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We’ve all been there. We receive a software drop from development that is ostensibly ready for testing then we spend the next three weeks trying to install that software and run it. The reasons why we can’t install, let alone run, the software cover the gamut from missing files and rogue DLLs to database structure incompatibilities and out-and-out bugs. It’s a battle that not only tries our patience but also burns up our test time: You know, the measly few weeks they give you for testing when you should have months.

We’ve all heard of the various types of testingunit, integration, system, compatibility, regression, acceptance, and so on; it is not, however, always clear which of these belong in development and which belong in testing. Even more to the point, it is not clear what test determines whether the software is ready to graduate from development and enter the test organization.

That’s what the Build Verification Test (BVT) is for. If you don’t have one, get one. It will make the difference between doing your job and doing someone else’s.

What is a BVT?

The BVT establishes that the software has sufficient integrity to be tested in the first place. That is, it confirms that the following are true: The software has all its required components, those components are properly linked together, they are accessible, and they are functional. The BVT should be designed to cover the system from beginning to end, but not in depth. When designing a BVT, think inch deep, mile wide,” and you get the idea.

The BVT is sometimes referred to as a smoke test, a nickname that has its roots in the early days of hardware testing. Back then, testers applied electric power to a hardware device to see if it would smoke, spark, or burst into flames. With a BVT, testers are not looking for smoke but for fundamental problems that prevent the software from operating or from performing a critical task. In many organizations, the development team creates smoke tests to ensure that new code has not broken the daily or weekly build.

In my experience, though, the BVT should be something more than a test for dramatic failure. It should ensure that major functions are present and operating as expected. Just because the software doesn’t blow up doesn’t mean it is ready to gosometimes the most dangerous errors occur quietly, without fanfare, such as a failure to make critical calculations or update the database. Moreover, the test organization should develop the BVT, not development, for the same reason you can’t issue your own diploma: If you don’t know what you don’t know, you won’t know it.

What a BVT is For

The test organization has to determine what constitutes an acceptable level of software quality to support the test process. This should not be an exhaustive test, but neither should it be cursory. The best approach is to select one test case out of each category or functional area, usually a positive case that will cause the program to create, retrieve, or update data. This focus on data will assure that the database or other data sources are in sync with the software, and that the test has touched most, if not all, of the underlying tables and files. This process should also invoke middleware and other components that all have to be in harmony.

In addition to making sure you have a test-worthy build, however, the BVT can serve anotherin some cases, more criticalpurpose. It is an embarrassing but undeniable fact that so-called hot fixes are a fact of life in some companies. A hot fix is a correction for a defect so severe that it cannot wait for a regular release cyclewhich also usually means it doesn’t have time to be tested. In that case, the BVT may be all that stands between correcting and exacerbating an already bad situation.

When used for hot fixes, though, the BVT should be applied to an installed system, not a new one, since the software will be shipped to an existing customer. Database conversions, whether of structure or content, can wreak major havoc and cause the loss or corruption of data. Because of this, it’s a good idea to perform a two-level BVT, one for a clean install and one for an existing one.

What It Means

The greatest benefit of a BVT is that it draws a clear line between the testing that is expected from development and that which is expected from the testing organization. Testers can’t cover for inadequate development test practices. That’s unreasonable and unworkable. Every issue found in testing that should have been removed in development takes away the scarce time and resources needed for testing.

While this may sound like a slam against development, it isn’t. In fact, it is a criticism of the rampant lack of definition as to what types of testing are required in development. By creating and executing a BVT, you establish an objective standard. If that standard is not met, at least you know where to look for answers.

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Difference Between System Testing And Acceptance Testing

Testing is a major step for the successful delivery of any application. On the basis of the level of execution of testing, we can classify testing into two categories − System Testing and Acceptance Testing. Read this article to learn more about system testing and acceptance testing and how they are different from each other.

What is System Testing?

