Difference between Change Related Software Testing like Confirmation Testing & Regression Testing

During software testing we get failures. In most cases the underlying defects are corrected and the corrected test object is handed over to the testers for confirmation. This is the situation where we iterate in the test process and go back to the test execution process. We go back to perform confirmation testing and regression testing.

Confirmation testing and regression testing are important activities in test execution. They can appear in all the test levels from component testing to (one hopes rarely) acceptance testing and even during maintenance of a product in operation.

These two types of change-related software testing activities have one thing in common: they are executed after defect correction. Apart from that, they have very different goals.



In the figure above the test object with a defect is shown to the left. The defect has been discovered during the software testing activity. The defect has subsequently been corrected and we have got the new test object back again for testing; this is the one to the right.

What we must do now are confirmation testing and regression testing of the corrected test object.

Confirmation Testing:
Confirmation testing is the first to be performed after defect correction. It is done to ensure that the defect has indeed been successfully removed. The test that originally unveiled the defect by causing a failure is executed again and this time it should pass without problems. This is illustrated by the dark rectangle in the place where the defect was.

Regression Testing:

Regression testing may and should then be performed.

Regression testing is repetition of tests that have already been performed without problems to confirm that new defects have not come up or discovered after the change. In other words it is to ensure the object under test has not regressed.

Following example shows a case of regression:

A correction of a fault in a document using the "replace all" of the word "Author" with the word "Speaker" had an unintended effect in one of the paragraphs:

"… If you are providing the Presentation as part of your duties with your company or another company, please let me know and have a Speakerized representative of the company also sign this Agreement."

The amount of regression testing can vary from a complete rerun of all the test procedures that have already passed, to, well, in reality, no regression testing at all. The amount depends on issues such as:

1) The risk involved

2) The architecture of the system or product

3) The nature of the defect that has been corrected

The amount of regression testing we choose to do must be justified in accordance with the strategy for the test.

Regression testing should be performed whenever something in or around the object under testing has changed. Fault correction is an obvious reason. There could also be others, more external or environmental changes, which could cause us to consider regression testing.

An example of an environment change could be the installation of a new version of the underlying database administration system or operating system. Experience shows that such updates may have the strangest effects on systems or products previously running without problems.

Escaped Defects Found

Definition

An escaped defects is a defect that was not found by, or one that escaped from, the quality assurance team. Typically, those issues are found by end users after released version has made available to them. The metrics Escaped Defects Found counts number of new escaped defects found over period of time (day, week, month).
Calculation
To be able to calculate that metric, it is important that in your defect tracking system you track:

* affected version, version of software in which this defect was found.
* release date, date when version was released

The calculation process:

* Find all versions that were already released
* for each version, find all defects that affected the version
* if defect creation date is after version release date then this defect is an escaped defect.
* now count all those escaped defects.

Dimensions
It should be possible to monitor Escaped Defects Found in following dimensions:

* Affected version, to monitori metric value of any released version.
* Project/Product. To aggregated Escaped Defects Found over all released versions of the project or product.

Usage
I like this metric because tracking that sends correct message to development team: functionality that is released, should be of good quality. Less Escaped Defects almost always means good job of QA team.

Also important reason to keep the number of Escaped Defects low, is that fixing a single escaped software defect can take from a week to several weeks of effort to correct. As you have to include the time to isolate, repair, checkout, retest, reconfigure and redistribute. Fixing defect inside the development iteration is always much cheaper.
Presentation
Best presentation is a simple bar chart, where each bar shows number of escaped defects found per (day/week/month/quarter/year). Depending on your requirements you may want to create this chart for a whole company or each product. Also to compare quality of different versions it could makes sense to show bar chart with current values of escaped defects for each version of the product.

