NDLI: Comparative performance analysis of calling context profiling data structures

Content Provider	Springer Nature Link
Author	Yadav, Prita Singh, Paramvir
Copyright Year	2016
Abstract	Calling context profiling has proved out to be extremely useful in program understanding and optimization. Calling contexts can be organized in several ways, such as Dynamic Call Tree (DCT), Calling Context Tree (CCT), Hot Calling Context Tree (HCCT), Approximate Calling Context Tree (ACCT) and Calling Context Uptree (CCU). This paper conducts a comparative performance analysis on all major calling context data structures using the evaluation of a set of dynamic cohesion and coupling metrics at package, class and method levels on a set of open source java applications. The results imply that DCT, CCT and CCU are all similar in metric accuracy but vary in time and space requirements. ACCT, while incurring least time and space overhead, generates highly inaccurate and misleading results. The performance of HCCT is highly dependent on a predetermined hot-context threshold value. It was concluded that CCT outperforms others in comprehensive representation of inter-procedural control flow and collection of dynamic metrics for a program, in terms of both time–space overhead and accuracy.
Starting Page	135
Ending Page	150
Page Count	16
File Format	PDF
ISSN	09756809
Journal	International Journal of System Assurance Engineering and Management
Volume Number	8
Issue Number	1
e-ISSN	09764348
Language	English
Publisher	Springer India
Publisher Date	2016-03-30
Publisher Place	New Delhi
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Calling Context Tree Context sensitivity Dynamic metrics Trace analysis Program comprehension Profiling Quality Control, Reliability, Safety and Risk Engineering Economics, Organization, Logistics, Marketing
Content Type	Text
Resource Type	Article
Subject	Strategy and Management Safety, Risk, Reliability and Quality

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