AYUSHCare: Predictive Disease Diagnosis and
Recommendation System for Holistic Wellness
using AI
SOFTWARE ENGINEERING AND PROJECT MANAGEMENT
(21CSC303J)
PROJECT REPORT
Submitted by:
K Krisha
RA2211008020019
S Ritika
RA2211008020027
Tanesha K Tomas
RA2211008020055
SRM INSTITUTE OF SCIENCE AND TECHNOLOGY
FACULTY OF ENGINEERING AND TECHNOLOGY
BONAFIDE CERTIFICATE
This is to certify that this is the project report work titled “AYUSHCare:
Predictive Disease Diagnosis and Recommendation System for Holistic
Wellness using AI” done by K Krisha (RA2211008020019), S Ritika
(RA2211008020027), Tanesha K Tomas (RA2211008020055) in the course
21CSC303J/SOFTWARE
ENGINEERING
AND
PROJECT
MANAGEMENT from the Department of Information Technology at SRM
Institute of Science and Technology, Ramapuram Campus during the academic
year 2024-2025
………………...
Staff-In charge
…………………………
Head of the Department
Dr. R. Kavitha, Asso. Prof. /IT
Dr. Rajeswari Mukesh, Professor/IT
……………………
……………………..
Internal Examiner – 1
Internal Examiner – 2
DEPARTMENT OF INFORMATION TECHNOLOGY
Faculty of Engineering and Technology SRM Institute of Science and
Technology
EVEN SEMESTER
2024-2025
Lab code & Sub name: 21CSC303J SOFTWARE ENGINEERING &
PROJECT MANAGEMENT
Year & Semester : 3nd Year & 6th Semester
Project Title : AYUSHCare: Predictive Disease Diagnosis and
Recommendation System for Holistic Wellness using AI
Lab Supervisor : Dr. R. Kavitha
Reg. No
Mark split up
Novelty in the project work
(2 marks)
Level of understanding of the
software design concepts(4
marks)
Contribution to the project
(2 Marks)
Report (2 Marks)
Total (10 Marks)
RA2211008020019
RA2211008020027
RA2211008020055
INDEX
S.No Date
of Name of the Experiment
Marks Faculty
experiment
signature
Identify the Software Project, Create
1
Business Case, Arrive at a Problem
Statement
Stakeholder and User Description, Identify
2
the
appropriate
Process
Model,
Comparative study with Agile Model
Identify the Requirements, System
3
Requirements, Functional Requirements,
Non-Functional Requirements
Prepare Project Plan based on scope, Find
4
Job roles and responsibilities, Calculate
Project effort based on resources
Prepare the Work, Breakdown Structure
5
based on timelines, Risk Identification and
Plan
Prepare the Work, Breakdown Structure
6
based on timelines, Risk Identification and
Plan
State and Sequence Diagram, Deployment
7
Diagram, Sample Frontend Design (UI/UX)
Module Description, Module
8
Implementation (phase 1) Using Agile
Module Implementation, Scrum Master to
9
Induce New requirements in Agile
Development
Module Implementation (Phase 2), Scrum
10
Master to Induce New Issues in Agile
Development
Module Implementation (Phase 3) Scrum
11
Master to Induce New requirements in Agile
Development, Scrum Master to Induce New
Issues in Agile Development, Code
Documentation
Master Test Plan, Test Case Design
12
(Phase 1)
Manual Testing
13
14
15
User Manual, Analysis of Costing, Effort
and Resources
Project Demo and Report Submission with
the team
Experiment 1
Identify the Software Project, Create Business Case, Arrive at a
Problem Statement
PROBLEM STATEMENT:
A problem statement is a concise description of the problem or issues a project seeks to address.
The problem statement identifies the current state, the desired future state and any gaps between the
two. A problem statement is an important communication tool that can help ensure everyone
working on a project knows what the problem they need to address is and why the project is
important.
The increasing global shift towards holistic and preventive healthcare emphasizes the need for
accessible and personalized wellness solutions that go beyond conventional medicine. Despite the
growing recognition of AYUSH practices—Ayurveda, Yoga, Unani, Siddha, and Homeopathy—
there remains a gap in integrating these ancient systems of medicine with modern technology.
Existing healthcare tools predominantly focus on conventional treatments, leaving individuals
unaware of the potential benefits of holistic approaches. This gap highlights the need for a
comprehensive, user-friendly platform that not only predicts health risks but also recommends
preventive and curative measures based on AYUSH principles.
SOLUTION:
To bridge this gap, this project proposes the AYUSHCare system—a predictive disease diagnosis
and recommendation platform built on the foundation of AYUSH medicinal practices. The system
uses advanced AI algorithms to analyze user-input symptoms, demographic information, and health
parameters to predict potential health conditions. By leveraging an extensive database of AYUSH
knowledge, the platform provides holistic treatment recommendations, including dietary guidelines,
lifestyle adjustments, and AYUSH-based therapies (Ayurveda, Yoga, Unani, Siddha, and
Homeopathy). The system delivers insights into managing and preventing health issues through
natural, preventive measures, aligning with the principles of holistic wellness.
This solution is accessible via an interactive web application, developed using Streamlit, enabling
users to input their health data and receive immediate predictions and treatment suggestions. The
intuitive platform ensures accessibility for both healthcare professionals and the general public,
promoting informed decision-making and proactive health management based on traditional healing
methods.
BUSINESS CASE:
The proposed system presents a significant opportunity for both individuals and healthcare providers
by offering an affordable, comprehensive tool for health prediction and wellness recommendations
rooted in AYUSH practices. It empowers users with personalized insights while promoting the
principles of Ayurveda, Yoga, Unani, Siddha, and Homeopathy.
BENEFITS:

