Annotated Bibliography .Guidelines Annotated Bibliograph.docx

Annotated Bibliography
.
Guidelines: Annotated Bibliography
Purpose: Explore current literature (collection of writing on a
specific topic) to increase
knowledge of leadership in nursing practice.
The annotated bibliography assignment will help students
prepare to design and present a poster presentation regarding
nursing leadership in practice. The focus is building student
knowledge of various leadership roles in nursing (current
trends). The assignment also promotes student reflection on
development of their own leadership skills.
Students will read the summary of the Institute of Medicine
(IOM) “The Future of Nursing: Leading Change, Advancing
Health” for baseline identification of leadership roles (posted in
Blackboard).
Students will then search the literature to identify and select
five (5) nurse leaders, who will be the topic of the annotated
bibliography summaries (students must use credible sources
when searching literature).
Students may also choose to submit 2 of the 5 annotated
bibliography summaries on the following topics:
1. Student Nurse Leaders (2)
2. Current Trends in Nursing Leadership (3)
Each of the five annotated bibliography summaries should be no
more than one page, typed, and must include the following:
1. The identified leader’s specific roles & responsibilities
2. The identified leader’s accomplishments
3. Barriers and facilitators to leader achievement of goals
4. Knowledge gained from reading content included in the
annotated bibliography summary

Annotated Bibliography Grading Rubric
Criteria
Points Possible
Points Earned
Faculty Comments
Provides a clear description of the identified leader’s role (s)
and responsibilities (related to nursing)
20
Provides examples of the leader’s
accomplishments (at least 2 examples)
10
Summarizes barriers inhibiting the leader’s achievement of
goals
15
Summarizes facilitators enhancing the leader’s achievement of
goals
15
Summary of leadership knowledge gained from reading content

included in the annotated bibliography summary
20
Correct grammar/spelling
10
APA format
10
Total
100
[Type text]
30 February 2005 QUEUE rants: [email protected]
DARNEDTesting large systems is a daunting task, but there are
steps we can take to ease the pain.
T
he increasing size and complexity of software, coupled with
concurrency and dis-
tributed systems, has made apparent the ineffectiveness of using
only handcrafted
tests. The misuse of code coverage and avoidance of random
testing has exacer-
bated the problem. We must start again, beginning with good
design (including

dependency analysis), good static checking (including model
property checking), and
good unit testing (including good input selection). Code
coverage can help select and
prioritize tests to make you more effi cient, as can the all-pairs
technique for controlling
the number of confi gurations. Finally, testers can use models to
generate test coverage
and good stochastic tests, and to act as test oracles.
HANDCRAFTED TESTS OUTPACED BY HARDWARE AND
SOFTWARE
Hardware advances have followed Moore’s law for years, giving
the capability for run-
ning ever-more complex software on the same cost platform.
Developers have taken
advantage of this by raising their level of abstraction from
assembly to fi rst-generation
compiled languages to managed code such as C# and Java, and
rapid application devel-
opment environments such as Visual Basic. Although the
number of lines of code per
day per programmer appears to remain relatively fi xed, the
power of each line of code
has increased, allowing complex systems to be built. Moore’s
law provides double the
computing power every 18 months and software code size tends
to double every seven
years, but software testing does not appear to be keeping pace.
QUEUE February 2005 31 more queue:
www.acmqueue.comBIGBIGBIGTO TESTTOO DARNED
Quality

AssuranceFO
C
U
S
KEITH STOBIE, MICROSOFT
Unfortunately, the increased power has not, in gen-
eral, made testing easier. While in a few cases the more
powerful hardware means we can do more complete test-
ing, in general the testing problem is getting worse. You
can test square root for all the 32-bit values in a reason-
able time,1,2 but we are now moving to 64-bit machines
with 64-bit values (and even longer for fl oating point).
Assuming a nanosecond per test case, it would take 584
years to test all the values. Sure, you could scale to, say,
1,000 processors, but you would still need six months for
just this one test case.
Two other issues of complexity to consider are: the
number of different execution states software can go
through, and concurrency. User interfaces were originally
very rigid, letting users progress through a single fi xed
set of operations—for example, a single set of hierarchi-
cal menus and prompts. Now, good user design is event
driven with the user in control of the sequence of actions.
Further, many actions can be accomplished in multiple
manners (e.g., closing a window via the menu [File/
Close], a shortcut key [ALT-F4], or the mouse [click on the
Close icon]). Multiple rep-
resentations of items (e.g.,
fi le names) also generally

