We claim:
1. A system (100) for optimized processing of requirements data in a software development life cycle, the system (100) comprising:
a memory (108) storing program instructions;
a processor (106) executing instructions stored in the memory (108);
a requirements analysis engine (104) executed by the processor (106) and configured to:
process fetched user story data from a database (112) for determining a first pre-defined parameter associated with the user story by applying a first set of pre-defined rules, wherein the first pre-defined parameter represents persona of the user associated with the user story data;
determine a second pre-defined parameter associated with the user story data by applying a second set of pre¬defined rules, wherein the second pre-defined parameter represents action requirement associated with the user story data for an application;
determine a third pre-defined parameter associated with the user story data by applying a third set of pre-defined rules, wherein the third pre-defined parameter represents action outcome associated with the user story data for the application;
determine a fourth pre-defined parameter associated with the user story data by applying a fourth set of pre¬defined rules, wherein the fourth pre-defined parameter represents atomicity of the user story data;
determine a fifth pre-defined parameter associated with the user story data by applying a fifth set of pre-defined

rules, wherein the fifth pre-defined parameter represents ambiguity in the user story data;
determine a sixth pre-defined parameter associated with the user story data by applying a sixth set of pre-defined rules, wherein the sixth pre-defined parameter represents acceptance criteria for the user story data; and
determine a seventh pre-defined parameter associated with the user story data by applying a seventh set of pre¬defined rules, wherein the seventh pre-defined parameter represents determination of length of the user story data;
and
an output and correction unit (128) configured to render an output in the form of a Requirement Completeness Index (RCI) for the user story data, the RCI is generated based on a cumulative computation of percentage weighted score for each of the pre-defined parameters, and corrective actions are automatically carried out on the user story data based on the generated RCI.
2. The system (100) as claimed in claim 1, wherein the requirements analysis engine (104) comprises a story fetching unit (110) executed by the processor (106) and configured to periodically and incrementally fetch user stories data from external source systems by employing workflow management tools, and wherein the fetched user stories data are stored in the database (112) for later retrieval.
3. The system (100) as claimed in claim 1, wherein the requirements analysis engine (104) comprises a persona identification unit (114) executed by the processor (106) and configured to determine user persona from the user story data by applying the first set of pre-defined rules including processing a user story data format used for user story creation by analyzing one or more regular expressions present

in the user story to determine and validate the user persona associated with the user story data, and wherein the one or more regular expressions are analyzed by applying a pre-built NLP library, the regular expression data present in the user story are determined as Parts of Speech (POS), and wherein a correlation is determined between the data present in the user story data.
4. The system (100) as claimed in claim 3, wherein the user persona is determined from the user story data by applying the first set of pre-defined rules including in the event it is determined by the persona identification unit (114) that the user story format is not determinable from the user story data, then NLP Parts-Of-Speech (POS) parsers are applied by the persona identification unit (114) to identify POS that is present in the user story sentence from the user story data for identifying the user persona, and wherein the identified user persona from the user story data is verified against a Named Entity Recognition (NER) dataset created based on the format of user story data, and if verified successfully the POS is identified as user persona.
5. The system (100) as claimed in claim 1, wherein the requirements analysis engine (104) comprises an action identification unit (116) executed by the processor (106) and configured to analyze the user story data for extracting the action requirement by applying the second set of pre-defined rules, and wherein a data pattern is determined in the user story data in the event user story format is determinable for determining the second pre-defined parameter.
6. The system (100) as claimed in claim 5, wherein the action identification unit (116) is configured to determine the second pre-defined parameter, in the event user story format is not determinable, by firstly determining a first POS using one or more NLP POS tags; by secondly determining root of the first word with POS ('VB' (verb),'VBG' (verb gerund),'VBZ'

(verb, present tense with third person singular),'VBD' (verb past tense)) having dependency tag XCOMP and child of the verb having a dependency tag DOBJ; and thirdly by checking child of PREP if there is no root associated with XCOMP, and if child of the first word with POS ('VB','VBG','VBZ','VBD') is a PREP (preposition).
7. The system (100) as claimed in claim 1, wherein the requirements analysis engine (104) comprises an action outcome identification unit (118) executed by the processor (106) and configured to analyze the user story data for extracting the action outcome requirement from the user story data by applying the third set of pre-defined rules, and wherein in the event user story format is determinable, then the action outcome identification unit (118) is configured to determine the third pre-defined parameter by applying a Regex (regular expressions) data, and wherein in the event user story format is not determinable, then the action outcome identification unit (118) is configured to determine the third pre-defined parameter by using POS tags and a phrase occurring in the last part of the user story data is identified by words following action.
8. The system (100) as claimed in claim 5, wherein the action identification unit (116) is configured to minimize false positive data by execute a data model by using a Viterbi® technique, and wherein the data model is trained with a static list of words tagged to the POS for identifying appropriate parts of speech based on the current context of the user story data.
9. The system (100) as claimed in claim 1, wherein the requirements analysis engine (104) comprises an atomicity identification unit (120) executed by the processor (106) and configured to determine atomicity of the user story data by analyzing the action identified by an action identification unit (116) for identifying the POS having one or more actions