Early Detection & Preventive Care: Identifies potential health issues, allowing individuals
to take preventive actions using holistic methods.

Holistic Wellness Solutions: Recommends comprehensive generalized treatment plans that
combine the wisdom of traditional AYUSH practices with modern predictive technologies.

Public Awareness & Education: Informs users about the factors influencing their health
and promotes a better understanding of AYUSH principles, encouraging healthier lifestyle
choices and preventive care.
COSTS AND RISKS:
Costs:

Development Costs: Designing and implementing machine learning algorithms, integrating
AYUSH knowledge, and building the web application.

Operational Costs: Hosting, maintaining the platform, and regular updates to ensure the
database remains accurate and up-to-date.

Training & Support Costs: Educating users and healthcare providers on how to effectively
use the system and incorporate AYUSH practices into their wellness routines.
Risks:

Data Quality & Completeness: Incomplete or biased datasets may reduce the accuracy of
disease predictions.

Technical Challenges: Ensuring the system’s performance, scalability, and integration of
new features over time.

Adoption Resistance: Potential reluctance from users or healthcare institutions to adopt AIdriven, holistic health solutions due to unfamiliarity with or skepticism toward traditional
medicine.
PROPOSAL:
This project aims to deliver the following key components:
1. AI-Powered Predictive Model: A model that uses advanced data analysis techniques to
accurately analyze symptoms and health data, predicting potential diseases based on
AYUSH principles.
2. Interactive Web Application: A user-friendly platform that allows individuals to input
health information and receive immediate, AI-driven disease predictions and treatment
recommendations.
3. Educational Resources: Clear, interpretable insights into users’ health risks, factors
contributing to their condition, and how AYUSH-based treatments can aid in prevention and
management.
DELIVERABLES:

Holistic Treatment Recommendations: Treatment options grounded in AYUSH practices,
offering a natural and preventive approach to health management.

Scalable and Accessible Platform: A solution designed to integrate new features, expand
to more AYUSH treatments, and serve a growing user base.
By investing in this project, stakeholders will contribute to increasing awareness of AYUSH
practices, improving health outcomes, and promoting preventive care. The platform will empower
individuals to make informed health decisions, blending the ancient wisdom of traditional medicine
with the capabilities of modern AI technology.
Experiment 2
Stakeholder and User Description, Identify the appropriate
Process Model, Comparative study with Agile Model
STAKE HOLDERS :
A stakeholder is a party that has an interest in a company or project and can either affect or be
affected by its activities.
For the AYUSH-based recommendation system, the primary stakeholders include:
Internal Stakeholders:

K Krisha

S Ritika

Tanesha K Tomas
External Stakeholders:

Users seeking holistic AYUSH treatments.

AYUSH practitioners and healthcare professionals.

Researchers and experts in traditional medicine.

Government bodies promoting AYUSH healthcare.

Media professionals spreading awareness about AYUSH practices.

Educational institutions and students interested in AYUSH methodologies.

