OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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application. Once the bottleneck is identified, optimizations can be performed on
portions of the code that will have the greatest impact. The profile data collected
typically includes some combination of information about memory allocation and
garbage collection, threading and deadlock, and CPU performance. These tools do
not record the bytecodes executed because it is not necessary in order to identify
the performance issues they are designed to solve. Examples of some of these tools
include JProbe [6], Optimizeit Enterprise Suite [24] and JInsight [88]. A list of several
additional tools is available at Java Performance Tuning [98].
3.3.2 Other Profiling Studies
A number of other studies have been conducted which profile the characteristics of
Java applications. Some of these studies are conducted at the bytecode level while
others examine higher level behaviors.
High Level Profiling
High level metrics describe some aspect of the behaviour of an application. While
the results are inherently a result of the bytecodes that are executed, the measures
typically do not involve examining every bytecode executed.
Metrics collected may be static, based on the files that make up the application,
or dynamic, based on data collected as the application runs. Many static metrics
are examined in Fenton and Pfleeger’s book [48]. Static metrics have also been the
subject of numerous conference papers [32, 111, 21] and journal articles [77, 26].
Dynamic metrics are collected as the application runs. They consider behaviors
that cannot be determined from the application source code alone. One example
of a dynamic metric is the level of runtime coupling between classes [80, 81]. This
measure is used in software engineering circles in order to assess the complexity of understanding,
enhancing and correcting an application. Dufour, Driesen, Hendren and
Verbrugge describe several other dynamic metrics for Java [45]. These metrics cover
five different behaviors of a Java application which include its size, data structure,
memory use, concurrency and polymorphism.
Bytecode Level Profiling
Previous studies have been conducted that examine the bytecode profile of Java applications.
The simplest of these studies perform static profiling. This involves examining
the Java class files that make up the application directly, without actually