mean multiple execution
paths. A manifestation of
this is the security issue
that occurs when an
application makes wrong
decisions based on a non-
canonical representation
of a name.3 These explo-
sive combinations make
it virtually impossible for a tester to both conceive of and
then test a reasonable sampling of all the combinations.
Concurrency is becoming more prevalent at many
levels. Beyond multiple processes, we see increasing
lower-level concurrency, especially as a result of multicore
chipsets that encourage parallelism via threading and
hyperthreading; soon, everyone will have a dual-proces-
sor machine. Machine clustering and Web services are
increasing the concurrency across applications.
All of this creates greater possibilities for more insidi-
ous bugs to be introduced, while making them harder to
detect.
MISUNDERSTANDING CODE COVERAGE
AND STOCHASTIC TESTING
Before discussing possible approaches to these problems,
let’s clear up some pervasive misconceptions.
Myth: Code coverage means quality. Tools for measur-
ing structural code coverage have become increasingly
easy to use and deploy and have thus deservedly gained
greater acceptance in the testing arsenal. A fundamental
fl aw made by many organizations (especially by manage-
ment, which measures by numbers) is to presume that

because low code-coverage measures indicate poor test-
ing, or that because good sets of tests have high coverage,
high coverage therefore implies good testing (see Logical
Fallacies sidebar). Code coverage merely measures that a
statement, block, or branch has been exercised. It gives
no measure of whether the exercised code behaved cor-
rectly. As code gets too big to easily roll up qualitative
assessments, people start summarizing code coverage
instead of test information. Thus, we know low code cov-
erage is bad, because if we have never exercised the code,
it appears impossible to say we have “tested” it. However,
while having something that just executes the code may
consequently reveal a few possible fl aws (e.g., a complete
system failure), it doesn’t really indicate if the code has
been what most people consider tested.
BIG
TO TEST
TOO DARNED
Quality
AssuranceFO
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Logical Fallacies
Denying the Antecedent:
If A, then B; not A; therefore, not B
If low coverage, then poor tests; not low coverage; therefore,
not poor tests
Asserting the Consequent:

If p, then q; q; therefore, p
If good tests, then high coverage; high coverage; therefore,
good tests
QUEUE February 2005 33 more queue: www.acmqueue.com
Tested indicates you have verified the results of the
code execution in useful ways. The fallacy of high code
coverage will become more evident with the next genera-
tion of automatic unit testing tools (discussed later) that
can generate high code coverage with no results checking.
Further, results of real software comparing code coverage
and defect density (defects per kilo-lines of code) show
that using coverage measures alone as predictors of defect
density (software quality/reliability) is not accurate.
Myth: Random testing is bad. One of the big debates
in testing is partitioned (typically handcrafted) test design
versus operational, profile-based stochastic testing (a
method of random testing). Whereas many organiza-
tions today still perform testing (such as choosing the
partitions) as a craft (either manually or semi-automati-
cally), it is unclear that this is the most effective method,
especially with the increasing complexity of software.
Current evidence indicates that unless you have reliable
knowledge about areas of increased fault likelihood, then
random testing can do as well as handcrafted tests.4,5
For example, a recent academic study with fault seed-
ing showed that under some circumstance the all-pairs
testing technique (see “Choose configuration interactions
with all-pairs” later in this article) applied to function
parameters was no better than random testing at detect-

