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In the topic on object manipulation, we have covered the concept of creating and updating Internal objects and persisting the data/information as per the existing structure of the object. When an object needs to be manipulated with a particular data/information, it needs to be reflected against the predefined storage name associated with it.

The storage name is same as the method name available with the object. In real life scenarios, as per business requirements the data storage requirements may not be limited only to the methods already available within the Objects. The Tally user may require additional fields on the screen apart from the ones available in the Default Tally. For example, while entering a Sales Voucher, the dispatch details should store Vehicle details also. In such scenarios, the need to store or persist additional information as a part of existing Internal Object becomes mandatory.

integrity of data especially when additional functionalities are incorporated apart from the ones provided in default.

When additional information needs to be stored within the existing internal objects and persisted into the Tally Database, User Defined Fields(UDF) are created. User Defined Fields have a storage component defined by the user. All the valid datatypes available in TDL are applicable for UDF’s also.  A user defined field can be of data type such as Strings, Amount, Quantity, Rate, Number, Logical and Date. For usage and implementation of UDF’s, the following points need to be taken care of:

The UDF must be defined i.e. a storage component needs to be defined with a specific data type. At this point the storage does not have a correlation with an Internal Object.

The field associated with the UDF needs to be in the context of a Data Object. If the data is to be stored in a sub-object in the existing hierarchy of Internal Object, then the field associated with UDF also needs to be in the same sub-object.

The UDFs are defined under the definition [System: UDF]. The datatype and index number must be specified while creating the UDF.

Syntax

[System : UDF]

Where,

Numbers falling between 1 to 9999 and 20001 to 65536 are opened for customisation, and those between 10000 to 20000 are allotted for common development in TSPL. The user can create 65536 UDFs of each data type.

The index numbers 1 to 29 are already used for Default TDL and are as follows:

1 – 29 of data type String

1 – 3 of data type Date and

1 – 2 of data type Number

Example

[System : UDF]

MyUDF 1 : String : 20003

MyUDF 2 : Date   : 20003

In the example, above the UDF MyUDF1 is defined with a String Datatype and MyUDF2 is defined with a Date Datatype. A UDF does not come into existence until some value is stored into it and is attached with an Internal Object. A UDF value can be stored along with an object already existing in the Tally database or to a new object being created for a specific object type. Once the value is stored, it can be accessed and used from the specified level just like an ordinary method.

UDF/storage component and attach it at the data object level to which the field is associated i.e. the field value is stored in the context of current object.

The attribute Storage is used to store the value entered in the field, in the current object context.

Syntax

Where,

Example

[Field : NewField]

Use     : NameField

Storage : MyUDF

As discussed, a UDF is attached to an Internal Object at a particular level in the existing hierarchy structure. Once it is stored, it can be accessed in the same way as an existing internal method.

In the context of the current object, the value of a UDF can be accessed by prefixing $ to the UDF name.

Syntax

Example

[Field: NewField]

Use    : NameField

Set As : $MyUDF

Previously, if the TDL or the TCP was lost or corrupted, then there was no way by which we could know the UDF details like the UDF Number, and hence, the retrieval of data related to the UDF was quite difficult.

Now, an XML attribute ‘Index’, within the UDF ‘List Tag’, has been introduced to help retrieve the original UDF number corresponding to the data available within the Objects associated with it. This UDF number will be available in the Index attribute in the UDF List Tag, even when the TDL is not attached or is unavailable.

Example

Here, the UDF number (1010) is displayed under the ‘Index’ attribute in the UDF List Tag.

that, UDFs can be classified as given below:

Simple UDF

Complex/Compound/Aggregate UDF

A simple UDF is used when a single or multiple values of a specific data type needs to be stored along with the Object specified. A UDF storing a single value of a specific data type can be correlated to a method. For example,  $closingbalance and a UDF storing multiple values of the same data type can be correlated to a simple collection. For example name and address collection.

It can store one or more values of a single data type. A UDF used for storage, stores the values in the context of the object associated at Line/Report level, by default. Only one value is stored in this case.

retrieval for the same.

The following example code snippet demonstrates how a UDF can be made use of to store a single value:

Example

[Report : CompanyVehicles]

Object : Company

.

.

.

[Field : CVeh]

Use     : Name Field

Storage : Vehicle

Unique  : Yes

[System : UDF]

Vehicle : String : 700

using $vehicle.

The object is associated at the Report Level. The value stored in a UDF is in the context of Company Object in this case. The UDF Vehicle stores a single string value.

Multiple values can be entered into a field when the line containing it is repeated in the part over the specified UDF. The storage in the field also specifies the name of the UDF. The implementation and usage of this UDF is exactly like a simple collection.

Syntax

Where,

Example

 Let us consider the example below to understand the storage and retrieval for the same. Since the implementation of a Simple UDF storing multiple values is exactly like a Simple Collection, the repeat attribute of Part definition in this case will be as follows: 

[Part : CompVeh]

Line     : CompVeh

Repeat   : CompVeh  : Vehicle

Break On : $$IsEmpty:$Vehicle

Scroll   : Vertical

[Line: CompVeh]

Field : CVeh

[Field: CVeh] Storage: Vehicle

empty value. All the values entered are stored in the UDF Vehicle and are attached to the Company object associated to the report. Thereafter the values stored in the UDF can be retrieved by using $vehicle in the field contained in the line repeated over the UDF Vehicle.

A simple UDF can store single or multiple values of a specific datatype i.e., it contains single or repeated values of the same data type. In real life business scenarios, this does not suffice the data storage requirements. In order to store composite values of discrete datatypes repeating itself once or multiple times, aggregate UDF can be used.

An aggregate UDF can contain multiple Simple UDFs of different datatypes where the Simple UDF can either be Single or Repeat. It can also contain other aggregate UDFs within it and this nesting can continue upto infinity. This can be correlated with compound collections.

