Comparative performance analysis of calling context profiling data structures

Prita Yadav () and Paramvir Singh ()
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Prita Yadav: Dr. B.R. Ambedkar National Institute of Technology
Paramvir Singh: Dr. B.R. Ambedkar National Institute of Technology

International Journal of System Assurance Engineering and Management, 2017, vol. 8, issue 1, No 12, 135-150

Abstract: 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.

Keywords: Calling Context Tree; Context sensitivity; Dynamic metrics; Trace analysis; Program comprehension; Profiling (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s13198-016-0447-x

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