PROCESS MODEL:

The software process model used in the development of the AYUSH-based recommendation
system represents a structured approach to software development. It defines the sequence
and stages of the process to ensure efficiency and effectiveness in delivering the end product.

Choosing the right software process model is crucial. Given the structured nature of the
system and the need for well-defined phases, we are using the Waterfall Model for
development.
SOFTWARE PROCESS AND SOFTWARE DEVELOPMENT LIFECYCLE MODEL
A Software Development Life Cycle (SDLC) model provides guidance for organizing
tasks, defining input/output processes, and ensuring seamless execution of activities.
The Waterfall Model was chosen due to its systematic and sequential approach,
ensuring each stage is completed before moving to the next. This is particularly useful
for the AYUSH-based recommendation system, as it requires clear requirement
definitions and structured implementation.
There are many development life cycle models that have been developed in order to
achieve different required objectives.
There are many kinds of process models for meeting different requirements.
We refer to these as SDLC models (Software Development Life Cycle models). The
most popular and important SDLC models are as follows:
•
Waterfall model
•
V model
•
Incremental model
•
RAD model
•
Agile model
•
Iterative model
•
Prototype model
•
Spiral model
Here we are using waterfall model.
WATERFALL MODEL:
The Waterfall Model was chosen due to its systematic and sequential approach, ensuring
each stage is completed before moving to the next. This is particularly useful for the
AYUSH-based recommendation system, as it requires clear requirement definitions and
structured implementation.
Phases of the Waterfall Model:
1. Requirements Gathering: Identifying the key functionalities, such as
symptom analysis, disease prediction, and treatment recommendation based
on AYUSH principles.
2. Design: Structuring the database, AI algorithms, and user interface
components to enhance user experience.
3. Implementation: Developing the core system, including data collection, AI
integration, and treatment recommendation logic.
The Waterfall Model ensures a step-by-step progression, making it an ideal choice for
this project, as it demands a structured and research-backed development approach to
integrate traditional AYUSH practices with modern AI technology.
Diagram :
REQUIREMENT
ANALYSIS
SYSTEM
DESIGN
IMPLEMENTATION
TESTING
DEPLOYMENT
MAINTAINANCE
Experiment 3
Identify the Requirements, Project Requirements, Functional
Requirements, Non-Functional Requirements
REQUIREMENTS:
A requirement is a condition or capability that a system must fulfill to meet its intended
purpose. For an AYUSH-based recommendation system, requirements define the core
functionalities that ensure accurate symptom analysis, disease prediction, and treatment
recommendations aligned with AYUSH principles.
PROJECT REQUIREMENTS:
Project requirements outline the essential tasks, features, and functionalities necessary
for the successful development and deployment of the AYUSH-based recommendation
system. These requirements serve as a roadmap for stakeholders and development teams,
ensuring the system delivers a reliable and accessible platform for users seeking holistic
healthcare solutions.
In many cases, stakeholders may have broad expectations, such as an efficient and userfriendly recommendation system, without specifying intricate details. Therefore, it is
crucial to thoroughly gather and analyze project requirements to ensure alignment with
AYUSH principles, technological feasibility, and user accessibility. The success of the
project is measured based on its ability to accurately predict diseases and provide
relevant, generalized AYUSH treatment recommendations.
SOFTWARE REQUIREMENTS:
A Software Requirements Specification (SRS) document is created based on input from
various stakeholders. This document defines how the system interacts with different
components, including databases, user interfaces, and external data sources.
For the AYUSH-based recommendation system, the SRS should address key aspects such
as:
Programming Language:

Python (version 3.6 or higher)
Web Development:

Streamlit (for building the interactive web application)
Model Serialization:

Pickle (for saving and loading trained models)
Libraries and Frameworks:

Machine Learning Libraries:
o
Scikit-learn (for implementing SVM, Logistic Regression, Decision Tree,
and Random Forest)
o
NumPy (for numerical computations and data manipulation)
Development Tools:

An Integrated Development Environment (IDE) such as PyCharm or Visual Studio
Code
TECHNICAL REQUIREMENTS:
Technical requirements define the technological aspects essential for the system's
development and smooth operation. These include:

Programming Language: The system should be developed using technologies that
support efficient data processing and AI-based recommendations, such as Python
for backend processing and JavaScript for UI/UX.

Operating System Compatibility: The system must be accessible on various
platforms, including web and mobile.

Database Management: A structured and scalable database is required to store
AYUSH knowledge, symptom records, and disease prediction data.