Aggregate UDFs are defined in the same way as Simple UDFs inside the System:UDF definition. The data type to be specified here is Aggregate. The UDF defined using the keyword Aggregate is actually the container for the subcomponents defined thereafter. The subcomponents can be a Simple UDF or another aggregate UDF.

Syntax

[System: UDF]

Where,

Example

A company wants to create and store multiple details of company vehicles. The details required are: Vehicle Number, Brand, Year of Mfg., Purchase Cost, Type of Vehicle, Currently in Service, Sold On date and Sold for Amount.

[System : UDF]

Company Vehicles      : Aggregate : 1000

VVehicle Number       : String : 1000

VBrand                : String : 1001

VYear of Mfg          : Number : 1000

VPurchase Cost        : Amount : 1000

VType of Vehicle      : String : 1002

VCurrently in Service : Logical : 1000

VSold On date         : Date : 1000

VSold for             : Amount : 1001

To store the required details, simple UDFs are defined and to store them as one entity , a UDF of type Aggregate is defined, as shown in the example.

Multiple values of discrete data types can be entered in different fields contained in a line. This line will be repeated over the aggregate UDF and the storages in the fields specify the component UDF’s. Aggregate UDF definition does not associate each component field with the aggregate UDF. The association will take place only when the line is repeated over aggregate UDF and the fields within that stores value into the component UDFs. Since the implementation of Aggregate UDF is exactly like a Compound collection, the repeat attribute of Part definition in this case will be as follows:

Syntax

Where,

Example

[Part : Comp Vehicle]

Line     : Comp VehLn

Repeat   : Comp VehLn : Company Vehicles

BreakOn  : $$IsEmpty:$VBrand

.

.

.

[Field : CMP VBrand]

Use     : Short Name Field

Storage : VBrand

Thereafter, the values stored in the individual UDF’s can be retrieved by using $VBrand, $VVehicleNumber and so on in the fields contained in the line repeated over the aggregate UDF Company Vehicles. The Line is repeated over the Aggregate UDF and the Simple UDFs are entered in the fields.

SubForm is an attribute that is used within a Field definition. It relates to a report (not Form) and can be invoked by a field. This attribute is useful to activate a report within a report, perform the necessary action and return to the report used to invoke the Subform. There is no limit on the number of subforms that can be used at the field level.

Syntax

[Field : Field Name]

Where,

A Sub Form is not associated to the Object at the Report level. An Object associated to the Field in which the Sub Form is defined, gets associated to the Sub Form. A Sub Form will inherit the info object from the Field which appears as a pop-up.

The Bill-wise Details is an example of a Sub Form attribute. This screen is displayed as soon as an amount is entered for a ledger whose Bill-wise Details feature has been activated.

Example

The following code snippet uses a Sub Form to enter the details of bills when the Bill Collection ledger is selected, while entering a Voucher. The values entered in the Sub Form are stored in an Aggregate UDF. This UDF is attached to the object to which the field displaying the Sub Form is associated. Here, it is the Object of a Ledger Entries Collection.

The following code is used to associate a Sub Form to the default Field in a voucher.

[#Field : ACLSLed]

Sub Form : BillDetail : ##SVVoucherType = “Receipt” and $LedgerName = “Bill Collection”

The Name Report for the Subform uses an Aggregate UDF to store the data. A Line is repeated over the Aggregate UDF at the Part level.

[Part : BillDetails]

Scroll      : Vertical

Line        : BillDetailsH, BillDetailsD

Repeat      : BillDetailsD : BAggre

Break After : $$Line=2

The attribute Storage is used for all the fields.

[Field: CustName1]

Use     : Name Field

Storage : CustName

The UDF is defined as follows:

[System : UDF]

CustName : String : 1000

BillNo   : String : 1001

BillAmt  : Amount : 1001

EPrint1  : String : 1002

BAggre   : Aggregate : 1000

The data stored in the Repeat UDFs and Aggregate UDFs are analogous to the Objects in the Collection. This data can be displayed as a table. In order to use the data stored in the UDFs as a table a collection needs to be constructed.

Since, the UDF will always be attached to an existing internal object, the type specification will contain reference to the primary object.

Syntax

Example:

[Collection: CMP Vehicles] Title    : “Company Vehicles”

We have seen in previous examples that the Repeat UDF “Vehicle” stores multiple values of the same data type and is associated with the Company Object. The collection CMP Vehicles is constructed by specifying the type as Vehicle of a Company Object.

The Child of specifies the Company Object identifier which is the current company. Once the collection is defined it can be used in the Table attribute of field definition. So when the cursor is in the defined field the values stored in the UDF will be displayed as popup table.

[Field: EI Vehicles Det] Show Table  : Always

As we know a UDF can be stored at any level in the existing Object hierarchy. In those cases, referring to the UDF data and construction of the collection using the referencing method as above is not possible. In those cases the data corresponding to the UDFs can be gathered only by traversing to the desired level in the hierarchy. The Walk attribute of the collection will be used for the same.

Example

Refer to the example used in using Subforms where the aggregate UDF “BAggre” with components BillNo, BillAmt, etc. are attached at the Ledger Entries level. The source collection is constructed using Vouchers of type “Receipt”

[Collection: Src Bills] Child Of : $$VchTypeReceipt

The BillTable collection walks over the Ledger Entries and then over BAggre UDF and then fetches the methods “BillNo” and “BillAmt”. Format is specified for the methods to be displayed in the Table.

[Collection: BillTable]

Source Collection : SrcBills

Walk              : LedgerEntries, BAggre

Fetch             : BillNo, BillAmt

Format            : $BillNo, 10

Format            : $BillAmt, 20

[#Field: VchNarration]

Table             : BillTable

This can be well utilized by the application developers to enforce business requirements. The validation concept can be used for different purposes like

Each business will have unique organizational structure. Naturally this needs to be reflected in the usage of Tally application. For example, the restricting access of Reports to the Data Entry Person or restricting Data Entry person to create Masters.

