SYSTEMS AND METHODS FOR ALLOCATING ONE OR MORE RESOURCES TO DEVELOP AN APPLICATION

1 . A method for allocating one or more resources to develop an application, the method comprising:

receiving one or more requests from a customer, wherein the one or more requests include information about the application to be developed;

generating a buildcard based on the received one or more requests; and

allocating the one or more resources to develop the application based on the generated buildcard.

2 . The method of claim 1 , wherein allocating the one or more resources to develop the application comprises:

classifying one or more past requests as regular past requests and irregular past requests;

determining one or more events associated with the irregular past requests to develop one or more applications;

predicting a current demand related to each of the one or more events to develop the one or more applications based on the irregular past requests;

reserving a group of resources based on the predicted current demand to develop the one or more applications; and

allocating the one or more resources to develop the application based on the generated buildcard and the reserved group of resources.

3 . The method of claim 2 , wherein the one or more past requests are classified by inputting the one or more past requests to a classification-based machine learning model, and wherein the one or more events are determined by processing the irregular past requests to a natural language processing (NLP) model.

4 . The method of claim 1 , wherein allocating the one or more resources to develop the application comprises:

retrieving one or more features associated with the application to be developed based on the generated buildcard; and

classifying the one or more features as standard features and non-standard features.

5 . The method of claim 4 , further comprises:

determining a dependency score between each pair of the non-standard features of the retrieved one or more features;

predicting resource units required for development of each non-standard feature using one or more historical features developed;

estimating a development score for each non-standard feature included in one or more ongoing applications; and

allocating the one or more resources to develop the application further based on the determined dependency score, the predicted resource units, and the estimated development score.

6 . The method of claim 5 , wherein estimating the development score for each non-standard feature included in the one or more ongoing applications comprises:

determining a timeline for completing each non-standard feature included in the one or more ongoing applications;

retrieving a current delay in the development of each non-standard feature included in the one or more ongoing applications; and

estimating the development score for each non-standard feature based on the determined timeline and the current delay.

7 . The method of claim 5 , wherein predicting the resource units required for the development of each non-standard feature comprises:

determining one or more additional sub-features in each non-standard feature when compared to one or more sub-features in the standard features;

retrieving an expertise level associated with each of one or more available resources to develop the determined one or more additional sub-features; and

predicting the resource units required for the development of each non-standard feature based on the retrieved expertise level, the determined one or more additional sub-features, and the one or more historical features developed.

8 . A system to allocate one or more resources to develop an application, the system comprises:

a memory; and

a processor coupled to the memory and configured to:

receiving one or more requests from a customer, wherein the one or more requests include information about the application to be developed;

generating a buildcard based on the received one or more requests; and

allocating the one or more resources to develop the application based on the generated buildcard.

9 . The system of claim 8 , wherein to allocate the one or more resources to develop the application, the processor is configured to:

classify one or more past requests as regular past requests and irregular past requests;

determine one or more events associated with the irregular past requests to develop one or more applications;

predict a current demand related to each of the one or more events to develop the one or more applications based on the irregular past requests;

reserve a group of resources based on the predicted current demand to develop the one or more applications; and

allocate the one or more resources to develop the application based on the generated buildcard and the reserved group of resources.

10 . The system of claim 9 , wherein the one or more past requests are classified by inputting the one or more past requests to a classification-based machine learning model, and wherein the one or more events are determined by processing the irregular past requests to a natural language processing (NLP) model.

11 . The system of claim 8 , wherein to allocate the one or more resources to develop the application, the processor is configured to:

retrieve one or more features associated with the application to be developed based on the generated buildcard; and

classify the one or more features as standard features and non-standard features.

12 . The system of claim 11 , wherein the processor is further configured to:

determine a dependency score between each pair of the non-standard features of the retrieved one or more features;

predict resource units required for development of each non-standard feature using one or more historical features developed;

estimate a development score for each non-standard feature included in one or more ongoing applications; and

allocate the one or more resources to develop the application further based on the determined dependency score, the predicted resource units, and the estimated development score.

13 . The system of claim 12 , wherein to estimate the development score for each non-standard feature included in the one or more ongoing applications, the processor is configured to:

determine a timeline for completing each non-standard feature included in the one or more ongoing applications;

retrieve a current delay in the development of each non-standard feature included in the one or more ongoing applications; and

estimate the development score for each non-standard feature based on the determined timeline and the current delay.

14 . The system of claim 12 , wherein to predict the resource units required for the development of each non-standard feature, the processor is configured to:

determine one or more additional sub-features in each non-standard feature when compared to one or more sub-features in the standard features;

retrieve an expertise level associated with each of one or more available resources to develop the determined one or more additional sub-features; and

predict the resource units required for the development of each non-standard feature based on the retrieved expertise level, the determined one or more additional sub-features, and the one or more historical features developed.

15 . A computer readable storage medium having data stored therein representing software executable by a computer, the software comprising instructions that, when executed, cause the computer readable storage medium to perform:

receiving one or more requests from a customer, wherein the one or more requests include information about the application to be developed;

generating a buildcard based on the received one or more requests; and

allocating the one or more resources to develop the application based on the generated buildcard.

16 . The computer readable storage medium of claim 15 , wherein allocating the one or more resources to develop the application comprises:

classifying one or more past requests as regular past requests and irregular past requests;

determining one or more events associated with the irregular past requests to develop one or more applications;

predicting a current demand related to each of the one or more events to develop the one or more applications based on the irregular past requests;

reserving a group of resources based on the predicted current demand to develop the one or more applications; and

allocating the one or more resources to develop the application based on the generated buildcard and the reserved group of resources.

17 . The computer readable storage medium of claim 16 , wherein the one or more past requests are classified by inputting the one or more past requests to a classification-based machine learning model, and wherein the one or more events are determined by processing the irregular past requests to a natural language processing (NLP) model.

18 . The computer readable storage medium of claim 15 , wherein allocating the one or more resources to develop the application comprises:

retrieving one or more features associated with the application to be developed based on the generated buildcard; and

classifying the one or more features as standard features and non-standard features.

19 . The computer readable storage medium of claim 18 , further comprises:

determining a dependency score between each pair of the non-standard features of the retrieved one or more features;

predicting resource units required for development of each non-standard feature using one or more historical features developed;

estimating a development score for each non-standard feature included in one or more ongoing applications; and

allocating the one or more resources to develop the application further based on the determined dependency score, the predicted resource units, and the estimated development score.

20 . The computer readable storage medium of claim 18 , wherein estimating the development score for each non-standard feature included in the one or more ongoing applications comprises:

determining a timeline for completing each non-standard feature included in the one or more ongoing applications;

retrieving a current delay in the development of each non-standard feature included in the one or more ongoing applications; and

estimating the development score for each non-standard feature based on the determined timeline and the current delay.