Maximizing QA Performance

An important element of any IT strategy is to ensure deployment of defect free systems. Among other benefits, this helps
minimize significantly the total cost of ownership (TCO) of applications. However, organizations quickly discover that
despite their best intentions and efforts, their QA team has hit a ceiling from a defect leakage standpoint. It seems as if
an invisible hurdle is preventing the QA team from achieving its true potential - deploying defect free systems.
"So what are we not seeing in this picture?"While tool based automation or customized test automation is definitely beneficial in lowering defects, it is an option
which requires significant investments. Hence organizations, with their budgets stretched thin, are constantly on the
lookout for inexpensive options. Maximizing the performance of existing QA teams is often the 'low hanging fruit'. In an
attempt to squeeze the maximum out of the QA teams, organizations often overlook a few extraneous factors that play
havoc with the team's testing effectiveness. Without an eye for these factors, they can go unnoticed and continue to
bleed the organization.
Based on our extensive and varied experience in test engagements, we have identified a few factors which we believe
can help QA teams unlock their true potential. Of course before we start we would like to quote an old saying, "To improve
the condition we need to first start measuring it". In line with this quote, we recommend organizations begin by collecting
metrics that indicate the current health of their QA programs. Most companies miss this basic, but critical point in
improving processes. Once we have measured the current state of the QA programs, we are all set to further analyze the
shortcomings and maximize the QA performance of teams -
How many releases does the application go through in a year?This provides a good indication of whether sufficient time is being allocated for every QA iteration. Across engagements
we have seen clients plan from as many as 12 releases in a year to as few as 3 in a year. It is essential for IT teams to plan
the number of releases in combination with business teams, such that only releases which are necessitated due to
business needs are included. Allowing the QA team to 'huddle and regroup', before the next iteration, is extremely important
and the time needed for QA iterations must not to be viewed solely as test script execution time. Root cause analysis, test
script updates based on requirements in the beginning, and UAT result analysis at the end of the test cycle are crucial
steps in the QA process too and demand equal attention. Through these steps the QA team is able to refine its processes
and prevent a recurrence of defects. Frequent releases compromise the effectiveness of these crucial steps and curtail
the feedback loop, limiting the improvement in defect leakage. Hence, every application should have an optimum number
of releases in a year and this should not be exceeded.
The best way to avoid having more than the optimal number of releases in a year, is to educate the business team on the
pros and cons of increasing or decreasing the release frequency. It is absolutely essential that the business and QA team
agree upon a plan keeping in consideration the resources available to the QA team.
How many 'code drops' are allowed per release?
Often frequent changes to the requirements result in increased instances of 'code drops' within a release. At times the
frequency of code drops are not even planned making it almost impossible for QA teams to effectively test before a fresh
code drop occurs. The solution lies in defining requirements freeze dates, post which no new change requests should be
accommodated.
Is there a large variation in the size of every release?The frequency of a release by itself will not set off any alarms and it needs to be considered in conjunction with the size of
the release. The size of the release directly impacts the number of enhancements to be made to test scripts, some of them
permanent, which need to be diligently incorporated into the QA plan. Further, releases which modify the base or common
functionalities of a system need to be viewed as large releases even if their enhancement time is not large. This is
because such releases have sweeping implications on the system and improper assessment of the size of the release from
a QA standpoint and can lead to heightened levels of defect leakage. Further, variations in size of the release leads to
variations in the size of the QA team which makes effective knowledge management a huge challenge. Hence we strongly
recommend that business and IT teams plan uniform sized releases as much as possible. This can be done by prioritizing
and limiting the change requests (CRs) which will help in reducing defect leakage rates and better management of the QA
team.
How process oriented is the development team and how early is the QA team engaged in the SDLC?
While the defect detection during the testing phase is the onus of the QA team, the team in charge of the development
also plays a significant role in the performance of the QA team. Clear sign-offs during the development process and
waterfall execution of lifecycles help provide the QA team unambiguous points of entry into the development process.
Further even though the role of QA team starts much later in SDLC, the involvement of QA teams in the requirement phase
can make them far more effective. The distinct advantage gained by having a better view of the system enhancements to
be built, allows the QA team to plan their tasks more effectively
Are you providing the QA team with an interference free environment?QA teams need environments; viz. independent servers, database instance, test data, etc. that demand investments on a
continuous basis from the organization. However organizations are faced with the twin dilemma of reducing costs and
simultaneously ensuring that sufficient investments in IT infrastructure have been made. In the bargain QA teams are not
always provided with an independent environment for testing. Dedicated test environments ensure that the QA execution
plans are not upset and testing proceeds without interference from external factors.
Have you staffed the QA team with the right onsite-offshore leverage?
In a highly globalized world where offshore teams are a given, the onsite-offshore ratio for a QA team determines the
efficiency of the QA processes. Maintaining a low onsite presence creates bottlenecks. Consequently the reviews of the
enhancement documents (or change requests), test script vetting with the UAT team, etc. suffer. At the same time, a big
onsite team may not always help realise the cost benefits of an offshore model either. Hence it is recommended that QA
teams begin with a 25-30% onsite staff and gradually target aggressive onsite ratios as low as 15-20%.
How well do you manage knowledge erosion?
This is especially important for a large and complex application. QA teams often struggle because of knowledge erosion
associated with planned or unplanned attrition in team. In teams where staffing size fluctuates either due to varying size
of releases or due to attrition, it is important to retain a core group of experts as the base team. An extended team, built
around this base group, can then be engaged or released depending on the demands of the project. Of course, while such
a model would help manage knowledge erosion to a large extent, it would need to be bolstered further by strong Knowledge
Management (KM) practices.
Finally, while the above mentioned factors can help improve QA performance, it is important to track the QA program using
objective metrics. Some of the metrics which we believe need to be tracked closely are:
a) Defect Leakage: This measures the capability of the QA team in detecting defects/bugs. It is important to track this
number accurately, assigning specific reasons for each defect. All issues reported during QA may not necessarily be
defects. Categorizing the defects as "Valid Defects", "Not a Defect", "Duplicate", "Changes in Specifications" and
"Recommendations" help identify the true performance of the QA team. The unsaid part, of course, is that there needs to
be a robust defect tracking system already in place for formal recording, tracking and subsequent analysis.
b) Release frequency: This indicates the number of releases that an application is subjected to in a normal year.
c) Code drops per release: Refers to the number of builds during a release.
d) Schedule/Effort Adherence: Measures how well the QA team is performing vis-à-vis the planned
milestones and bandwidth of resources.
e) 'Not-a-bug' percentage: It indicates the number of defects logged by the QA team that are not defects, as a percentage
of the total reported defects. This metric is very important since it is an indication of the unproductive effort expended by
the development team in investigating a non-defect. It is also an indication of the need to update the documentation /
improve test scripts
The environmental factors discussed in this document, while hidden from the immediate field of view of the team, greatly
influence the outcome of the QA efforts. Hence they have to be objectively assessed and tracked to understand the influence
they exercise on QA performance. By learning from them and adopting appropriate measures, QA teams can be helped to
realize their true performance potential.