To assist the Data Entry operator to enter meaningful information. For Example PF Date of Joining should not be less than Date of Joining.

To enforce the integrity constraints. For example Vouchers having manual numbering with ‘prevent duplicates’, duplication of Voucher numbers are not allowed.

Customized reports can be brought under default security control

The following section discusses about developing TDL level validation with the help of definitions, attributes and built-in functions.

The attribute Validate checks if the given condition is satisfied. Unless the given condition is satisfied, the user cannot move further. In other words, if the given condition for Validate is not satisfied, the cursor remains placed on the current field without moving to the subsequent field. It does not display any error message.

Syntax

Where,

Example

[Field: CMP Name]

Use      : Name Field

Validate : NOT $$IsEmpty:$$Value

Storage  : Name

Style    : Large Bold

In the above code snippet,

The field CMP Name is a field in Default TDL which is used to create/ alter a Company.

Validate stops the cursor from moving forward, unless some value is entered in the current field.

The function, IsEmpty returns a logical value as True, only if the parameter passed to it contains NULL.

The function, Value returns the value entered in the current field.

Thus, the attribute Validate used in the current field, controls the user from leaving the field blank and forces a user input.

This attribute takes a logical value. If it is set to Yes, then the values keyed in the field have to be unique. If the entries are duplicated, an error message, Duplicate Entry pops up. This attribute is useful when a Line is repeated over UDF/Collection, in order to avoid a repetition of values.

Syntax

Example

[!Field: VCHPHYSStockItem]

Table  : Unique Stock Item : $$Line = 1

Table  : Unique Stock Item, EndofList

Unique : Yes

In this code snippet, the field, VCHPHYSStockItem is an optional field in DefTDL which is used in a Physical Stock Voucher. The attribute, Unique avoids the repetition of Stock Item names.

This attribute is similar to the attribute Validate. The only difference is that it flashes a warning message and the cursor moves to the subsequent field. A System Formula is added to display the warning message.

Syntax

Where,

Example

[!Field: VCH NrmlBilledQty]

Notify : NegativeStock : ##VCFGNegativeStock AND @@IsOutwardType AND $$InCreateMode AND +

         $$IsNegative:@@FinalStockTotal

In this code snippet, VCH NrmlBilledQty is a default optional field in DefTDL used in a Voucher. The Notify attribute pops up as a warning message, if the entered quantity for a stock item is more than the available stock and the cursor moves to the subsequent field.

The attribute Control is similar to Notify. The only difference is that it does not allow the user to proceed further after displaying a message. The cursor does not move to the subsequent field.

Syntax

Example

[Field: Employee PFDateOfJoining]

Use       : Uni Date Field

Set As    : If $$IsEmpty:$PFAccountNumber AND $$IsEmpty:$FPFAccountNumber Then “” Else If NOT +

Control   : PFJoiningDateBelowJoinDate:If $$IsEmpty:$PFAccountNumber AND $$IsEmpty: + 

Set Always: Yes

In this code snippet, the field, ‘Employee PFDateOfJoining’ is a default field. The control always makes sure that Date of Joining for a Employee is always less than the Date Provident Fund joining Date.

The difference between the field attributes, Validate, Notify and Control are:

Field Attributes Displays Message Curser Movement Validate No Restricted Notify Yes Not Restricted Control Yes Restricted If the condition specified with Control is not satisfied, then the Form displays an error message while trying to save. The Form cannot be saved until the condition in the attribute Control is fulfilled.

Syntax

Example

[Form: Voucher]

In the example, Voucher is a default Form. While creating a voucher, the attribute, Control does not accept dates beyond the financial period or before beginning of the books.

The attribute, Control restricts the appearance of Menu Items, based on the given condition.

Syntax

Example

[Menu: Quick Setup]

Key Item  : @@locExciseForManufacturer: M:Display: ExciseMfgr QuickSetUp

Control   : @@locExciseForManufacturer: @@IsIndian AND $$IsInventoryOn

In this code snippet, the Menu, Quick Setup is a default definition. The Menu Item, Excise for Manufacturer, will be displayed only if the selected company is having statutory compliance for India Inventory module enabled.

This is a ‘Report’ level attribute which is required to be specified, in case Multiple Objects of the same collection are being added/modified in a Report. It is required specifically in case of multi master creation or alteration.

Syntax

Where,

Example

[Report: Multi Ledger]

Multi Objects: Ledger Under MGroup

The report level attribute Family is useful when the Security Control is enabled for the company. A Report can be made accessible for only a set of user(s) by setting proper rights at security levels.

For this name of the Report needs to be brought under default Collection ‘Report List’. The value specified with the attribute, Family is automatically added to the security list as a pop-up while assigning the rights under Security Control Menu.

Syntax

Where,

Example

[Report:Balance Sheet]

Family : $$Translate:”Balance Sheet”

In this code snippet, the Balance Sheet is added to the Security list. The users having rights to display Balance sheet can only view the Report.

This built-in function checks the permissions for the currently logged in user. This function can be effectively used to enable or disable an interface based on the permissions for the currently logged in user.

Syntax

Where,

Example

[!Menu: Gateway of Tally]

Key Item: @@locAccountsInfo : A : Menu : Accounts Info. : NOT $$IsEmpty:$$SelectedCmps

Control : @@locAccountsInfo :$$Allow:Create:AccountsMasters OR $$Allow:Alter:AccountsMasters +

          OR $$Allow:Display:AccountsMasters

attribute Family at the report definition.

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User Defined Data Types In C++

Introduction to User-Defined Data Types in C++

User Defined Data types in C++ are a type for representing data. The data type will inform the interpreter how the programmer will use the data. A data type can be pre-defined or user-defined. Examples of pre-defined data types are char, int, float, etc.