System Testing is a type of testing which is used to validate the functionality of a developed software product. It is also known as black box testing. This testing involves the behavioral as well as functional testing of the software product as per the documents provided for testing. System testing is generally performed by developers as well as testers.

System testing includes both the module basis testing and integration testing with multiple modules. System testing is generally executed after the development and before the delivery of the software product. Since the system testing is performed by a group of testers, thus it would contain more negative test cases. The system testing uses all the possible dummy inputs for testing purpose.

What is Acceptance Testing?

Acceptance testing is a type of testing that validates if a developed software product meets the client’s requirements or not. It covers the testing of parameters provided by the client before development of the software product.

Acceptance testing is generally performed by testers as well as clients. Therefore, acceptance testing covers both alpha and beta testing. This testing is generally performed after the system testing and before the delivery of the software product. Unlike system testing, acceptance testing uses all the possible random values of the inputs for testing.

Difference between System Testing and Acceptance Testing

The following are some basic differences between System Testing and Acceptance Testing −


System Testing

Acceptance Testing


System Testing is the testing which basically validates the functionality of product developed. Also this testing covers the behavioral as well as functional testing as per the document provided to the tester for testing.

Acceptance testing is the type of testing which is used to check whether the software meets the customer requirements or not. It basically covers the acceptance criteria provided by the client before development of product.

Performed By

In general, System testing is done by developers as well as testers.

Acceptance testing is done by testers, stakeholders as well as clients.


System Testing covers the module basis testing as well as integration testing with multiple modules.

Acceptance testing covers alpha and beta testing which means it is done at both sides i.e. at the side of delivery as well as side of client.

Order of execution

System testing is generally done after development and before delivery of the product.

Acceptance testing is done after System testing and delivery of the product.

Test Cases

In System Testing since it is performed by group of testers, it would contain more negative test cases.

Acceptance Testing contains more of positive test cases


All the possible dummy inputs are used in System Testing.

In acceptance testing, all the possible random inputs are used.


The most significant difference between the two types of testing is that System Testing covers the behavioral as well as functional testing of the product as per the document provided, whereas Acceptance Testing covers the acceptance criteria provided by the client before development of the product.

Cyclomatic Complexity In Software Testing (Example)

What is McCabe’s Cyclomatic Complexity?

Cyclomatic Complexity in Software Testing is a testing metric used for measuring the complexity of a software program. It is a quantitative measure of independent paths in the source code of a software program. Cyclomatic complexity can be calculated by using control flow graphs or with respect to functions, modules, methods or classes within a software program.

Independent path is defined as a path that has at least one edge which has not been traversed before in any other paths.

This metric was developed by Thomas J. McCabe in 1976 and it is based on a control flow representation of the program. Control flow depicts a program as a graph which consists of Nodes and Edges.

In the graph, Nodes represent processing tasks while edges represent control flow between the nodes.

Flow graph notation for a program:

Flow Graph notation for a program defines several nodes connected through the edges. Below are Flow diagrams for statements like if-else, While, until and normal sequence of flow.

How to Calculate Cyclomatic Complexity

Mathematical representation:

Mathematically, it is set of independent paths through the graph diagram. The Code complexity of the program can be defined using the formula –

V(G) = E - N + 2


E – Number of edges

N – Number of Nodes

V (G) = P + 1

Where P = Number of predicate nodes (node that contains condition)

Example –

i = 0; n=4; while (i<n-1) do j = i + 1; while (j<n) do if A[i]<A[j] then swap(A[i], A[j]); end do; j=j+1; end do;

Flow graph for this program will be

Computing mathematically,

V(G) = 9 – 7 + 2 = 4

V(G) = 3 + 1 = 4 (Condition nodes are 1,2 and 3 nodes)

Basis Set – A set of possible execution path of a program

1, 7

1, 2, 6, 1, 7

1, 2, 3, 4, 5, 2, 6, 1, 7

1, 2, 3, 5, 2, 6, 1, 7

Properties of Cyclomatic complexity:

Following are the properties of Cyclomatic complexity:

V (G) is the maximum number of independent paths in the graph

G will have one path if V (G) = 1

Minimize complexity to 10

How this metric is useful for software testing?