Concept testing

It is important to make a distinction between the different types of testing applied at different stages of the development process. This helps the development team to understand the purpose of each test and consider how data is to be captured.

Description
Different testing methods will have different objectives, approaches and types of modelling. Four general types of testing are described in more detail:

Exploratory tests
Assessment tests
Validation tests
Comparison tests
ISO 9000 tests are also briefly summarised.


Exploratory testsCarried out early in the development process during the fuzzy front end, when the problem is still being defined and potential solutions are being considered, preferably once the development team has a good understanding of the user profile and customer needs. The objective of the exploratory test is to examine and explore the potential of preliminary design concepts and answer some basic questions, including:

What do the users think about using the concept?
Does the basic functionality have value to the user? Is the user interface appropriate and operable?
How does the user feel about the concept?
Are our assumptions about customer requirements correct?
Have we misunderstood any requirements?
This type of early analysis of concepts is potentially the most critical of all types of prototyping and evaluation, for if the development is based on faulty assumptions or misunderstanding about the needs of the users, then problems are almost inevitable later on. Data collection will tend to be qualitative based on observation, interview and discussion with the target audience. Ideally, the customer should be asked to use the product without training or prompting, to assess the intuitiveness of controls and instructions. Some quantitative measures may be appropriate, such as time to perform tasks, number of failures or errors.

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Assessment testsWhile the exploratory test aims to explore the appropriateness of a number of potentially competing solutions, the assessment test digs into more detail with a preferred solution at a slightly later stage of development. The main aim of an assessment test is to ensure that assumptions remain relevant and that more detailed and specific design choices are appropriate. The assessment test will tend to focus on the usability or or level of functionality offered and in some cases, may be appropriate for evaluating early levels of performance. Assuming that the right concept has been chosen, then the assessment test aims to ensure that it has been implemented effectively and answer more detailed questions, such as:

Is the concept usable?
Does the concept satisfy all user needs?
How does the user use the product and could it be more effective?
How will it be assembled and tested and could this be achieved in a better way?
Can the user complete all tasks as intended?
Assessment testing typically requires more complex or detailed models than the exploratory test. A combination of analytical models, simulations and working mock ups (not necessarily with final appearance or full tooling) will be used.

The evaluation process is likely to be relatively informal, including both internal and external stakeholders. Data will typically be qualitative and based on observation, discussion and structured interview. The study should aim to understand why users responds in the way that they do to the concept.