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Types of User-Defined Data in C++

The types of user-defined data are as follows:

1. Structure

A structure is a collection of various types of related information under one name. The declaration of structure forms a template, and the variables of structures are known as members. All the members of the structure are generally related. The keyword used for the structure is “struct.”

For example, a structure for student identity having ‘name,’ ‘class,’ ‘roll_number,’ and ‘address’ as a member can be created as follows:

struct stud_id { char name[20]; int class; int roll_number; char address[30]; };

This is called the declaration of the structure, and it is terminated by a semicolon (;). The memory is not allocated, while the structure declaration is delegated when specifying the same. The structure definition creates structure variables and gives storage space for them. One can define the variables of the structure as follows:

stud_id I1, I2;

Where I1 and I2 are the two variables of stud_id. After defining the structure, one can access its members using the dot operator as follows:

I1.roll_number will access the roll number of I1

I2.class will access the class of I2

Example:

struct stud_id { int class, roll_number; }; int main() { struct stud_id entries[10];   // Create an array of structures entries[0].class = 4;           // Access array members entries[0].roll_number = 20; cout <<entries[0].class << ", " << entries[0].roll_number; return 0; } 2. Array

An array is a collection of homogeneous data and must be defined before using it for the storage of information. The array can be defined as follows:

int marks[10]

The above statement defined an integer-type array named “marks” that can store the marks of 10 students. After creating an array, one can access any element of an array by writing the name of an array, followed by its index. For example, to access the 5th element from marks, the syntax will be:

marks[5]

It will give the marks stored at the 5th location of an array. An array can be one-dimensional, two-dimensional, or multi-dimensional, depending upon the specification of elements.

int main() { int marks[10]; marks[0] = 5; marks[2] = -10; cout<<marks[0], marks[2]); return 0; } 3. Union

Just like structures, the union also contains members of different data types. The main difference is that the union saves memory because union members share the same storage area. In contrast, members of the structure have their unique storage areas. One declares the unions with the keyword “union,” as shown below:

union employee { int id; double salary; char name[20]; }

One can define the variable of the union as:

union employee E;

To access the members of the union, one can use the dot operator as follows:

E.salary; 4. Class

A class is an essential feature of object-oriented programming languages like C++. A class is a group of objects with the same operations and attributes. To declare a class, use the keyword “class” and follow this syntax:

{ private: Data_members; Member_functions; public: Data_members; Member_functions; };

In this, the names of data members should be different from member functions. There are two access specifiers for classes that define the scope of the members of a class. These are private and public. The member specified as private can only be accessed by the member functions of that particular class. The members, defined as the public, have internal and external access to the class. The members with no specifier are private by default. We refer to the objects that belong to a class as instances of the class. The syntax for creating an object of a class is as follows:

Example:

class kids { public:                char name[10];   int age; void print()         { cout<<"name is:"<< name; } } Int main { Kids k;                    k.name="Eash"; k.print(); return 0; } 5. Enumeration

Enumeration is specified by using the keyword “enum.” It is a set of named integer constants that define all the possible values a variable of that particular type can have. For example, the enumeration of the week can have names of all the seven days of the week as shown below:

enum week_days{sun, mon, tues, wed, thur, fri, sat}; int main() { enum week_days d; d = mon; cout << d; return 0; } 6. Pointer

A Pointer is a user-defined data type that creates variables for holding the memory address of other variables. If one variable holds the address of another variable, we say that the first variable is the pointer to the other variable. The syntax for the same is:

type *ptr_name;

Here type is any data type of the pointer, and ptr_name is the pointer’s name.

Example:

void main() { int a = 10; int *p;   // pointer variable is declared p = &a;  cout<<"Value at p = ",<<p); cout<<"Value at variable a = “,<<a); cout<<"Value at *p = ",<< *p); } 7. Typedef

Using the keyword “typedef,” one can define new data type names to the existing ones. Its syntax is:

typedef float balance;

When we create a new name for the float data type, such as “balance,” we can use it to declare variables of the float type. The use of a typedef can make the code not only easy to read but also to port to a new machine as well.

Example:

typedef  int score; int main() { score s1, s2; s1 = 80; cout << " " << b1; return 0; } Conclusion

C++ supports different kinds of user-defined data types, as discussed above. Many other data types exist, such as functions, references, etc. Their use makes programming much easier, and they also help us to club different types of data in a single variable.

Recommended Articles

This is our guide to User-Defined Data Types in C++. Here are some further articles to learn more:

What Are Smart Contracts In Blockchain?

This article was published as a part of the Data Science Blogathon.

Introduction

Source: Image by Gerd Altmann from Pixabay

Smart contracts are blockchain-ba computer programs that activate at predefined times. In most cases, they are used to eliminate the need for a third party during the execution of a contract, allowing all parties to the deal to know for sure what will happen without wasting time waiting on a mediator. They may also auto process by automatically causing one action to lead to another.

Smart contracts are one of the most potentially useful applications of blockchain technology, and they can facilitate the transfer of everything from bitcoin and fia base and cannot be altered because they operate on a decentralized network such as blockchain.

Smart contract transactions are processed by the blockchain, allowing them to be sent automatically and without a third party. This indicates that no one can be relied upon!

There are no trust issues because the transactions only occur when the conditions of the agreement are met, and there is no third party.

When were Smart Contracts Invented?

Nick Szabo, a cryptographer, developed the idea in 1994 that recording contracts as computer code would be possible. As soon as certain conditions are met, the contents of this contract will become effective. Future reliance on reliable third-party firms, such as banks, may no longer be required due to this strategy.

How Does Smart Contract Work?