Basis Path testing is one of White box technique and it guarantees to execute atleast one statement during testing. It checks each linearly independent path through the program, which means number test cases, will be equivalent to the cyclomatic complexity of the program.

This metric is useful because of properties of Cyclomatic complexity (M) –

M can be number of test cases to achieve branch coverage (Upper Bound)

M can be number of paths through the graphs. (Lower Bound)

Consider this example –

If (Condition 1) Statement 1 Else Statement 2 If (Condition 2) Statement 3 Else Statement 4

Cyclomatic Complexity for this program will be 8-7+2=3.

As complexity has calculated as 3, three test cases are necessary to the complete path coverage for the above example.

Steps to be followed:

The following steps should be followed for computing Cyclomatic complexity and test cases design.

Step 1 – Construction of graph with nodes and edges from the code

Step 2 – Identification of independent paths

Step 3 – Cyclomatic Complexity Calculation

Step 4 – Design of Test Cases

Once the basic set is formed, TEST CASES should be written to execute all the paths.

More on V (G):

Cyclomatic complexity can be calculated manually if the program is small. Automated tools need to be used if the program is very complex as this involves more flow graphs. Based on complexity number, team can conclude on the actions that need to be taken for measure.

Following table gives overview on the complexity number and corresponding meaning of v (G):

Complexity Number Meaning


High Testability

Cost and Effort is less


Medium Testability

Cost and effort is Medium


Low Testability

Cost and Effort are high

Very high Cost and Effort

Tools for Cyclomatic Complexity calculation:

Many tools are available for determining the complexity of the application. Some complexity calculation tools are used for specific technologies. Complexity can be found by the number of decision points in a program. The decision points are if, for, for-each, while, do, catch, case statements in a source code.

Examples of tools are

OCLint – Static code analyzer for C and Related Languages

Reflector Add In – Code metrics for .NET assemblies

GMetrics – Find metrics in Java related applications

Uses of Cyclomatic Complexity:

Cyclomatic Complexity can prove to be very helpful in

Helps developers and testers to determine independent path executions

Developers can assure that all the paths have been tested atleast once

Helps us to focus more on the uncovered paths

Improve code coverage in Software Engineering

Evaluate the risk associated with the application or program

Using these metrics early in the cycle reduces more risk of the program


Cyclomatic Complexity is software metric useful for structured or White Box Testing. It is mainly used to evaluate complexity of a program. If the decision points are more, then complexity of the program is more. If program has high complexity number, then probability of error is high with increased time for maintenance and trouble shoot.

Difference Between Static And Dynamic Testing

As we know that testing is the most important stage in the process of delivery of any application or software. Testing not only validates the quality of an application but also provides an opportunity to the developer to improve its product.

Every application is being developed in some high or low level language which means some code has been written for its development so on the basis of execution of code written for the application there is classification of testing namely static testing and dynamic testing.

In this article, we will discuss all the important differences between static testing and dynamic testing. Let’s start with some basics of static testing and dynamic testing.

What is Static Testing?

Static testing is the testing in which code written for application is not executed during testing phase and only review of code is performed and basis on which defects and code quality has been examined. As its name implies, static testing performs the static verification of the code. It targets the assessment of program code and documentation.

Static testing is generally performed before the compilation of the code. Static testing involves two types of testing techniques namely review and static analysis.

What is Dynamic Testing?

Dynamic testing there is execution of code written for the application and then defects and application behavior has been examined. Dynamic testing is performed to examine the behavior of the software based on dynamic inputs. The dynamic testing of a software product is performed after compilation of the software code.