ing faults.6
The real difficulty in doing random testing (like the
problem with coverage) is verifying the result. How, for
random inputs, can we judge the output? It is easier to
have static tests with precomputed outputs. In testing,
being able to verify the output is called the test oracle
problem.7,8 There are numerous current areas of work on
this problem.
Another problem is that very low probability sub–
domains are likely to be disregarded by random testing,
but if the cost of failures in those subdomains is very
high, they could have a large impact. This is a major flaw
of random testing and a good reason for using it as a
complementary rather than the sole testing strategy.
Part of the testing discipline is to understand where
we have reliable knowledge of increased fault likelihood.
The classic boundary value analysis technique succeeds
because off-by-one errors increase the fault likelihood at
boundaries. Many other analysis techniques have a less
tried-and-true underlying fault model to reliably indi-
cate increased fault likelihood. For example, partitioning
based on code coverage doesn’t guarantee finding the
fault, as the right input must be used to stimulate the
fault and sufficient verification must be done to realize
the fault has produced a failure.
TEST ISSUES: DO OVER
It’s clear that increasing structural code coverage doesn’t
solve the testing problem and that stochastic testing is
frequently as good as or better than handcrafted test
cases. Given this state of affairs, how do we address the
issue of “it’s too darned big to test”? There are several
approaches to consider: unit testing, design, static check-

ing, and concurrency testing.
Good unit testing (including good input selection).
Getting high-quality units before integration is the first
key to making big system testing tractable. The simple
way to do that is to follow the decades-old recommenda-
tion of starting with good unit testing. The IEEE Standard
for Software Unit Testing has been around for years [Std.
1008-1987] and requires 100 percent statement cover-
age. This has been nicely reincarnated via the test-driven
development movement, which also espouses the previ-
ously seldom-adhered-to idea of writing the tests before
the code. Testing a big system is especially difficult when
the underlying components and units are of low quality.
Why isn’t good unit testing prevalent? In many shops
the problem is partly cultural. Software developers pre-
sumed someone else was responsible for testing besides
them. Some development groups have spent more effort
building out stubbed components and test harnesses than
on building the actual components. Standard unit testing
harnesses now exist for many environments, and the
creation of mock objects is becoming semi-automated.
Another reason for poor unit testing is that software was
once simpler and reliability expectations lower. In today’s
world insecure systems arising from simple problems (for
example, buffer overruns) that good unit testing could
have caught have really motivated management to drive
for better unit testing.
A variety of tools exist in this arena. Unit testing
frameworks include the XUnit family of tools, Framework
for Integrated Test (Ward Cunningham’s Fit), Team Share,
etc. Further, IDEs (integrated development environments)
such as Eclipse and Rational can generate unit test out-
lines. Any development shop should be able to find and

use applicable ones.
In general, today’s unit testing requires developers to
handcraft each test case by defining the input and the
expected output. In the future, more intelligent tools will
be able to create good input coverage test cases and some
expected output. We may see 100 percent code coverage,
with no results checking. We will need to become more
aware of the thoroughness of results checking instead
of just code coverage. How to measure results-checking
thoroughness hasn’t really been addressed.
Current automatic unit-generation test techniques
work from the simple known failure modes (such as null
parameters as inputs) to analysis of the data structures
used. Data-structure generation can be based on con-
straint solvers, models, and symbolic execution.
Other tools can enhance an existing set of tests. For
example, Eclat creates an approximate test oracle from a
test suite, then uses that oracle to aid in generating test
inputs that are likely to reveal bugs or expose new behav-
ior while ignoring those that are invalid inputs or that
exercise already-tested functionality.
Even without tools, just training people in simple
functional test techniques can help them choose bet-
ter inputs for their unit testing. You can also start with
generic checklists of things to test and then make them
your own by recording your organization’s history of
what makes interesting input. You can fi nd industry
checklists in several books under different names such
as the following: “Common Software Errors” (Kaner, C.,