The logic of smart contracts is reduced to a series of “if/when/then” statements stored in the blockchain’s underlying code. When the prerequisites are satisfied and validated, the activities are carried out by a group of computers. For example, this can include distributing payments to the right people, registering a car, sending out alerts, or issuing a penalty. When the transaction is complete, the blockchain is updated. Thus, the deal is final and cannot be altered.

1. Predefined Contract

Collaboration opportunities and desired results are identified by several parties, who then come to mutually beneficial agreements that may include the sharing of business processes, the exchange of assets, etc. The participants in a smart contract may choose to begin it. Collaboratively, the parties to the smart contract determine the conditions under which the contract should execute the intended actions.

2. Business Logic

A computer program is built that, after the conditional parameters have been satisfied, will run without any further intervention. Simple events include the authorization of a payment, the acceptance of a package, or reaching a certain threshold for the reading on a utility meter. It’s possible that more complicated processes, such as automatically releasing an insurance payout or assessing the value of a derivative financial instrument, may be encoded using more sophisticated logic.

3. Execution

After the contract is approved, it is next deployed on an already existing blockchain or distributed ledger architecture. When an agreement is formed between the parties about authentication and verification in a blockchain iteration, the code is then run, and the outputs are documented for compliance and verification. This happens anytime the consensus is obtained.

4. Settlement

After the execution of smart contracts, each node on the network will update its ledger to reflect the current status of the network. After the record has been uploaded to the blockchain network and confirmed, it can no longer be edited; it will only operate in add mode from that point forward.

How are Smart Contracts Created in Blockchain?

We’ll outline the essential processes of smart contract creation to offer you an overview. We won’t cover the coding, but we’ll explain how to write a smart contract. Creating a smart contract concept, developing, coding, and deploying the code require multiple steps. Let’s Start!

1. Concept

The first thing you need to do is decide exactly what you want your contract to include. What challenges are you looking to have solved by an intelligent and automated computer program? Having a clear idea of the result you want the coding process to produce is an excellent way to get started. This is true regardless of the demand you are trying to satisfy.

2. Code

3. Testing

After coding, test your smart contract. Testing is crucial. Once on the mainnet, you can’t change smart contract errors. Tenets (or test chains) are used to test smart contracts. These don’t need cryptocurrency, thankfully. Use dummy currency to test your contract and receive fast feedback. Testing tools are available. Remix simplifies testing and deployment with integrated environments.

4. Compile

Smart contracts need to be built before they can be used. What this means is making a JSON file out of your contract’s code so that it can be read by any old web app. Let’s look at a smart contract built on Ethereum as an example. After being written in Solidity, your contracts are compiled into the bytecode of the Ethereum Virtual Machine (EVM), which makes them compatible with any network that runs the EVM.

5. Deploy

Finally, deploy your contract on your network. Smart contracts are executed and transactions are made utilizing actual crypto when deployed. You’ll specify wallets and incentives. After this step, your deployed contract will launch, and all programmed functionalities will activate when the criteria are satisfied.

6. Follow-up Checks

After you’ve signed, deployed, and put your contract on the mainnet, you’ll need to do your due diligence to make sure it’s working the way it should. This includes keeping an eye on your wallets to make sure they show the right amounts at the right times. Additionally, now is the time to handle any storage concerns and maintenance responsibilities.

Benefits of Smart Contract

1. Speed, Efficiency, and Accuracy

As soon as the precondition is satisfied, the contract is instantly put into effect. There is no need for documentation or time-consuming reconciliation when dealing with smart contracts since they are digital and automated.

2. Trust and Transparency

Since there is no middleman and all participants have access to the same encrypted logs of transactions, it is impossible to tell whether any data has been changed for malicious purposes.

3. Security

Because the records of transactions in a blockchain are encrypted, it is very difficult to get into them. In addition, in a distributed ledger, each record is tied to the record that came before it and the record that will come after it. This means that hackers would need to modify the whole chain to change a single record.

4. Savings

Because they remove the need for middlemen in business transactions, smart contracts result in cost savings. In addition, the amount of money spent on the documentation is negligible to nonexistent.

Industrial Applications of Smart Contracts 1. Insurance

Processing claims is an annual expense for the insurance business that runs into tens of millions of dollars. In addition to this, the company loses millions of dollars every year due to bogus claims.

In addition to being able to support the original insurance policy, smart contracts assist in many other ways that help enhance the process of claim processing. They might permit mistake checks and establish payment amounts based on a set of criteria that takes into consideration the kind of policy that was carried out by the person or organization in question.

In the future term, smart contracts might be used in conjunction with Internet of Things-enabled automobiles to enable pay-as-you-go insurance coverage and the prompt activation of claims after an accident. This would be possible via the usage of Internet of Things-equipped vehicles.

2. Government

Smart contracts are useful because they facilitate the automation of procedures. The government might likely benefit much from this kind of activity management. Transferring property in a transparent and orderly way is essential to the process of registering land ownership. Smart contracts may be used to facilitate this. Using it will also increase transparency across the system while decreasing the cost of audits.

One more example of a use case, this time for the government, including applications such as electronic record filing, electronic elections, and digital identities that we covered before.

3. Supply Chain Management

Smart contracts may greatly benefit the supply chain by streamlining transactions and reducing human error. It may be put to use, for instance, to keep tabs on products through the supply chain. When a company uses supply chains enabled by smart contracts, it may get a far more accurate count of its stock.

It also benefits the company in ways unrelated to supply chain management. Smart contracts also imply less time spent verifying and tracking down lost or stolen items. However, to make it work, the institutions will need to integrate new machinery into their supply chain, such as sensors. What’s more, it serves as a demonstration of a smart contract in action.

4. Medical Research

Advantages comparable to this will accrue to the medical research sector. For starters, medical records and other sensitive information might be safely moved between departments or research institutes using blockchain technology for encryption.

Patient’s medical data must be kept confidential since many of them are dealing with personal health issues they’d rather not discuss. Medical research companies also have a huge amount of sensitive information, like test results and drug formulas, that needs to be kept safe.

