2008 November « SQA Solution

Please let me know if anyone has Used VSTS-visual studio team system for Performance testing of web based application. What is your Experience on same ?

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Overcoming SOA testing challenges

Some people say service-oriented architecture (SOA) testing isn’t really new, that this type of testing has occurred in-house ever since there’s been middleware. However, that type of testing does differ from testing traditional applications. On top of that, SOA is being used more often, which means more of those types of applications need to be tested.

This special report takes a look at the challenges of testing SOA applications, as well as blind spots to be aware of. It also provides technical tips for conducting the different types of tests that are required.
Working with the business

The business side of SOA testing processes
Rami Jaamour discusses the ways in which business and IT people can work together in the SOA testing processes.

Building a test system

Building a testing system for SOA
Rami Jaamour discusses the best place to begin when building a testing system for SOA and how to establish a quality policy as part of the overall SOA governance strategy.

Unit testing

Unit, integration testing first steps toward SOA quality

When it comes to quality SOA applications, many people play a role. Developers begin the testing effort with unit testing, followed by testers doing integration testing.
Unit testing for SOA best practices
Rami Jaamour discusses two types of unit testing and what they both need to have in order to maintain best practices for SOA.

Functional and regression testing

Use functional and regression testing to validate SOA solutions
With SOA applications, the quality of the individual services does not necessarily translate into the overall quality of the business solution. It is the quality of the whole that truly matters. In this tip, David W. Johnson explains how to use functional and regression testing to validate that SOA solutions deliver the business functionality required.

Performance testing

Performance testing: Ensure your SOA applications perform
Performance testing SOA applications brings numerous challenges. Not only must you test individual services and test the services together, but you also need to consider third-party services.

Security testing

Be aware of SOA application security issues
Making disparate systems work together via Web services and SOA can save money, time, and frustration. But if you aren’t careful, the combination can leave you open to attack.

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Strategic QA. Steps to effective software quality assurance

There is a great white paper from IBM by Dominic Tavassoli, IBM

SQA Solution uses IBM techniques and tools to streamline QA process. Download this new white paper now:

http://go.techtarget.com/r/4986076/8268532

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Database Testing: How to Regression Test a Relational Database

Relational database management systems (RDBMSs) often
persist mission-critical data
which is updated by many applications and potentially thousands if not
millions of end users. Furthermore, they implement important
functionality in the form of database methods (stored procedures, stored
functions, and/or triggers) and database objects (e.g. Java or C#
instances). The best way to ensure the continuing quality of these
assets, at least from a technical point of view, you should have a full
regression test suite which you can run on a regular basis.

In
this article I argue for a fully automated, continuous regression
testing based approach to database testing. Just as agile software
developers take this approach to their application code, we should also
do the same for our databases.

Why test an RDBMS?
What should we test?
When should we test?
How should we test?
Who should test?
Introducing database testing into
your organization
Database testing and data
inspection
Best practices

1. Why Test an RDBMS?

There are several reasons why you need to develop a
comprehensive testing strategy for your RDBMS:

Data is an important corporate asset.
Doesn’t it make sense to invest the effort required to validate the quality
of data via effective testing? My July 2006

survey into the current state of data management indicates that 95.7% of
respondents believe that data is a corporate asset. Yet of them only
40.3% had a database test suite in place to validate the data and of those
without a test suite only 31.6% had even discussed the concept.
Mission-critical business functionality is implemented in
RDBMSs. In the

survey, 63.7% of respondents indicated that their organizations
did this, but of those only 46% had regression tests in place to validate
the logic. Shouldn’t we be doing better?
Current approaches aren’t sufficient. The current state of
the art in many organizations is for data professionals to control changes to
the database schemas, for developers to visually inspect the database
during construction, and to perform some form of formal testing
during the test phase at the end of the lifecycle. Unfortunately,
none of these approaches prove effective. Application developers will
often go around their organization’s data management group because they find
them too difficult to work with, too slow in the way they work, or sometimes
they don’t even know they should be working together. The end result
is that the teams don’t follow the desired data quality procedures and as a
result quality suffers. Although visual inspection

of query results is a good start it is little more than a debugging technique in
practice that will help you to find problems but not prevent them. Testing late in the lifecycle is better than nothing, but as Barry
Boehm noted in the early 80s it’s
incredibly
expensive to fix any defects you find at that point.
Testing provides the concrete feedback required to
identify defects. How do you know how good the quality of your
source data actually is without an effective test suite which you can run
whenever you need to?
Support for evolutionary development. Many

evolutionary development techniques, in particular
database
refactoring, are predicated upon the idea that it must be possible to
determine if something in the database has been broken when a change has
been made. The easiest way to do that is to simply run your regression
test suite.

Uncomfortable Question:

Isn’t it time that we stopped talking about data
quality and actually started doing something about it?