Falk, J. and Nguyen, H.Q. 1993. Testing Computer Soft-
ware. Van Nostrand Reinhold); “Test Catalog” (Marick, B.
1994. Craft of Software Testing. Prentice Hall PTR. www.
testing.com/writings/short-catalog.pdf); or “Attacks”
(Whittaker, J.A. 2002. How to Break Software: A Practi-
cal Guide to Testing. Addison Wesley); as well as on the
Web (for example, http://blogs.msdn.com/jledgard/
archive/2003/11/03/53722.aspx).
Good design (including dependency analysis). Many
books and papers explain how to do good up-front design
(the best kind), but what if you already have the code?
Dependency analysis can be used to refactor code
(rewriting to improve code readability or structure; see
http://www.refactoring.com/). Many IDEs are incorporat-
ing features to make code refactoring easier. Good unit
tests, as agile methods advocate, provide more confi dence
that refactoring hasn’t broken or regressed the system.
Dependency analysis allows testers to choose only
those test cases that target a change. Simplistic analysis
can be done by looking at structural code dependencies,
and it can be very effective. More sophisticated data-fl ow
analysis allows more accurate dependency determina-
tion, but at a greater cost. Either technique can be a big
improvement over just running everything all the time. A
test design implication of this is to create relatively small
test cases to reduce extraneous testing or factor big tests
into little ones.9
Good static checking (including model property
checking). The convergence of static analysis tools with
formal methods is now providing powerful tools for
ensuring high-quality units and to some extent their

integration. For example, PREfi x is a simulation tool for
C/C++ that simulates the program execution state along
a selected set of program paths and queries the execution
state to identify programming errors.10 The issue creat-
ing the greatest resistance to adoption for most static
checking remains the false positive rate. Some tools can
only make reliable guesses and sometimes guess wrong.
Developers get frustrated if they can’t turn off the tool
for the places they know it is wrong. Sometimes develop-
ers get carried away and turn off too much, then miss
problems the tool could have caught. Some developers
don’t believe the issues found are really bugs even when
they are.
Many of the more advanced checks require additional
annotations to describe the intent. You can emulate some
of the stricter checks in any language just using compiler
or language constructs such as assert(). It is still a chal-
lenge to convince many developers that the extra annota-
tions will pay off in higher-quality, more robust, and
more maintainable code. Others, however, who have used
it and seen nasty issues that would have taken hours,
days, or weeks to debug being found effortlessly up front,
would never go back.
All of this data can also help independent testers later
because defects found through static analysis are early
indicators of pre-release system defect density. Static anal-
ysis defect density can be used to discriminate between
components of high and low quality (fault-prone and
non-fault-prone components).
Concurrency testing. Concurrency issues can be
detected both statically (formal method properties such as
liveness, livelock/deadlock, etc.) and dynamically (auto-
matic race lockset tracking). Static detection frequently

BIG
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TOO DARNED
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requires extensive annotation of the source code with
additional information to allow the necessary reasoning
to be done about the code. The advantage of static analy-
sis is its potential to uncover all race conditions.
Dynamic race detectors, such as code coverage tools,
require a good set of test cases as input because they look
only at what the tests force to occur. They can, however,
determine race conditions even though the test itself
didn’t force it. As long as the test executes the code of the
overlapping locks, it is likely to be detected. Several tools,
such as Java PathExplorer (JPAX), have been created that
extend the Eraser lockset algorithm.11 Besides no guaran-
tee of completeness without complete test cases, the other
issues still being resolved with dynamic race detectors
include their performance and the number of false posi-
tives they generate.
More traditional methods of concurrency testing

involve controlling the thread scheduler, if possible, to
create unusual timings. Fault injection tools, such as Secu-
rity Innovation’s Holodeck, can be used to create artifi cial
delays and increase the likelihood of race conditions. As a
last resort, the old standby of just running lots of activity
concurrently under varying conditions has been fruitful
in fi nding defects (although perhaps not as effi ciently as
many of the newer methods).
PRIORITIZE TESTING
The previous section helps set the stage at a basic level
with units of code and analysis, but most test organiza-
tions deal with testing the integrated whole. Testers
have to be creative in addressing the overwhelming size
of today’s software systems. When it’s too darned big
to test, you must be selective in what you test, and use
more powerful automation such as modeling to help you
test. Fault injection tools again come in handy as a way
to tweak things when reaching through the overall big
system becomes daunting (which it does quickly!).
Use code coverage to help select and prioritize tests.
Prioritizing test cases has become increasingly practi-
cal in the past two decades. If you know the coverage of
each test case, you can prioritize the tests such that you
run tests in the least amount of time to get the highest
coverage. The major problem here is getting the coverage
data and keeping it up to date. Not all tools let you merge
coverage data from different builds, and running coverage
all the time can slow down testing. The time to calculate
the minimization can also be a deterrent.
For many collections of test cases, running the mini-
mal set of test cases that give the same coverage as all of
the test cases typically fi nds almost all of the bugs that