('VB','VBG','VBZ','VBD') based on executing the fourth set of pre-defined rules, and wherein the fourth set of pre¬defined rules are executed based on the POS tag of CONJ (conjunction), and wherein atomicity in the user story data is identified by the atomicity identification unit (120), despite presence of a conjunction.
0. The system (100) as claimed in claim 1, wherein the requirements analysis engine (104) comprises an ambiguity identification unit (122) executed by the processor (106) and configured to determine one or more categories associated with the ambiguous words by applying the fifth set of pre¬defined rules, and wherein the categories associated with ambiguous words comprises comparative data, indirect data, persuasion data, qualifier data, and quantities data, and wherein the categories of ambiguous words are determined by checking for the presence of ambiguous words from a pre¬defined dataset of ambiguous words present in the user story data.
1. The system (100) as claimed in claim 10, wherein the ambiguity identification unit (122) is configured to identify the false positive data associated with the identified ambiguities data present in the user story data for correctly determining the ambiguity data.
2. The system (100) as claimed in claim 1, wherein the requirements analysis engine (104) comprises an acceptance criteria verification unit (124) executed by the processor (106) and configured to determine the acceptance criteria by implementing a trained acceptance criteria Machine Learning (ML) model stored in the database (112), using the sixth set of pre-defined rules, and wherein the acceptance criteria ML model is trained and generated based on historical acceptance criteria data, and wherein the acceptance criteria ML model determines the acceptance criteria by determining if the acceptance criteria data has been added in the user story

data description field, and wherein the acceptance criteria is determined by analyzing one or more suffix tags present in the user story data, the suffix tags are identified by one or more data input adaptors associated with the acceptance criterion ML model.
13. The system (100) as claimed in claim 12, wherein one or more data adaptors are created for a specific Application Life Management (ALM) tool using relevant Application Programming Interfaces (APIs) to retrieve user story data for checking data related to acceptance criteria based on the default list of tags used to identify acceptance criteria.
14. The system (100) as claimed in claim 12, wherein the acceptance criteria is determined by the acceptance criteria ML model based on associated historical data of user story data and corresponding acceptance criteria by carrying out pattern mining to identify the association amongst user story data.
15. The system (100) as claimed in claim 12, wherein the acceptance criteria is determined by the acceptance criteria ML model for new user stories data by carrying out nested clustering of user stories data and acceptance criteria associated with the historical user story data, and wherein an iGraph clustering technique is implemented by the acceptance criteria verification unit (124) to carry out clustering of user stories data and acceptance criteria associated with the historical user story data, and wherein based on the clustering technique repeatable acceptance criteria appearing under different but similar user stories data are identified.
16. The system (100) as claimed in claim 15, wherein the user stories data is created as a pattern and stored in the database (112), and wherein when a new user story is processed, it is validated for similarity with the stored

pattern and the corresponding acceptance criteria is recommended by the acceptance criteria verification unit (124).
17. The system (100) as claimed in claim 1, wherein the requirements analysis engine (104) comprises a sufficiency checking unit (126) executed by the processor (106) and configured to verify whether length of the user story data is greater than a pre-defined user story data configurable length or not, and wherein the count of words in the user story data before eliminating the stop words is computed as length of the user story data, and wherein the sufficiency checking unit (126) is configured to flag the user stories data having data less than the pre-defined user story data configurable length.
18. The system (100) as claimed in claim 17, wherein the sufficiency checking unit (126) is configured to determine that each user story data is unique based on verification of the title or summary of the user story data, and wherein the title or summary of the user story data is verified to be unique based on comparison with the other user stories delivered in the current or past sprints, and wherein the comparison is done by carrying out a string match between the titles of user stories data by the sufficiency checking unit (126).
19. The system (100) as claimed in claim 1, wherein an output is rendered at the user-end via an output and correction unit (128) in the form of the Requirement Completeness Index (RCI), and wherein the pre-defined parameter based on RCI are configurable and indicates clarity and readiness of the user story data, and wherein the user story data is improved by setting up a pre-defined threshold value associated with the RCI.