Validation testsThe validation test is normally conducted late in the development process to ensure that all of the product design goals have been met. This may include usability, performance, reliability, maintainability, assembly methods and robustness. Validation tests normally aim to evaluate actual functionality and performance, as is expected in the production version and so activities should be performed in full and not simply walked through.

It is probable that the validation test is the first opportunity to evaluate all of the component elements of the product together, although elements may have been tested individually already. Thus, the product should be as near to representing the final item as possible, including packaging, documentation and production processes. Also included within validation tests will be any formal evaluation required for certification, safety or legislative purposes. Compared to an assessment test, there is a much greater emphasis on experimental rigour and consistency. It may be preferable for evaluation to be carried out independently from the design team, but with team input on developing standards and measurement criteria.

Data from a validation test is likely to be quantitative, based on measurement of performance. Normally, this is carried out against some benchmark of expected performance. Usability issues may be scored in terms of speed, accuracy or rate of use, but should always be quantified. Issues such as desirability may be measured in terms of preference or user ranking. Data should also be formally recorded, with any failures to comply with expected performance logged and appropriate corrective action determined.

Comparison testsA comparison test may be performed at any stage of the design process, to compare a concept, product or product element against some alternative. This alternative could be an existing solution, a competitive offering or an alternative design solution. Comparison testing could include the capturing of both performance and preference data for each solution. The comparison test is used to establish a preference, determine superiority or understand the advantages and disadvantages of different designs.

ISO 9000 tests
ISO 9000 defines a number of test activities:

· Design review
A design review is a set of activities whose purpose is to evaluate how well the results of a design will meet all quality requirements. During the course of this review, problems must be identified and necessary actions proposed.

· Design verification
Design verification is a process whose purpose is to examine design and development outputs and to use objective evidence to confirm that outputs meet design and development input requirements.

· Design validationDesign validation is a process whose purpose is to examine resulting products and to use objective evidence to confirm that these products meet user needs.

Difference between Latent and Masked Defect.

Latent bug is an existing uncovered or unidentified bug in a system for a period of time. The bug may have one or more versions of the software and might be identified after its release.

The problems will not cause the damage currently, but wait to reveal themselves at a later time.

Masked defect hides the other defect, which is not detected at a given point of time. It means there is an existing defect that is not caused for reproducing another defect.

How to test software requirements specification (SRS)?

Do you know “Most of the bugs in software are due to incomplete or inaccurate functional requirements?” The software code, doesn’t matter how well it’s written, can’t do anything if there are ambiguities in requirements.

It’s better to catch the requirement ambiguities and fix them in early development life cycle. Cost of fixing the bug after completion of development or product release is too high. So it’s important to have requirement analysis and catch these incorrect requirements before design specifications and project implementation phases of SDLC.

How to measure functional software requirement specification (SRS) documents?
Well, we need to define some standard tests to measure the requirements. Once each requirement is passed through these tests you can evaluate and freeze the functional requirements.

Let’s take an example. You are working on a web based application. Requirement is as follows:
“Web application should be able to serve the user queries as early as possible”

How will you freeze the requirement in this case?
What will be your requirement satisfaction criteria? To get the answer, ask this question to stakeholders: How much response time is ok for you?
If they say, we will accept the response if it’s within 2 seconds, then this is your requirement measure. Freeze this requirement and carry the same procedure for next requirement.

We just learned how to measure the requirements and freeze those in design, implementation and testing phases.

Now let’s take other example. I was working on a web based project. Client (stakeholders) specified the project requirements for initial phase of the project development. My manager circulated all the requirements in the team for review. When we started discussion on these requirements, we were just shocked! Everyone was having his or her own conception about the requirements. We found lot of ambiguities in the ‘terms’ specified in requirement documents, which later on sent to client for review/clarification.

Client used many ambiguous terms, which were having many different meanings, making it difficult to analyze the exact meaning. The next version of the requirement doc from client was clear enough to freeze for design phase.

From this example we learned “Requirements should be clear and consistent”

Next criteria for testing the requirements specification is “Discover missing requirements”

Many times project designers don’t get clear idea about specific modules and they simply assume some requirements while design phase. Any requirement should not be based on assumptions. Requirements should be complete, covering each and every aspect of the system under development.

Specifications should state both type of requirements i.e. what system should do and what should not.

