: IBM Toronto Lab
: CAS
Publisher
ACM  New York, NY, USA
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ABSTRACT

One of the challenges when examining enterprise applications is the ability to understand the dependencies of these applications on external and internal resources such as database access or transaction activation. Inferring dependencies can be achieved using a static approach, a dynamic one or a combination of the two. Static analysis tools detect dependencies based on code investigation while dynamic tools detect dependencies based on runtime execution. The combination of these two approaches is essential for a complete and precise analysis. In this paper we present and illustrate a technique for inferring application dependencies on resources. The technique is based on a combined dynamic and static analysis. A pattern language is defined to enable the specification of dependencies as sequences of method invocations in the application code. Specifically, the sequences are patterns that constitute access to resources, e.g. databases, message queues, and control systems. We propose an algorithm for inferring application dependencies based on hybrid dynamic and static analysis that propagates information provided by dynamic analysis into the static analysis and back to the dynamic analysis. Empirical results from our implemented prototype are presented.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Inbal Ronen: colleagues
Nurit Dor: colleagues
Sara Porat: colleagues
Yael Dubinsky: colleagues