If they have to give any of this information to a third party, smart contracts could keep it safe while it’s being sent. Only one use of smart contracts on the blockchain shows promise for improving healthcare research significantly.

5. NFTs and Gaming

The global gaming industry is an ecosystem that is worth $100 billion and continues to grow quickly. However, the way that value is created and shared within the industry can be unfair at times. Game designers make and release their games, and people pay money to play and interact with them. Because of this, a one-way flow of value is maintained, in which players must spend actual cash to get access to more in-game assets and gameplay options. On the other hand, if blockchain technology is used in games, players may be able to get more out of their in-game investments.

6. Decentralized Finance

Decentralized finance (DeFi) dApps are a strong alternative to traditional financial services, and they are becoming more popular because blockchain and smart contract technology are trustworthy, can’t be changed, and are clear. DeFi dApps offer similar services to banking and financial services, like lending, borrowing, trading, and many other financial services. They also offer completely new types of goods and decentralized business models that may give consumers a lot of value and profit. Smart contracts make things more clear, which could make it easier for people all over the world to get into the financial services industry through dApps.

7. Real Estate

The use of smart contracts can be used to record who owns a piece of property. The use of smart contracts is not only quicker but also more cost-efficient, which makes them a far superior option to the systems that are now in place. It also means that they can be used to register ownership of buildings, land, phones, watches, and other similar items.

When it comes to the real estate market, using smart contracts could eliminate the need for more expensive services like those of lawyers and real estate agents. Because of this new technology, for the first time in history, sellers now have the power to manage the transaction on their own, without any assistance from a third party.

8. Escrow

Escrow services let the parties to a contract keep valuables safe while the contract is in effect. The payer initiates the process to disburse the money for this purpose. However, if a smart contract is used, the whole process may be automated after the service provider has submitted its work and it has been authenticated.

Smart contracts may be especially helpful for freelance platforms that keep an escrow fund. Many businesses now rely on these digital agreements.

9. Identity Management

Existing technologies that are used to prevent crimes such as identity theft aren’t as successful as they should be since many of them do not provide the owner ultimate control of his data and the information that they choose to send out. This makes it easier for fraudsters to capture someone’s identity.

Individuals will have full control over their data and be able to share it however they want, thanks to digital identifier smart contracts that are built on distributed ledger technologies. This will make the system more secure and make it less likely that data will be mismanaged or stolen.

The protection of identities and the ease of conducting KYC checks are both helped by smart contracts.

10. Trade

Companies throughout the world are building a trustworthy network to facilitate international commerce. It is a blockchain-based platform that uses standardized trading rules and streamlined trading options to reduce friction and risk, make trading easier, and give businesses and institutions that use it more ways to trade.

11. Mortgage

There is potential for the application of intelligent contracts within the mortgage industry. It makes it possible to automate mortgages, which is convenient for the buyer as well as the owner. To make sure that all of these things happen, smart contracts need to be made based on the mortgage agreement. Once this is accomplished, the smart contracts may be activated, and the further steps of the process can be carried out in an automated fashion.

Limitations of Smart Contracts

Due to their inability to submit HTTP queries, smart contracts cannot get information on “real-world” occurrences. The importance of consensus for security and decentralization might be jeopardized by reliance on external information.

The maximum size of a smart contract is another constraint. If a smart contract exceeds 24KB, it will run out of gas.

Future of Smart Contracts

Undoubtedly, the future of relatively simple contracts that can be drafted and executed right away, provided preconditions are met is smart requirements-powered contracts. Numerous smart contract platforms will alter the supply chain and customer relationships while saving businesses across the world time and money. As a result, little human involvement will free individuals and important decision-makers from time-consuming administration and bureaucracy, allowing them to focus on their main duties. This is due to the smart contract’s ability to fill the gap.

Smart contracts are already used regularly by several banks and insurance organizations. As a result, smart contracts are already being evaluated in practical settings and will soon be incorporated into our everyday activities. Despite the aforementioned justification, there is still a long way to go until everything is governed by smart contracts.

Conclusion

The use of blockchain technology allows for the creation of smart contracts, which is one of its most appealing applications. Conside as conditional programming. Blockchain functions as a form of database, validating that transactions have taken place, while smart contracts carry out pre-determined conditions. As you can see, the use of smart contracts has the potential to make the world a better place while eliminating the need for commissions. It has the potential to cut down on things like fraud and delays, as well as total costs.

Key Takeaways

Smart contracts are one of the uses of blockchain technology with the biggest potential utility. They are apps that automatically execute for business automation or a contract between two parties written in computer code.

The blockchain processes transactions, including smart contracts, enabling them to be delivered instantly and without a middleman. Because they are digital and automated, smart contracts eliminate the need for intermediaries in commercial transactions. Since the transaction records in a blockchain are encrypted, it is very difficult to hack into them.

A smart contract is akin to a computer program that uses if-then logic. It verifies that transactions have taken place, while smart contracts carry out predetermined conditions. If a smart contract’s size exceeds 24 bytes, it will run out of gas.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Related

Openstack: We’re Living In A Software Defined Economy

ATLANTA. OpenStack is not just about the cloud, it’s about connecting the entire planet in a way that has never been done before. At the OpenStack Summit here, Jonathan Bryce, Executive Director of the OpenStack Foundation, kicked off the event with a visionary keynote about the role of the cloud in the modern world and how OpenStack fits in to this scenario.

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Bryce added that it’s critical to have both people and the systems that actually enable an agile infrastructure. He added that without an agile infrastructure it’s like having a high performance car without wheels.

“In the last five years seen we have seen a lot of growth in the agile infrastructure space,” Bryce said. “We have gone from a model where the only place to get agile infrastructure was Amazon and OpenStack has now opened that up.”