Generally I use my own method to uncover the unspecified requirements. When I read the software requirements specification document (SRS), I note down my own understanding of the requirements that are specified, plus other requirements SRS document should supposed to cover. This helps me to ask the questions about unspecified requirements making it clearer.

For checking the requirements completeness, divide requirements in three sections, ‘Must implement’ requirements, requirements those are not specified but are ‘assumed’ and third type is ‘imagination’ type of requirements. Check if all type of requirements are addressed before software design phase.

Check if the requirements are related to the project goal.
Some times stakeholders have their own expertise, which they expect to come in system under development. They don’t think if that requirement is relevant to project in hand. Make sure to identify such requirements. Try to avoid the irrelevant requirements in first phase of the project development cycle. If not possible ask the questions to stakeholders: why you want to implement this specific requirement? This will describe the particular requirement in detail making it easier for designing the system considering the future scope.

But how to decide the requirements are relevant or not?Simple answer: Set the project goal and ask this question: If not implementing this requirement will cause any problem achieving our specified goal? If not, then this is irrelevant requirement. Ask the stakeholders if they really want to implement these types of requirements.

In short requirements specification (SRS) doc should address following:
Project functionality (What should be done and what should not)
Software, Hardware interfaces and user interface
System Correctness, Security and performance criteria
Implementation issues (risks) if any

Conclusion:
I have covered all aspects of requirement measurement. To be specific about requirements, I will summarize requirement testing in one sentence:
“Requirements should be clear and specific with no uncertainty, requirements should be measurable in terms of specific values, requirements should be testable having some evaluation criteria for each requirement, and requirements should be complete, without any contradictions”

Testing should start at requirement phase to avoid further requirement related bugs. Communicate more and more with your stakeholder to clarify all the requirements before starting project design and implementation.

Do you have any experience testing software requirements?

Test Case Point Analysis

1. INTRODUCTION

Effective software project estimation is one of the most challenging and important activity in software project life-cycle. On time project delivery cannot be achieved without an accurate and reliable estimate. Estimates play a vital role in all phases of the software development life-cycle.
There are many popular models for estimation in vogue today. But none of the models determine the efforts needed for separate phases of the SDLC. Organizations specializing in niche areas such as providing only testing services need an estimation model that can accurately determine the size of the Application Under Test (AUT) and in turn the efforts needed to execute it. Today’s e-business applications impose new challenges for software testing. Some of the commonly known challenges are complex application scenarios, extensive third party integration, crunched testing life cycles and increased security concerns. These factors inherently make testing of e-business application far more complex and critical than conventional software testing. In no other projects is performance testing more critical than in web-based projects. Underestimating a testing project leads to under-staffing it (resulting in staff burnout), under-scoping the
quality assurance effort (running the risk of low quality deliverables), and setting too short a schedule (resulting in loss of credibility as deadlines are missed). Traditionally, estimation of efforts for testing has been more of a ballpark percentage of the rest of the development life cycle phases. This approach to estimation is more prone to errors and carries a bigger risk of delaying the launch deadlines.
TCP analysis is an approach for doing an accurate estimation of functional testing projects. This approach emphasizes on key testing factors that determine the complexity of the entire testing
cycle and gives us a way of translating test creation efforts to test execution efforts, which is very useful for regression testing estimation. In other words Test Case Points is a way of representing the efforts involved in testing projects.

2. TCP METHODOLOGY

As stated above, TCP analysis generates test efforts for separate testing activities. This is essential because testing projects fall under four different models. Though in practice, most testing projects are a combination of the four execution models stated below.

Model I – Test Case Generation
This includes designing well-defined test cases. The deliverables here are only the test cases.

Model II – Automated Script Generation
This execution model includes only automating the test cases using an automated test tool. The
deliverables include the tool generated scripts

Model III – Manual Test Execution
This execution model only involves executing the test cases already designed and reporting the
defects.

Model IV – Automated Test Execution
This phase includes the execution of the automated scripts and reporting the defects.

TCP Analysis uses a 7-step process consisting of the following stages:

1. Identify Use Cases
2. Identify Test Cases
3. Determine TCP for Test Case Generation
4. Determine TCP for Automation
5. Determine TCP for Manual Execution
6. Determine TCP for Automated Execution
7. Determine Total TCP

Given below is an overview of different phases as applied to the four project execution models.Each phase is explained in detail in subsequent sections. Determine TCP for Test Case Generation To determine the TCP for Test Case Generation, first determine the complexity of the Test Cases. Some test cases may be more complex due to the inherent functionality being tested. The complexity will be an indicator of the number of TCPs for the test case.