Dynamic testing is classified into two types namely – white box testing and black testing. In software testing techniques, the dynamic testing is one of the essential tools for detecting any security threats. The dynamic testing increases quality of the product.

Difference between Static and Dynamic Testing

The following table highlights the major differences between Static Testing and Dynamic Testing −

Parameter Static Testing Dynamic Testing

Definition Static testing is the testing in which code written for application is not executed during testing phase and only review of code is performed and basis on which defects and code quality has been examined. Dynamic testing there is execution of code written for the application and then defects and application behavior has been examined.

Nature of testing As name states static testing does the static verification process in which the requirement and corresponding written code has been verified. Dynamic testing does the validation process which examines the expected behavior of the application based on dynamic inputs provided to the application.

Testing target Static testing targets to the assessment of code and documentation. Dynamic testing targets the runtime bugs/bottlenecks in the software system.

Prerequisite For static testing, a checklist of application process and documentation is required. For dynamic testing, test cases for execution are to be developed.

Stage of testing Static testing generally get performed before compilation of code Dynamic testing mostly performed after compilation of code.

Cost to Company In Static testing, the cost of finding the defects and fixing them is less. Also, the Return on Investment is high because static testing is carried out at an early stage of development. In case of Dynamic testing, the cost of finding and fixing the defects is high. Also the Return on Investment (RoI) is low because this process is carried out after the development phase.


The most important difference between static and dynamic testing is that the static testing checks the defects in software without actual execution of the software code and it analyzes the static behavior of the software, while dynamic testing is used to analyze the dynamic behavior of the software.

Approval Workflow Setup And Testing In Power Automate

In this tutorial, we’ll learn how to set up an in Power Automate. There are times when flows can’t be fully automated and might require some user intervention.

Approvals are very useful because we can automate some part of the flow, and split the part that needs user intervention into its own separate flow.

For example, there could be a flow for an expense request. It will be processed through regular channels. However, someone still needs to look at it just to make sure that the amount makes sense. If it’s over a thousand dollars, the request should undergo approval before the flow continues.

Then there’s a condition where if the item includes an issue or problem, it creates a Trello card. Otherwise, it retweets the tweet.

However, we might not want to retweet it until an actual human being approves it. Hence, we’ll put an approval right before it gets to the retweet step. The automated flow will actually pause when it gets to that point, and will continue once the user approves it. 

Let’s now create an approval workflow. First, we need to add an action.

There are three different actions for this connector. You can either create an approval, wait for an approval, or combine those two things together (Start and wait for an approval). We’ll choose the second option because we want to both start and wait for an approval to go through.

There are four available approval options for this action. Let’s choose the First to respond option.

I’m going to copy and paste the email address that I used for this organization. Make sure you use valid emails.

We can also assign multiple people. We can have 20 reviewers and only one of them needs to actually approve them.

Let’s also add space.

After that, type in “Name: ”. Again, make sure to add a comma right after that. 

We want the retweet to actually happen after the approval. So, we need to switch the order of the two actions. Move the Retweet action below the “Start and wait for an approval” action.

The condition is if the outcome is equal to “Approve”.

If the condition is true, then it should be retweeted. Therefore, let’s move the Retweet action inside the “If yes” path.

If it’s not approved, we want a record for that. So, let’s add an action within the “If no” path.

Then choose “Create a card”.

Choose Tasks for the Board Id field and Issues for the Parent List Id.

For the Card name field, type “Tweet has been rejected”.

As for the Description field, choose “Tweet text” within the Dynamic content tab.

Let’s open Twitter and tweet “Power Automate is AMAZEBALLS!”.

Again, this usually takes a few moments because it’s constantly querying Twitter. After the flow runs successfully, go to Approvals.

We’ll then see that there’s a new approval request. 

Let’s now analyze how our flow worked. The process has gone through the condition.

The tweet doesn’t have the word “Issue”. That’s why a Trello card wasn’t created yet. Instead, it waited for an approval.