“OpenStack is the engine of the software defined economy,” Bryce added.

Going a step forward, OpenStack provides a different consumption model for technology than other technologies do. Bryce noted that that the modern enterprise is no longer simply passively consuming software that some vendor sells on a two- or three-year cycle.

“Users are getting involved and really making a difference,” Bryce said.

As has been the case for at least the last three OpenStack Summit events, Bryce brought some users on stage. Glenn Ferguson, Head of Cloud Enablement at Wells Fargo, explained why his company is using OpenStack and why he’s involved.

“This is a community and it is in my best interest to be at conferences and let the community know what we’re doing,” Ferguson said. “The collective mass of the community is what everyone benefits from.”

Chris Launey, director, cloud services and architecture at the Walt Disney Company, said he’s involved with OpenStack because he wants to empower people when they come to work with technology. Launey noted that Disney was able to build an OpenStack pilot deployment in only three months.

Troy Toman, Vice President, Cloud Infrastructure Products at Rackspace, followed Bryce with a keynote that compared OpenStack to the Rebel Alliance from the Star War movies. “We may not be facing the Death Star, but we do have our challenges,” Toman said.

While showing a picture of Imperial Stormtroopers, Toman said that proprietary non-OpenStack cloud technologies have armies of software developers and they are relentless.

Going a step beyond the Star Wars metaphor, Toman said that OpenStack could one day power a Planetary Scale cloud operating system. OpenStack now has some federation capabilities, though he noted that there is still work to be done to improve interopability.

“OpenStack is at a key moment now,” Toman said. “We’re moving to a software defined world that will change the world in ways we can’t imagine.”

Troy Toman, Vice President, Cloud Infrastructure Products at Rackspace

Sean Michael Kerner is a senior editor at Datamation and chúng tôi Follow him on Twitter @TechJournalist

Graphic image courtesy of Shutterstock.

What Are Data Silos And What Problems Do They Cause?

Is your organization having problems with data consistency? Are you getting complaints about incomplete or duplicate data?

You could have data silos bogging down business operations.

It’s a common problem not just for big organizations with multiple departments, but also small businesses that mismanage their data.

To get to the bottom of this sticky situation, you must first understand what data silos are.

What is Data Silos?

As the name suggests, a data silo is like a stockroom of data owned and managed by a single department.

That doesn’t sound so bad — until you realize that data silos are isolated from the rest of the business.

Data silos may occur whenever departments prerogatively acquire new technologies by themselves. Some companies allow this to help business units streamline their operations without involving the upper management.

As a result, the newly-adopted technology may include databases that aren’t natively compatible with existing systems.

Other than that, data silos may also form due to the following reasons:

Business Expansion – Rapidly growing companies assume a speedy stance when deploying new technologies to address their changing needs. This could lead to the creation of new business units and, in turn, siloed databases.

Decentralized Business Units – In large companies, data silos are widely common since departments are often managed independently of each other. As such, creating a more consolidated data infrastructure for the entire organization becomes a tremendous challenge.

Misguidance – In some cases, departments or even individuals willingly create data silos simply because they’re unaware of the implications. Rather, they’re fixated on the idea that they’re free to manage their department’s data as they see fit.

Now that you understand what data silos are, let’s talk about what it means to your business.

Also read:

Top 10 IT Skills in Demand for 2023

Why Data Silos Suck

Having data silos in your organization has numerous, costly consequences.

1. Inaccurate and Inconsistent Data Quality

Data silos can result in out-of-sync, inconsistent data sets between two or more departments.

This can lead to a slew of problems. Customer data may appear erroneous due to different formats, one department’s database may get outdated, and so on.

Due to the isolated nature of data silos, it’s also difficult to track and correct issues related to data quality.

2. Harder to Make Data-Driven Business Decisions

Business decision-makers need all the data they can get to function properly.

But since data silos block access to other departments, decision-makers will be forced to work with incomplete data. Unless, they’re willing to go through a more time-consuming, manual retrieval method.

3. Collaboration Problems

In the world of digital transformation, seamless data management is crucial to the success of interdependent business units.

4. Impact on Profit Margins

Data silos can affect profit margins in different ways.

For one, it has a major impact on an organization’s operational efficiency.

Data silos can also lead to duplicate data — effectively wasting data storage space and forcing the organization to purchase more.

5. Data Security Risks

A business culture that proliferates data silos probably has poor data management and safety protocols.

Employees may be haphazardly storing data on their own through Google Sheets or some cloud storage service. Small teams may also have their own mindset and strategies when it comes to sharing their data.

This inevitably increases the likelihood of cybersecurity breaches as more potential attack vectors are introduced to the data infrastructure.

How to Break Down Data Silos

It’s clear that data silos are detrimental to business operations — affecting not just data quality but also profit margins.

The question now is, what can businesses do about them?

Here are some of the ways businesses are uprooting data silos:

1. Data Warehouses

data.

It works as a single data storage environment especially configured for BI (Business Intelligence) and analytics purposes.

Data warehouses are also different from data lakes, which is another form of a unified data repository.

Unlike data lakes, data warehouses have organization. Incoming data will be cleaned, transformed, and saved in a structured interface — ready to be pulled whenever needed.

2. Better Data Management Culture

Remember, some departments could be keeping their data to themselves as means of boosting their performance. This incentivizes the idea of building data silos — unless departments are made well aware of the consequences of data silos.

That’s why every arm of your organization should be aboard your new data management initiative. Make it the entire organization’s job to ensure that each department is complying with data protocols.

3. Data Integration

Data integration methods, namely ETL (Extract, Transform, and Load), can help organizations deal with data silos upfront.

It works in precisely three steps: extracting data from multiple systems, cleaning data for consistency, and loading it to a target database.

Conclusion

Data silos can drain your organization’s productivity, IT budget, and team collaboration. And now that businesses depend on tons of data for day-to-day operations, the urgency to address data silos is greater than ever.