Here’s a few interesting questions to ask someone who isn’t convinced that
you need to test the DB:

If you’re implementing code in the DB in the form of stored procedures, triggers,
… shouldn’t you test that code to the same level that you test your app code?
Think of all the data quality problems you’ve run into over the years.
Wouldn’t it have been nice if someone had originally tested and discovered those
problems before you did?
Wouldn’t it be nice to have a test suite to run so that you could determine
how (and if) the DB actually works?

I think that one of the reasons that we don’t hear much about database
testing is because it is a relatively new idea within the data community. Many
traditional data professionals seem to think that testing is something that
other people do, particularly test/quality assurance professionals, do. This
reflects a penchant for

over-specialization and a
serial approach towards
development by traditionalists, two ideas which have also been shown to be
questionable organizational approaches at best.

2. What Should We Test?

Figure 1 indicates what you should
consider testing when it comes to relational databases. The diagram is drawn
from the point of view of a single database, the dashed lines indicate
threat
boundaries, indicating that you need to consider threats both within the
database (clear box testing) and at the interface to the database (black box
testing). Table 1
lists the issues which you should consider testing for both internally within
the database and at the interface to it.
For details, read the article
What To Test in an
RDBMS.

Figure 1. What to test.

Table 1. What to test in an RDBMS.

Black-Box Testing at the Interface	White/Clear-Box Testing Internally Within the Database
O/R mappings (including the meta data) Incoming data values Outgoing data values (from queries, stored functions, views …)	Scaffolding code (e.g. triggers or updateable views) which support refactorings Typical unit tests for your stored procedures, functions, and triggers Existence tests for database schema elements (tables, procedures, …) View definitions Referential integrity (RI) rules Default values for a column Data invariants for a single column Data invariants involving several columns

3. When Should We Test?

Agile software developers take a test-first approach to development
where they write a test before you write just enough production code to fulfill
that test. The
steps of test first development (TFD) are overviewed in the UML activity diagram of
Figure 2. The first step is to quickly add a test,
basically just enough code to fail. Next you run your tests, often the
complete test suite although for sake of speed you may decide to run only a
subset, to ensure that the new test does in fact fail. You then update
your functional code to make it pass the new tests. The fourth step is to
run your tests again. If they fail you need to update your functional code
and retest. Once the tests pass the next step is to start over.

Figure 2. The Process of Test First Development (TFD).

Test-driven
development (TDD) is an
evolutionary approach to development which combines test-first development and
refactoring.

When an agile software developer goes to implement a new feature, the first
question they ask themselves is “Is this the best design possible which enables
me to add this feature?” If the answer is yes, then they do the work to
add the feature. If the answer is no then they refactor the design to make
it the best possible then they continue with a TFD approach. This strategy
is applicable to developing both your application code and your database schema,
two things that you would work on in parallel.

When you first start following a TDD approach to development you quickly
discover that to make it successful you need to automate as much of the process
as possible? Do you really want to manually run the same build script(s)
and the same testing script(s) over and over again? Of course not.
So, agile developers have created OSS tools such as
ANT,

Maven, and Cruise Control
(to name a few) which enable them to automate these tasks. More
importantly, it enables them to automate their

database testing script into the build procedure itself.

Agile developers realize that testing is so important to their success that
it is something they do every day,
not just at the end of the lifecycle. They test as often and early as possible, and better yet
they test first. As you can see with the
agile system development
lifecycle (SDLC) of Figure 3 testing is in fact
something that occurs during the development and release cycles, not just during
release. Furthermore, many agile software developers realize that you can
test more than just your code, you can in fact validate every work product
created on a software development project if you choose to. This
philosophy is exemplified by the
Full
Lifecycle Object-Oriented Testing (FLOOT) Methodology.

Figure 3. The Agile Lifecycle.

4. How to Test

Although you want to keep your database testing efforts as simple as
possible, at first you will discover that you have a fair bit of both learning
and set up to do. In this section I discuss the need for various
database sandboxes in which people will test:
in short, if you want to do database testing then you’re going to need test
databases (sandboxes) to work in. I then overview how to

write a database test and more importantly
describe setup strategies for database tests.
Finally, I overview several database testing tools which
you may want to consider.

4.1 Database Sandboxes

A common

best practice on
agile teams is to ensure that developers have their own “sandboxes” to work in. A sandbox is basically a technical environment whose
scope is well defined and respected.
Figure 4 depicts the various types of sandboxes
which your team may choose to work in. In each sandbox you’ll have a copy
of the database. In the development sandbox you’ll experiment, implement
new functionality, and refactor existing functionality, validate your changes
through testing, and then eventually you’ll promote your work once you’re happy
with it to the project integration sandbox. In this sandbox you will
rebuild your system and then run all the tests to ensure you haven’t broken
anything (if so, then back to the development sandbox). Occasionally, at
least once an iteration/cycle, you’ll deploy your work to the level (demo and
pre-production testing), and rerun your test suite (including database tests)
each time that you do so to ensure that your changes integrate with the
changes made by other developers. Every so often (perhaps once every six to twelve
months) into production. The primary advantage of sandboxes are that they help to reduce the risk
of technical errors adversely affecting a larger group of people than is
absolutely necessary at the time.