System Availability and Reliability: The system should ensure continuous
availability, handling multiple user queries simultaneously.
USER REQUIREMENTS:
User requirements define how users will interact with the system and their expectations
from the platform. These include:

Ease of Use: The system should provide a simple and intuitive interface for users
to input symptoms and receive recommendations.

Comprehensive Information: Users should be able to access details about AYUSH
practices, treatment options, and their relevance to various health conditions.

Educational Content: The platform should include resources that help users
understand Ayurveda, Yoga, Unani, Siddha, and Homeopathy.

Accessibility: The system must support multiple languages and be accessible to
a diverse user base.
FUNCTIONAL REQUIREMENTS:
Functional requirements define the core operations that the system must perform. For the
AYUSH-based recommendation system, these include:

Symptom Analysis: Users should be able to input symptoms, and the system
should match them with relevant AYUSH medical data.

Disease Prediction: The system must predict potential health conditions based on
user-input symptoms using AI algorithms.

Treatment Recommendations: The platform should provide non-personalized
AYUSH-based treatment suggestions, including dietary guidelines and lifestyle
changes.

User Feedback Mechanism: Users should be able to provide feedback on the
system’s recommendations and usability.
NON-FUNCTIONAL REQUIREMENTS:
Non-functional requirements define the quality attributes of the system. Key nonfunctional requirements for the AYUSH-based recommendation system include:

Performance: The system should deliver fast and accurate predictions with
minimal processing delays.

Scalability: The platform should handle a growing number of users and expand
its knowledge base without performance degradation.

Availability and Reliability: The system should maintain high uptime, ensuring
consistent access to recommendations.

Usability: The platform should offer a seamless and user-friendly interface,
making it easy for users to navigate and access relevant information.
By implementing these requirements, the AYUSH-based recommendation system aims
to bridge the gap between traditional medicine and modern technology, promoting
holistic well-being and preventive healthcare solutions.
Experiment 4
Prepare Project Plan based on scope, Find Job roles and
responsibilities, Calculate Project effort based on resources
Project Plan & its Component
The AYUSHCare: Predictive Disease Diagnosis and Recommendation System for Holistic
Wellness using AI is an advanced healthcare solution that integrates traditional AYUSH medicine
with AI-driven analysis. This system is designed to provide users with personalized health
recommendations based on symptom evaluation, disease prediction, and AYUSH-based treatment
plans. By leveraging modern machine learning techniques and an extensive AYUSH knowledge
base, the system enhances accessibility to holistic healthcare solutions.
JOB ROLES AND RESPONSIBILITIES

Project Sponsor: Healthcare professionals, wellness researchers, AYUSH practitioners,
patients, and general users.

Technical Lead & Software Developers: Responsible for AI model development, backend
implementation, and system integration.

Project Manager (PM) & Content Writing: Manages workflow, documentation, and content
related to AYUSH principles.

Quality Assurance & Software Testers: Ensure accuracy in disease predictions, treatment
recommendations, and user experience.
Scope Statement :
1. Disease Prediction – The system identifies potential health conditions based on user
symptoms and historical data.
2. AYUSH Treatment Recommendations – Personalized treatment plans including
Ayurveda, Yoga, Unani, Siddha, and Homeopathy are provided based on diagnosed
conditions.
3. Dietary and Lifestyle Guidance – The system suggests lifestyle and dietary
adjustments in line with AYUSH practices.
4. Educational Resource – Users gain insights into AYUSH treatments and their
scientific relevance for preventive care.
Baseline Management Plans
The project follows a structured development approach, integrating schedule, cost, and scope
baselines to monitor progress:

Schedule Baseline: The development phases include research, data collection,
model training, system integration, and testing.

Cost Baseline: As the project progresses, the cost structure will include AI model
training, data acquisition, and system maintenance.
Project Effort based on Resources:
Effort estimation for the AYUSHCare system follows the COCOMO (Constructive Cost
Model) technique:

Size: The project’s function points are estimated based on AI models,
symptom datasets, and the treatment recommendation system.

Complexity: The system integrates multiple AI algorithms, NLP processing,
and real-time symptom evaluation, making it a moderately complex project.