Remember, it all starts with a culture shift towards better, cleaner data management. Once your isolated departments adopt a more transparent approach to data, your company is ready to use data warehousing or data integration techniques to break down data silos — once and for all.

What Are Assertions In Selenium With Python?

There are assertions in Selenium which are verification or checkpoints for the test case. In the absence of an assertion, there is no option of determining if a test case has failed or not.

Sometimes, we may use the conditional statements like if – else and so on and then print the result of pass/ fail in the console. But that can only solve the problem of checking logs and not for actual reporting.

Thus assertion is used for generating test execution reports. In case, our test case passes all the test steps, the assertions do not impact the test cases in any way, however if the test case fails, it is reported.

A test case can contain numerous methods of assertions. Some of them can accept values of all data types and some will only have numeric values. The different types of assertions are listed below −

assertEqual – This assertion has two parameters. A comparison is done between the first and second parameter. In case both of them match, the test case is considered a pass; else the test case is failed.

The third parameter of assertEqual is an optional one. It is mostly used for informational purposes for result analysis.

Syntax assertEqual("Tutorialspoint", "Tutorialspoint")

assertNotEqual – This assertion can have more than two parameters. A comparison is done between the first and second parameter. In case both of them do not match, the test case is considered a pass; else the test case is failed.

The third parameter of assertEqual is an optional one. It is mostly used for informational purposes for result analysis.

Syntax assertNotEqual("Tutorials", "Tutorialspoint")

assertTrue – This assertion can handle more than two parameters. A comparison is done between the first and second parameter. In case both of them do match, the test case is considered a pass; else the test case is failed.

The assertTrue assertion allows the use of relational operators for comparison. In these situations, the result is in Boolean (True or False). The final parameter of this assertion can contain informational messages used for result analysis.

Syntax assertTrue((hd="Tutorialspoint") OR (hd="Selenium"),"Matching header")

assertFalse – This assertion can handle more than two parameters. A comparison is done between the first and second parameter. In case both of them do not match, the test case is considered a pass; else the test case is failed.

The assertTrue assertion allows the use of relational operators for comparison. In these situations, the result is in Boolean (True or False). The final parameter of this assertion can contain informational messages used

assertIs – This assertion can handle two parameters. A comparison is done between the first and second parameter. In case both of them match, the test case is considered a pass; else the test case is failed.

The third parameter of assertEqual is an optional one. It is mostly used for informational purposes for result analysis.

Syntax assertIs(Tutorialspoint", "Tutorialspoint", "Both are equal")

assertIsNot – This assertion can handle two parameters. A comparison is done between the first and second parameter. In case both of them do not match, the test case is considered a pass; else the test case is failed. The final parameter of this assertion can contain informational messages used for result analysis.

The final parameter of this assertion can contain informational messages used for result analysis.

Syntax assertIsNot(Tutorialspoint", "Tutorials", "Both are not equal")

assertIsNone – This assertion can handle a parameter. It is used to check if the value provided is none. If the result is equal to none, the test case is considered a pass; else the test case is failed.

The final parameter of this assertion can contain informational messages used for result analysis.

Syntax assertIsNone( result, "The result is equal to none")

assertIsNotNone – This assertion can handle a parameter. It is used to check if the value provided is none. If the result is not equal to none, the test case is considered a pass; else the test case is failed.

The final parameter of this assertion can contain informational messages used for result analysis.

Syntax assertIsNotNone( r, "The result is not equal to none")

assertIn – This assertion has two parameters. It is used to check if the first parameter exists in the second parameter. If the item is present in the second element, the test case is considered a pass; else the test case is failed. The third parameter of assertIs is an optional one. It is mostly used for informational purposes for result analysis.

This type of assertion is mostly used in set, list, tuple and dictionary.

Syntax s = set(["PLSQL", "Selenium", "Jmeter"]) assertIn("Selenium", s, " Selenium is present in set s")

assertNotIn – This assertion has two parameters. It is used to check if the first parameter exists in the second parameter. If the item is not present in the second element, the test case is considered a pass; else the test case is failed.

The third parameter of assertIs is an optional one. It is mostly used for informational purposes for result analysis.

This type of assertion is mostly used in set, list, tuple and dictionary.

Syntax s = set(["PLSQL", "Selenium", "Jmeter"]) assertIn("Oracle", s, " Oracle is not present in set s")

assertIsInstance – This assertion has two parameters. It is used to check if the given object( in the first parameter) is an instance of the class( in the second parameter). If yes, the test case is considered a pass; else the test case is failed.

The third parameter of assertIs is an optional one. It is mostly used for informational purposes for result analysis.

Syntax Cl1 c = new Cl1() assertIsInstance(c, Cl1, " c is an instance of Cl1")

assertIsNotInstance – This assertion has two parameters. It is used to check if the given object( in the first parameter) is an instance of the class( in the second parameter). If no, the test case is considered a pass; else the test case is failed.

The third parameter of assertIs is an optional one. It is mostly used for informational purposes for result analysis.

Syntax Cl1 c = new Cl1() assertIsInstance(d, Cl1, " d is not an instance of Cl1")

assertListEqual – This assertion has two parameters. It is used to check if the two lists mentioned in the parameter are similar or not. If there is any missing or not similar element, it is printed as an error message.

assertTupleEqual – This assertion has two parameters. It is used to check if the two tuples mentioned in the parameter are similar or not. If there is any missing or not similar element, it is printed as an error message.

assertSetEqual – This assertion has two parameters. It is used to check if the two sets mentioned in the parameter are similar or not. If there is any missing or not similar element, it is printed as an error message.

assertDictEqual – This assertion has two parameters. It is used to check if the two dictionaries mentioned in the parameter are similar or not. If there is any missing or not similar element, it is printed as an error message.

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