running all of the test cases would fi nd.
Industrial experience at several companies appears to
confi rm this. For example, for an industrial program of
20,929 blocks, choosing tests only by function call cover-
age required only 10 percent of the tests while providing
99.2 percent of the same coverage. Reducing by block
coverage meant that 34 percent of the tests provided
the same block coverage and 99.99 percent of the same
branch (segment) coverage. Further, there were no fi eld-
reported defects found that would have been caught by
the full set of tests but missed by the coverage-reduced set
of tests.
Best of all, anyone can easily run the experimental
comparison. First run the minimal set of tests providing
the same coverage as all of the tests, and then run the
remaining tests to see how many additional defects are
revealed.
You can also combine this with dependency analysis
to fi rst target only the changed code. Depending on the
sophistication of your dependency analysis, you may not
have to do any further testing.
Use customer usage data. Another great way to target
testing is based on actual customer usage. Customers
perceive the reliability of a product relative to their usage
of it. A feature used by only a few customers on rare occa-
sions will have less impact on customer satisfaction than
bread-and-butter features used by virtually all customers
all of the time. Although development teams can always
make conjectures about product usage, which makes a
useful start, actual data improves the targeting.
Ideally, you can automatically record and have actual

customer usage sent to you. This approach has several
problems, including privacy, performance, and secu-
rity. Each of these can be dealt with, but it is nontrivial.
Alternatively, you may have only a select few customers
reporting data, you may do random sampling, or you
may fall back on case studies or usability studies. You
When it’s too darned big to test,
you must be selective in what you test,
and use more powerful automation
such as modeling to help you test.
can also work with your customers for cooperative early
testing either through beta programs or even earlier pre-
release disclosures.12
Customer usage data is especially important in reli-
ability testing where an operational profi le improves the
meaningfulness of the reliability data. Customer usage
data is also critical to prioritizing the confi gurations that
you test. Setting up environments similar to those of your
customers helps you fi nd interactions that you might
otherwise miss.
Choose confi guration interactions with all-pairs. A
major problem that makes the testing task too darned
big is worrying about all the possible confi gurations.
Confi gurations cover things such as different hardware,
different software (operating system, Web browser, appli-
cation, etc.) or software versions, confi guration values
(such as network speed or amount of memory), etc. If
you understand which interactions of your confi gura-
tions cause problems, you should explicitly test for them.

But when all the software “should” be independent of
the confi guration, and experience has shown you it isn’t,
then you get worried. Setting up a confi guration to test is
frequently expensive, and running a full set of tests typi-
cally isn’t cheap, either.
If you are doing performance testing, you need the
more formal design-of-experiments techniques associ-
ated with robust testing and requiring orthogonal arrays.
For simple verifi cation, however, another easy, cheap
technique used by many test shops is all-pairs. The most
common defect doesn’t require any interaction. Just
triggering the defect under any confi guration will cause
a failure about 80 percent of the time. After basic testing
eliminates those defects, the next most common defects
turn out to require the interaction of just two aspects of
the confi guration. Which two aspects? That’s the trick!
Using all-pairs, you can cheaply verify any pair of interac-
tions in your confi guration. Public domain tools exist
(http://tejasconsulting.com/open-testware/feature/all-
pairs.html), and it takes less than a half hour to download
the tool and create a description of your confi guration
issues. You’ll then get a very quick answer.
As a simple example of this, I consulted with a test
group that had tested 100 different confi gurations, but
were still getting errors from the fi eld. I taught them the
technique and tool, and in less than an hour they had a
result showing that only 60 confi gurations were needed
to cover all pairs of interactions, and the interaction most
recently reported from the fi eld was one of them. That is,
if they tested with fewer confi gurations, they could have
found the bug before shipping.
All-pairs testing may be useful in other domains, but