20. A method for optimized processing of requirements data in a software development life cycle, the method is implemented by a processor (106) executing instructions stored in a memory (108), the method comprises:
processing fetched user story data from a database (112) for determining a first pre-defined parameter associated with the user story by applying a first set of pre-defined rules, wherein the first pre-defined parameter represents persona of the user associated with the user story data;
determining a second pre-defined parameter associated with the user story data by applying a second set of pre¬defined rules, wherein the second pre-defined parameter represents action requirement associated with the user story data for an application;
determining a third pre-defined parameter associated with the user story data by applying a third set of pre¬defined rules, wherein the third pre-defined parameter represents action outcome associated with the user story data for the application;
determining a fourth pre-defined parameter associated with the user story data by applying a fourth set of pre¬defined rules, wherein the fourth pre-defined parameter represents atomicity of the user story data;
determining a fifth pre-defined parameter associated with the user story data by applying a fifth set of pre¬defined rules, wherein the fifth pre-defined parameter represents ambiguity in the user story data;
determining a sixth pre-defined parameter associated with the user story data by applying a sixth set of pre¬defined rules, wherein the sixth pre-defined parameter represents acceptance criteria for the user story data;

determining a seventh pre-defined parameter associated with the user story data by applying a seventh set of pre¬defined rules, wherein the seventh pre-defined parameter represents determination of length of the user story data; and
rendering an output in the form of a Requirement Completeness Index (RCI) for the user story data, the RCI is generated based on a cumulative computation of percentage weighted score for each of the pre-defined parameters, and corrective actions are automatically carried out on the user story data based on the generated RCI.
21. The method as claimed in claim 20, wherein user persona is determined from the user story data by applying the first set of pre-defined rules by processing a user story data format used for user story creation by analyzing one or more regular expressions present in the user story to determine and validate the user persona associated with the user story data, and wherein the one or more regular expressions are analyzed by applying a pre-built NLP library, the regular expression data present in the user story are determined as Part of Speech (POS), and wherein a correlation is determined between the data present in the user story data.
22. The method as claimed in claim 21, wherein the user persona is determined from the user story data by applying the first set of pre-defined rules including in the event it is determined that the user story format is not determinable from the user story data, then NLP Parts-Of-Speech (POS) parsers are applied to identify POS that is present in the user story sentence from the user story data for identifying the user persona, and wherein identified user persona from the user story data is verified against a Named Entity Recognition (NER) dataset created based on the format of user story data, and if verified successfully the POS is identified as user persona.

23. The method as claimed in claim 20, wherein a data pattern is determined in the user story data in the event user story format is determinable for determining the second pre-defined parameter.
24. The method as claimed in claim 23, wherein in the event user story format is not determinable, then the second pre-defined parameter is determined by determining a first POS using one or more NLP POS tags; by determining root of the first word with POS ('VB'(verb),'VBG'(verb gerund),'VBZ' (verb, present tense with third person singular),'VBD' (verb past tense)) having dependency tag XCOMP and child of the verb having a dependency tag DOBJ; and checking child of PREP if there is no root associated with XCOMP, and if child of the first word with POS ('VB','VBG','VBZ','VBD') is a PREP (preposition).
25. The method as claimed in claim 20, wherein the user story data is analyzed for extracting the action outcome requirement from the user story data by applying the third set of pre-defined rules, and wherein in the event user story format is determinable, then the third pre-defined parameter is determined by applying a Regex (regular expressions) data, and wherein in the event user story format is not determinable, then the third pre-defined parameter is determined based on using POS tags and in this scenario, a phrase occurring in the last part of the user story data is identified by words following action.
26. The method as claimed in claim 20, wherein false positive data is minimized by executing a data model by employing a Viterbi® technique, and wherein the data model is trained with a static list of words tagged to the POS for identifying appropriate parts of speech based on the current context of the user story data.
27. The method as claimed in claim 20, wherein atomicity of the user story data is determined by analyzing the action

identified for identifying the POS having one or more actions ('VB','VBG','VBZ','VBD') by executing the fourth set of pre-defined rules, and wherein the fourth set of pre-defined rules are executed based on the POS tag of CONJ (conjunction), and wherein atomicity in the user story data is identified despite presence of a conjunction.
28. The method as claimed in claim 20, wherein one or more categories associated with the ambiguous words are determined by applying the fifth set of pre-defined rules, and wherein the categories associated with ambiguous words comprises comparative data, indirect data, persuasion data, qualifier data, and quantities data, and wherein the categories of ambiguous words are determined by checking for the presence of ambiguous words from a pre-defined dataset of ambiguous words present in the user story data.
29. The method as claimed in claim 20, wherein the acceptance criteria is determined by implementing a trained acceptance criteria Machine Learning (ML) model, using the sixth set of pre-defined rules, and wherein the acceptance criteria ML model is trained and generated based on historical acceptance criteria data, and wherein the acceptance criteria ML model determines the acceptance criteria by determining if the acceptance criteria data has been added in the user story data description field, and wherein the acceptance criteria is determined by analyzing one or more suffix tags present in the user story data, the suffix tags are identified by one or more data input adaptors associated with the acceptance criterion ML model.
30. The method as claimed in claim 29, wherein the user stories data is created as a pattern, and wherein when a new user story is processed, it is validated for similarity with the stored pattern and the corresponding acceptance criteria is recommended.

31. The method as claimed in claim 20, wherein an output is rendered in the form of the Requirement Completeness Index (RCI), and wherein the pre-defined parameter based on RCI are configurable and indicates clarity and readiness of the user story data, and wherein the user story data is improved based on setting up a pre-defined threshold value associated with the RCI.