Figure 4. Sandboxes.

4.2 Writing Database Tests

There’s no magic when it comes to writing a database test, you write them
just like you would any other type of test. Database tests are typically a three-step process:

Setup the test. You need to put your database into a known
state before running tests against it. There are
several strategies for doing so.
Run the test. Using a database regression
testing tool, run your database tests just like you would run your
application tests.
Check the results. You’ll need to be able to do “table
dumps” to obtain the current values in the database so that you can compare
them against the results which you expected.

The article
What To Test in an
RDBMS goes into greater detail.

4.3 Setting up Database Tests

To successfully your database you must first know the exact
state of the database, and the best way to do that is to simply put the database
in a known state before running your test suite. There are two common
strategies for doing this:

Fresh start. A common practice is to
rebuild the database, including both creation of the schema as well as
loading of initial test data, for every major test run (e.g. testing that
you do in your project integration or
pre-production test sandboxes).
Data reinitialization. For testing in
developer sandboxes, something that you should do every time you rebuild the
system, you may want to forgo dropping and rebuilding the database in favor
of simply reinitializing the source data. You can do this either by erasing
all existing data and then inserting the initial data vales back into the
database, or you can simple run updates to reset the data values. The
first approach is less risky and may even be faster for large amounts of
data.

An important part of writing database tests is the creation
of test data. You have several
strategies for doing so:

Have source test data.
You can maintain an external definition of the test data, perhaps in flat
files, XML files, or a secondary set of tables. This data would be
loaded in from the external source as needed.
Test data creation scripts.

You develop and maintain scripts, perhaps using data manipulation language (DML)
SQL code or simply application source code (e.g. Java or C#), which does the
necessary deletions, insertions, and/or updates required to create the test
data.
Self-contained test cases.
Each individual test case puts the database into a known state required for
the test.

These approaches to creating test data can be used alone or
in combination. A significant
advantage of writing creation scripts and self-contained test cases is that it
is much more likely that the developers of that code will place it under configuration
management (CM) control. Although
it is possible to put test data itself under CM control, worst case you generate
an export file that you check in, this isnâ€™t a common practice and therefore
may not occur as frequently as required. Choose
an approach that reflects the culture of your organization.

Where does test data come from? For unit testing, I prefer to
create sample data with known values. This way I can predict the
actual results for the tests that I do write and I know I have the
appropriate data values for those tests. For other forms of
testing — particularly load/stress, system integration, and function
testing, I will use live data so as to better simulate real-world
conditions.

Beware Coupling:

One danger with database regression testing, and
with regression testing in general, is coupling between tests.
If you put the database into a known state, then run several
tests against that known state before resetting it, then those
tests are potentially coupled to one another. Coupling
between tests occurs when one test counts on another one to
successfully run so as to put the database into a known state
for it. Self-contained test cases do not suffer from this
problem, although may be potentially slower as a result due to
the need for additional initialization steps.

4.4 What Testing Tools Are Available?

I believe that there are several critical features which you need to
successfully test RDBMSs. First, as Figure 1

implies you need two categories of database testing tools, one for interface tests
and one for internal database tests. Second, these testing tools should support the language that you’re developing
in. For example, for internal database testing if you’re a Microsoft SQL Server developer, your T-SQL
procedures should likely be tested using some form of T-SQL framework.
Similarly, Oracle DBAs should have a PL-SQL-based unit testing framework.
Third, you need tools which help you to put your database into a known state,
which implies the need not only for test data generation but also for managing
that data (like other critical development assets, test data should be under

configuration management control).

To make a long story short, although we’re starting to see a glimmer of hope
when it comes to database testing tools, as you can see in
Table 2, but we still have a long way to go. Luckily there are some good tools being developed by the open source software
(OSS) community and there are some commercial tools available as well.
Having said that, IMHO there is still significant opportunity for tool
vendors to improve their database testing offerings.

Table 2. Some database testing tools.

Category	Description	Examples
Unit testing tools	Tools which enable you to regression test your database.	DBFit DBUnit NDbUnit OUnit for Oracle

Archive for November, 2008

Table of Contents

1. Why Test an RDBMS?

2. What Should We Test?

3. When Should We Test?

4. How to Test

4.1 Database Sandboxes

4.2 Writing Database Tests

4.3 Setting up Database Tests

4.4 What Testing Tools Are Available?