Calculate test case generation complexity based on the factors given below.

Sl.No Test Case Generation Complexity Factors Weights
1 The number of steps in the case –
Assuming that test cases are atomic and that they test only one
condition, the number of steps will be an indicator of the complexity. 2
2 Interface with other Test Cases
This usually involves calling other test cases from this use case. Ex.
Address Book lookup from Compose Mail 1
3 Establishing a baseline result
e.g. Testing an EMI Calculator would involve validating the formulae
used.
This would typically be complex 3

Note: The above given weights are just sample weights and should not be mistaken for
benchmarks.
– Determine the complexity based on the following table:
Number of Steps Weight
<5 1
5-10 2
>10 3

* The standard weights range from 0 to 3.
0 —-> Not Applicable
1 —-> Simple
2 —-> Average
3 —-> Complex
Calculate the sum of the above weights and determine the complexity of each test case.
Calculate the Rough Test Case Points for test generation by using the table below.
Test Case Type Complexity Weights Test Case Points
Simple < 9 6
Average < 10-16 8
Complex >16 12

Calculate Test Case Points by using the below formulae
Rough TCP-G = ( # of Simple Test Cases X 6 ) + ( # of Average Test Cases X 8 ) + ( # of
Complex Test Cases X 12 )
The TCP estimates above might be affected by certain application level parameters. For
example, if the AUT is a vertical portal (e.g. Insurance), then creating test cases would involve an
understanding of the insurance business. This might increase the effort required. To factor the
impact of such parameters, we will increase the TCP by an Adjustment Factor. Some of the
Application level parameters that might have an influence on the TCP are listed in the table
below:
Sl. No. Factors Adjusted Weight
1 Design Complexity 0.1
2 Integration with other
devices such as WAP
enabled devices, etc. 0.1
1 Multi-lingual Support 0.05
4 Total Factor 0.25

The weights in the table are only indicative and will be subject to the application for which
estimation is being carried out.
Adjustment Factor = 1 + Total Factor = 1 + 0.25 = 1.25
Each of these factors is scored based on their influence on the system being counted. The
resulting score will increase the Unadjusted Test Case Point count. This calculation provides us
with the Test Case Point Generation count.
TCP-G = Rough TCP -Generation X Adjustment Factor
2.1 Determine TCP for Automation

From the list of Test Cases derived from Section 2.2, identify the test cases that are good
candidates for automation. Some test cases save a lot of effort if done manually and is not worth
automating. On the other hand, some test cases cannot be automated because the test tool does
not support the feature being tested.
Certain cases are straightforward and are quite easy to automate whereas certain cases involving
dynamic data are difficult to automate. Next, determine the test case automation complexity
based on the factors given below.
Sl.No Test Case Automation Complexity Factors Weights
1 Interface with other Test Cases
This usually involves calling other test cases from this use case. Ex.
Address Book lookup from Compose Mail 2
2 Interface with external application
This is interaction with an application that is outside the system boundary.
Ex. Credit Card validations through an independent gateway 0
3 External Application Validation
This involves testing of third party applications to validate the use case.
Ex. Checking Word, PDF reports generated by the system 0
4 Data Driven
This is usually helpful for testing the use case with positive and negative
data. Ex. User Registration 2
5 Links
# of links to be tested for broken/orphaned links. Typically, dynamic lists
like catalog items are rated as complex. 1
6 Numerical Computations
This involves validation of arithmetical calculations.
e.g. Testing an EMI Calculator would involve validating the formulae used.
This would typically be complex. 1
7 Check Points
This involves modifying the test scripts to check for validations.
Ex. Page titles, buttons, labels, null values, max char, numeric etc 1
8 Database Check Points
This involves cross checking the database values for integrity.
Ex. Check database after user registration for proper values. 0
9 UI ComponentsThese are usually Applets, ActiveX etc 0

Note: The above given weights are just sample weights and should not be mistaken for
benchmarks.
* The standard weights range from 0 to 3.
0 —-> Not Applicable
1 —-> Simple
2 —-> Average
3 —-> Complex
Calculate the sum of the above weights and determine the complexity of each test case.
From the complexity, calculate the Test Case Points for test automation by using the table below.
Test Case Type Complexity Weights Test Case Points
Simple < 9 6
Average < 10-16 8
Complex >16 12