the studies are not yet conclusive.
USE OF MODELS FOR STOCHASTIC TESTS
One way toward stochastic testing is via the use of
models. You can begin with the simplest of models, such
as “dumb monkey” test tools or “fuzz” testers. You can
enhance them toward “smart monkey” tools and formal-
ize them using a variety of modeling languages, such as
fi nite state machines, UML2 (Unifi ed Modeling Language,
version 2), and abstract state machines. Testers’ reluctance
in the past to embrace formal methods came principally
from the state explosion problem that appeared to rel-
egate most formal methods to “toy” problems instead of
the big system-level problems that many testers have to
deal with.
Recent advances in many fi elds have helped, but espe-
cially recent breakthroughs that make SAT (propositional
satisfi ability) solvers highly effi cient and able to handle
more than 1 million variables and operations. Models
can be used to generate all relevant variations for limited
sizes of data structures.13,14 You can also use a stochastic
model that defi nes the structure of how the target system
is stimulated by its environment.15 This stochastic testing
takes a different approach to sampling than partition test-
ing and simple random testing.
Even nonautomated models can provide useful
insights for test design. Simple fi nite state machine
models can show states the designers hadn’t thought
about or anticipated. They also help clarify the expected
behavior for the tester. You can build your own fi nite
state machines in almost any language. A simple example
for C# is goldilocks.16
BIG

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TOO DARNED
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The major issue with model-based testing is changing
testers’ mind-sets. Many testers haven’t been trained to
think abstractly about what they are testing, and model-
ing requires the ability to abstract away some detail while
concentrating on specific aspects. Modeling can be espe-
cially difficult for ad hoc testers not used to approaching
a problem systematically. Another reason modeling fails
is because people expect it to solve all their problems.
Frequently it is better just to add a few manually designed
test cases than to extend the model to cover all the details
for all cases.
ECONOMY IN TESTING
The size and complexity of today’s software requires that
testers be economical in their test methods. Testers need a
good understanding of the fault models of their tech-
niques and when and how they apply in order to make
them better than stochastic testing. Code coverage should
be used to make testing more efficient in selecting and
prioritizing tests, but not necessarily in judging the tests.
Data on customer usage is also paramount in selecting

tests and configurations with constrained resources. Pair-
wise testing can be used to control the growth of testing
different configurations.
Attempting to black-box test integrations of poor-qual-
ity components (the traditional “big bang” technique)
has always been ineffective, but large systems make it
exponentially worse. Test groups must require and prod-
uct developers must embrace thorough unit testing and
preferably tests before code (test-driven development).
Dependencies among units must be controlled to make
integration quality truly feasible. Q
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REFERENCES
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http://www.kaner.com/testarch.html.
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o%20many%20cases.ppt.
3. Howard, M., and LeBlanc, D. 2002. Writing secure code,
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4. Nair, V. N., et al. 1998. A statistical assessment of some
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6. Bach, J., and Schroeder, P. 2004. Pairwise testing: A
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9. Saff, D., and Ernst, M. 2004. Automatic mock object
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Anderson, T. 1997. Eraser: A dynamic data race detec-
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14. Ball, T., et al. 2000. State generation and automated
class testing. Software Testing, Verification and Reliability
10(3): 149-170.
15. Whittaker, J. A. 1997. Stochastic software testing.
Annals of Software Engineering 4: 115-131. http://www.
geocities.com/harry_robinson_testing/whittaker97.
doc.
16. Robinson, H., and Corning, M. Model-based testing
in the key of C#. http://www.qasig.org/presentations/
QASIG_Goldilocks2.pdf.
KEITH STOBIE is a test architect in Microsoft’s XML Web
Services group (Indigo), where he directs and instructs in
QA and test process and strategy. He also plans, designs,
and reviews software architecture and tests. With 20 years
of distributed systems testing experience, Stobie’s interests
are in testing methodology, tools technology, and quality
process. He is also active in the Web Services Interoperability
organization’s (WS-I.org) Test Working Group creating test
tools for analysis and conformance of WS-I profiles. He has a
B.S. in computer science from Cornell University.
© 2005 ACM 1542-7730/05/0200

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