Experience Factor: Given the expertise of the development team in AI and
AYUSH principles, an experience factor of 1.25 is assumed.
Effort Calculation:
Effort = Size × Complexity × Experience Factor
Assuming 120 function points, moderate complexity (1.0), and an experience factor
(1.25), the estimated effort is:
Effort = 120 × 1.0 × 1.25 = 150 person-months
This suggests a team of 15 developers working for 10 months or 10 developers for 15
months to complete the system. However, real-world implementation will depend on
team productivity, project scope, and system enhancements.
Experiment 5
Prepare the Work Breakdown Structure based on timelines, Risk
Identification and Plan
A Risk Breakdown Structure (RBS) is a hierarchical framework that categorizes and
prioritizes project risks. According to the Project Management Institute (PMI), a project
risk is defined as “an uncertain event that, if it occurs, has an effect on at least one project
objective.” Risks can have both negative and positive impacts on the project, and an RBS
helps project managers identify, prioritize, and mitigate these risks effectively.
What is RBS in Project Management?
A Risk Breakdown Structure (RBS) is a structured chart that classifies project risks
into categories and subcategories. It works similarly to a Work Breakdown Structure
(WBS) but focuses specifically on risks.
For example, a typical RBS categorizes risks into four major areas:
1. Technical Risks
2. External Risks
3. Organizational Risks
4. Project Management Risks
Each of these categories further breaks down into more specific risk areas, helping in
precise identification and mitigation planning.
Risk Breakdown Structure (RBS) for AYUSHCare: Predictive Disease Diagnosis
and Recommendation System

Unexpected changes in project scope affecting timelines

Resource constraints affecting project deadlines

Lack of expertise causing slower development progress
Cost Risks

Unexpected expenses in dataset acquisition

Changes in requirements increasing development costs

Increased costs due to unforeseen technical challenges

Budget constraints affecting software quality and security
Security Risks

Data breaches exposing user health information

Insufficient encryption for sensitive patient data

Unauthorized access due to poor authentication measures

Lack of compliance with health data regulations (e.g., HIPAA, GDPR)
User Acceptance Risks

Users find the system difficult to navigate

Resistance to using AI-based health recommendations

Lack of trust in AYUSH-based predictive diagnosis

Poor user engagement due to inadequate awareness
Training Risks

Inadequate training of healthcare professionals on system usage

Lack of documentation or learning materials for users

Users unable to interpret AI-generated recommendations correctly
Scoring the Risks
After identifying risks, it is crucial to prioritize them. PMI recommends using
Probability (P) and Impact (I) to calculate risk scores.
Probability (P) of Risk Occurrence:

High Probability (80% ≤ x ≤ 100%)

Medium-High Probability (60% ≤ x < 80%)

Medium-Low Probability (30% ≤ x < 60%)

Low Probability (0% < x < 30%)
Risk Impact (I) Ratings:

High Impact – Catastrophic (Rating A - 100)

Medium Impact – Critical (Rating B - 50)