TCP-A (Single Scenario) = ( # of Simple Test Cases X 6 ) + ( # of Average Test Cases X 8 ) + (
# of Complex Test Cases X 12 )
e-biz applications need to be tested on various configurations because they can be accessed
from anywhere and from an uncontrolled environment. A scenario is basically a combination of a
browser, operating system and hardware.
Sometimes, the application by its nature of usage might demand testing on different scenarios.
The TCPs identified above need to be modified to factor the impact of multiple scenarios.
The extra effort needed can be obtained by answering the following two questions:
Ø Can the scripts generated for a single scenario be run on multiple scenarios?
Ø Do the scripts generated for a single scenario need to be re-generated because the
automation tool does not support a scenario?
To arrive at the additional Test Case Points, we will identify the test cases (from section 3.2) that
need to be re-generated for every additional scenario.
The TCP-A (single scenario) will be added for every scenario for which regeneration is required.
For example, assume that the test case checks for an applet condition in Internet Explorer 5.0
and Windows NT and the TCP -A comes out to be 6. Further, the test tool does not support the
same script for Netscape Navigator 4.7 and Solaris. So the script needs to be regenerated. In
this case, the Total TCP -A comes out to be 12 for the 2 different scripts.
2.2 Determine TCP for Manual Execution

To determine the TCPs for Manual Execution, first calculate the manual test case execution complexity based on the factors given below.
Sl.No Manual Execution Complexity Factors Weights
1 Pre-conditions
This usually involves setting up the test data. It also includes
the steps needed before starting the execution. E.g. to test
whether the printing facility is working fine, the pre-conditions
would include opening the application, inserting a record into
the database, generating a report and then checking the
printing facility. 2
2 Steps in the Test Case
If the steps themselves are complex, the manual execution
effort will increase. Please refer to the legend below to
determine the complexity factor. 1
3 Verification
The verification itself might be complex. For example,
checking the actual result might itself involve many steps.
Let’s say in a test case, we need to check the server logs, it
might need opening up the log analyzer and verifying the
statistics. 2

Note: The above given weights are just sample weights and should not be mistaken for benchmarks.
Number of Steps Weight
<5 1
5-10 2
>10 3

The standard weights range from 0 to 3.
0 —-> Not Applicable
1 —-> Simple
2 —-> Average
3 —-> Complex
Calculate the sum of the above weights and determine the complexity of each test case.
Calculate the Test Case Points for manual execution by using the table below.
Test Case Type Complexity Weights Test Case Points
Simple < 9 6
Average < 10-16 8
Complex >16 12

TCP-ME = ( # of Simple Test Cases X 6 ) + ( # of Average Test Cases X 8 ) + ( # of Complex
Test Cases X 12 )
Test Cases need to be manually executed in all the scenarios. To arrive at the additional Test
Case Points, the TCP -ME (single scenario) will be added for every scenario for which manual
execution is required.
2.3 Determine TCP for Automated Execution

To determine the TCP for Automated Execution, calculate the automation test case execution complexity based on the factors given below.

Note: The above given weights are just sample weights and should not be mistaken for
benchmarks.
The standard weights range from 0 to 3.
0 —-> Not Applicable
1 —-> Simple
2 —-> Average
3 —-> Complex
Calculate the sum of the above weights and determine the complexity of each test case.
Calculate the Test Case Points for automated execution by using the table below.
Sl.No Test Case Complexity Factors Weights
1 Pre-conditions
This usually involves setting up the test data. It also includes
the steps needed before starting the execution. E.g. to test
whether the printing facility is working fine, the pre-conditions
would include opening the application, inserting a record into
the database, generating a report and then checking the
printing facility. 2
Test Case Type Complexity Weights Test Case Points
Simple < 9 6
Average < 10-16 8
Complex >16 12

TCP-AE = ( # of Simple Test Cases X 6 ) + ( # of Average Test Cases X 8 ) + ( # of Complex Test
Cases X 12 )
2.4 Calculate Total TCP

The Total TCP is computed by summing up the individual TCPs for Test Case Generation, Test Automation and Test Execution.
TCP-T = TCP-G + TCP-A + TCP-ME + TCP-AE
The total TCP is indicative of the size of the testing project.
3. EFFORT CALCULATION

To translate the Test Case Points into the total person months involved, based on your prior experience estimate the number of test case points per person month.