As we can see, it contains all the arguments that we required such as the tweet text and the name of the person who tweeted it.

Because of that, let’s approve the tweet.

Let’s now go back to our flow. As we can see, the condition was true. Therefore, the action (Retweet) within the “If yes” path was executed.

Once we check our Twitter, we’ll see that the tweet was already retweeted.

And that’s how you can manage approvals in Power Automate mobile app.

We’ve successfully created an approval workflow in Power Automate. Keep in mind that flows don’t need to be fully automated, and you don’t have to worry about being uncertain of what might happen in your flows. You can just include user intervention into your flows and make them more customized and logical. 

If you’re a consultant and you’re going to deploy Power Automate into a company, a lot of these approval processes and tools are built-in for you. This makes the user experience in Power Automate so much better, especially when users can do everything from both their desktop and phone.

All the best,


Google: A/B Testing Can Delay A Site Migration

According to Mueller, conducting an A/B test during a site migration can cause the process to take significantly longer than it needs to.

This was a topic that came up in a Google Webmaster Central hangout when someone asked the following question:

“What would be the impact if an A/B test is running while a large-scale URL migration launches? All old URLs properly 301 redirect to new URLs, but Googlebot is sometimes redirected to test URLs while the migration goes live… could this impact how signals are passed and potentially lengthen the time a site sees of volatility?”

Mueller had a lot to say in response to this question, providing a much longer answer than he usually does in these hangouts.

It’s best to refrain from conducting large sets of A/B tests during a site migration, Mueller says.

During a site migration Google will try to understand what the general picture is with a website.

The better Google can understand what’s going on with a site, the easier it will be to move all algorithmic signals from the old site to the new site.

If there’s a “fancy” A/B test going on during a site migration then it will take longer for Google to understand what’s happening.

In particular, he recommends checking for the following things:

Make sure that all the URLs from the old site are redirecting to the new version.

Make sure there are no crawl errors from URLs that should be available.

Don’t have anything blocked by chúng tôi that wasn’t blocked by chúng tôi before.

Once again — give a clean signal that you’re doing a straightforward move from one version of a site to another. Otherwise it will take a lot longer to process.

You can see Mueller’s full response in the video below, starting at the 11:17 mark.

“I don’t know if this would cause big issues in the bigger picture. Usually when you’re doing A/B testing it’s a small group of URLs within a website, it’s not that all URLs of your website are doing this A/B testing thing where sometimes we see one URL and sometimes we get redirected to another one.

For small sets of URLs I don’t see the problem there. For bigger sets of A/B tests I would try to minimize this as much as possible. The reason being is that, especially for site migrations, when you’re moving to a different domain, when you’re changing within the same domain, when you’re changing to HTTPS, with these things we try to understand what the general picture is with the website.

Is the website moving from one domain to another? Is the website moving to HTTPS, for example. And the clearer we can get a picture of what is actually happening here, the clearer we can apply kind of an algorithm to move all of the signals we have from this website to the new version of that website.

So if during a site migration you’re doing some kind of fancy A/B testing, and sometimes we see redirects to one direction, and sometimes we see site migration redirects, then that can certainly throw us off in the bigger picture and can delay the overall move from one version to another.

So especially when it comes to site migrations, the cleaner you can set that up the more you can make sure that all the URLs are redirecting to the new version, that you don’t have crawl errors from URLs which should be available, that you don’t have things blocked by chúng tôi that weren’t blocked by chúng tôi before, all of these things.

The cleaner you can get the more likely we’ll be able to say “oh, this is obviously a move from here to here, therefore we don’t even need to worry about all of the details we can just shuffle everything over.”

Whereas if we look at it and we say “well, maybe it’s a move, or maybe they’re doing something fancy, maybe we should take a bit more time to figure out what exactly is happening here.” That can end up causing a site migration to take significantly longer than it really needs to take.”

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