Modeling Runtime Behavior in Framework-Based Applications

Mitchell, N.; Sevitsky, Gary; Srinivasan, Harini

doi:10.1007/11785477_25

Cited by 53 publications

(46 citation statements)

References 12 publications

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“…Function recalculateIndex is shown in lines 35-46. Next, we add this incoming object x into the active container Γ i (lines [4][5][6][7][8][9][10][11][12][13][14][15]. If the element at newIndex is an abstract element (line 6), this abstraction is split into two separate abstractions (line 9) and then x is inserted between them (line 10).…”

Section: List Combomentioning

confidence: 99%

“…Note that once an abstraction a is encountered during a retrieval, we concretize the abstraction and move the entire range of concrete elements into the active container (lines [5][6][7][8]. This is conceptually similar to a cache line fill.…”

Section: Line 21) Otherwise We Have To Split This Abstraction Into mentioning

confidence: 99%

“…Software bloat analysis [19,4,20,1,21,22,3,2,23,24] attempts to identify and remove performance problems due to run-time inefficiencies in the code execution and the use of memory. Mitchell et al [5] propose a manual approach that detects bloat by structuring behavior according to the flow of information, and their later work [4] introduces a way to find data structures that consume excessive amounts of memory. Our work takes further step in performing online optimization of inappropriately-used data structures.…”

Section: Related Workmentioning

confidence: 99%

“…A great deal of evidence [1,2,3,4,5] suggests that these problems are primarily caused by run-time bloat [6]-excessive memory usage and computation to accomplish simple tasks-often from inappropriate design/implementation choices (e.g., creating general APIs to facilitate reuse, using general implementations without any specialization, etc.) that exist throughout the program.…”

Section: Introductionmentioning

confidence: 99%

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CoCo: Sound and Adaptive Replacement of Java Collections

2013

ECOOP 2013 – Object-Oriented Programming

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Abstract. Inefficient use of Java containers is an important source of run-time inefficiencies in large applications. This paper presents an application-level dynamic optimization technique called CoCo, that exploits algorithmic advantages of Java collections to improve performance. CoCo dynamically identifies optimal Java collection objects and safely performs run-time collection replacement, both using pure Java code. At the heart of this technique is a framework that abstracts container elements to achieve efficiency and that concretizes abstractions to achieve soundness. We have implemented part of the Java collection framework as instances of this framework, and developed a static CoCo compiler to generate Java code that performs optimizations. This work is the first step towards achieving the ultimate goal of automatically optimizing away semantic inefficiencies.

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Section: List Combomentioning

confidence: 99%

Section: Line 21) Otherwise We Have To Split This Abstraction Into mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CoCo: Sound and Adaptive Replacement of Java Collections

2013

ECOOP 2013 – Object-Oriented Programming

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“…As a result, framework-intensive applications do an extraordinary amount of work to accomplish simple tasks. For example, the conversion of a single date field from a SOAP data source to a Java object can require as many as 268 method calls and the generation of 70 objects [21]. Much work involves the creation and initialization of temporaries, short-lived objects that are created as the by-product of some computation.…”

Section: Introductionmentioning

confidence: 99%

A scalable technique for characterizing the usage of temporaries in framework-intensive Java applications

Dufour

Ryder

Sevitsky

2008

Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of Software Engineering

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Framework-intensive applications (e.g., Web applications) heavily use temporary data structures, often resulting in performance bottlenecks. This paper presents an optimized blended escape analysis to approximate object lifetimes and thus, to identify these temporaries and their uses. Empirical results show that this optimized analysis on average prunes 37% of the basic blocks in our benchmarks, and achieves a speedup of up to 29 times compared to the original analysis. Newly defined metrics quantify key properties of temporary data structures and their uses. A detailed empirical evaluation offers the first characterization of temporaries in frameworkintensive applications. The results show that temporary data structures can include up to 12 distinct object types and can traverse through as many as 14 method invocations before being captured.

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