Low Impact – Marginal (Rating C - 10)
By assigning a probability and impact score to each risk, we can prioritize mitigation
efforts efficiently.
Risk Breakdown Structure (RBS) for AYUSHCare
A Risk Breakdown Structure (RBS) is a hierarchical representation of potential
risks affecting a project. Below is the RBS for the AYUSHCare system:
1. Technical Risks
1.1. Inaccurate disease prediction due to limited dataset quality
1.2. Voice recognition errors affecting symptom input accuracy
1.3. Algorithm biases leading to incorrect AYUSH recommendations
1.4. Integration challenges with AI models and databases
1.5. System performance issues under high user load
2. Schedule Risks
2.1. Delays in acquiring and preprocessing medical datasets
2.2. Extended time required for AI model training and optimization
2.3. Unforeseen regulatory compliance requirements causing setbacks
2.4. Dependencies on third-party APIs affecting overall timeline
3. Cost Risks
3.1. Increased expenses due to the need for high-quality medical datasets
3.2. Unanticipated costs in upgrading computational resources
3.3. Long-term maintenance and update costs exceeding estimates
3.4. Budget constraints affecting the scope and accuracy of recommendations
4. Security & Privacy Risks
4.1. Unauthorized access to sensitive health data
4.2. Compliance risks with data protection laws (HIPAA, GDPR)
4.3. Potential cyber threats targeting the AI system’s integrity
4.4. Risk of AI-generated recommendations being misused
5. User Acceptance Risks
5.1. Resistance from healthcare professionals towards AI-driven
recommendations
5.2. Users finding the interface difficult to navigate
5.3. Skepticism regarding the reliability of AYUSH-based recommendations
5.4. Inconsistent user adoption due to lack of awareness about holistic medicine
6. Training & Usability Risks
6.1. Lack of adequate user training on system functionalities
6.2. Absence of proper documentation and tutorials
6.3. Misinterpretation of recommendations due to lack of medical knowledge
6.4. Incomplete feedback loop for improving AI model accuracy
By structuring risks into these categories, the AYUSHCare project team can
proactively address challenges and improve system reliability and user trust.
Work Breakdown Structure (WBS) for AYUSHCare
A Work Breakdown Structure (WBS) is a systematic approach to breaking
down a project into smaller, manageable components. Below is the WBS for
AYUSHCare:
Level 1: Project Initiation
•
Define project objectives and scope
•
Identify key stakeholders
•
Conduct feasibility study
•
Secure necessary funding and resources
Level 2: Data Collection & Processing
•
Gather AYUSH medical datasets
•
Perform data cleaning and preprocessing
•
Classify symptoms and treatment recommendations
•
Integrate voice recognition and NLP processing
Level 3: AI Model Development
•
Train machine learning models for disease prediction
•
Fine-tune algorithms for AYUSH treatment suggestions
•
Test AI accuracy with real-world user inputs
Level 4: System Integration & Development
•
Develop the user interface for web and mobile platforms
•
Implement database management for storing recommendations
•
Ensure API connectivity for real-time processing
•
Level 5: Testing & Validation
•
Perform unit testing for AI predictions
•
Conduct security testing for data privacy compliance
•
Gather feedback through beta testing with users
•
Optimize system performance
•
Level 6: Deployment & Maintenance
•
Launch the AYUSHCare platform
•
Provide user training and support
•
Monitor system performance and security updates
•
Continuously update AI models with new medical insights
EXPERIMENT – 6
ESTIMATION OF PROJECT METRICS
In this experiment, we estimate project metrics for the AYUSH-based
recommendation system, ensuring efficient planning and resource allocation. The
estimation is based on different scenarios regarding the availability of project data,
guiding the selection of an appropriate estimation technique.
Scenarios for Project Data Availability:
1. Sufficient project data is available for the current system, but little to no
information exists on previous similar projects.
2. Historical project data is available from previous healthcare-based
recommendation systems, but limited details are known about the current
project.
3. Both current and past project data are available, providing a comprehensive
dataset for estimation.
4. Partial project data is available for the current system, requiring estimation
adjustments.
5. No relevant project data is available for either the current system or previous
projects, making estimation more challenging.
Estimation Technique Selection Based on Project Data Availability:
Function Point Analysis (FPA):
Function Point Analysis is used to estimate project effort based on the functionalities
provided by the AYUSH-based recommendation system. It calculates the effort
required by analyzing system components such as:

User Inputs (symptom entry, treatment recommendation requests)

User Outputs (disease predictions, general treatment recommendations)

User Inquiries (searching AYUSH treatment information)

External Interfaces (database interactions, model execution)

Files and Data Structures (storage of user queries and AYUSH knowledge
base)
Using Function Point Analysis, we can estimate the development effort required to
implement the system efficiently.
COCOMO Model:
The Constructive Cost Model (COCOMO) is used to estimate effort, cost, and time
based on the size of the project (measured in lines of code). For the AYUSH-based
recommendation system, we apply:

Basic COCOMO Model: A simplified estimation method based on project
size, categorizing the system as Organic, Semi-Detached, or Embedded
based on complexity.

Effort Calculation: Using mathematical formulas, we estimate the personmonths required to develop the system.
By applying FPA and COCOMO, we ensure that the AYUSH-based
recommendation system is developed within an optimal timeline and resource
allocation framework.
FUNCTION POINT ANALYSIS | COCOMO MODEL:
Estimation Technique Selection Based on Project Information Availability:
FUNCTION POINT ANALYSIS:
Calculation of effort for the project using function point analysis calculation method
Measurement Parameter
Count (value >= 0) Weighting Factor
Number of User Inputs
Simple / Average / Complex
Number of User Outputs
Simple / Average / Complex
Number of User Inquiries
Simple / Average / Complex
Number of Files
Simple / Average / Complex
Number of External Interfaces
Simple / Average / Complex
COCOMO MODEL:
Calculation of effort for the project using COCOMO model calculation method.