INTERNSHIP

AT
CODING CLOUD

AN INTERNSHIP REPORT

Submitted by

Patel Ketu Shaileshkumar

[210160116005]

In partial fulfilment for the award of the degree of

BACHELOR OF ENGINEERING
in
DEPARTMENT OF COMPUTER ENGINEERING &
INFORMATION TECHNOLOGY
GOVERNMENT ENGINEERING COLLEGE, MODASA

Gujarat Technological University, Ahmedabad

May, 2025
 GOVERNMENT ENGINEERING COLLEGE, MODASA
SHAMLAJI ROAD, MODASA, DISTRICT ARAVALLI, GUJARAT, INDIA 383315

CERTIFICATE

This is to certify that the internship report submitted along with the Internship
in Python with Data Science at Coding Cloud has been carried out by Patel
Ketu Shaileshkumar under my guidance in partial fulfilment for the degree
of bachelor of engineering in Information Technology branch, 8th semester
of Gujarat Technological University, Ahmedabad during the academic
year 2024-25

Prof. Rahul N. Vaza Prof. H.R.Patel

Internal Guide Head of Department
OFFER LETTER
CERTIFICATE
COMPLETION CERTIFICATE
 GOVERNMENT ENGINEERING COLLEGE, MODASA
SHAMLAJI ROAD, MODASA, DISTRICT ARAVALLI, GUJARAT, INDIA 383315

DECLARATION

We hereby declare that the Internship report submitted along with the

Internship entitled Internship in Python with Data Science submitted in

partial fulfilment for the degree of Bachelor of Engineering in Information

Technolgy to Gujarat Technological University, Ahmedabad, is a bonafide

record of original project work carried out by me at Coding Cloud. under the

supervision of Mr. Rahul Vaza and that no part of this report has been

directly copied from any students’ reports or taken from any other source,

without providing due reference.

Name of the Student Sign of Student

Patel Ketu Shaileshkumar

782957

ACKNOWLEDGEMENT

First I would like to thank Coding Cloud for giving me the opportunity to do an

internship within the organization. It is a great chance for learning and professional

development. I am also grateful for having a chance to interact so many wonderful people

and professionals who led me though this internship period.

Bearing in mind previous I am using this opportunity to express my deepest gratitude and

special thanks to the Mr. Kaushik Chaturvedi Sir of Coding Cloud. Who in spite of

being extraordinarily busy with his duties, took time out to hear, guide and keep me on the

correct path and allowing me to carry out my project at their esteemed organization and

extending during the training. I would also like to express my gratitude to my reporting

manager Ms. Hiral Gajjar for her great efforts and instructive comments in this work. I

express my deepest thanks to Prof. Rahul N. Vaza for taking part in useful decision &

giving necessary advices and guidance throughout the internship period. I choose this

moment to acknowledge her contribution gratefully.

I perceive as this opportunity as a big milestone in my career development. I will strive to

use gained skills and knowledge in the best possible way, and I will continue to work on

their improvement, in order to attain desired career objectives.

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ABSTRACT

During my 15-week internship as a Data Science Intern at Coding Cloud (from

January 20, 2025, to April 30, 2025), I gained extensive hands-on experience in

Python and its applications in real-world scenarios. Under the mentorship of

Mr. Kaushik Chaturvedi, I was trained in key Python-based technologies such

as Django, Flask, and SciPy, along with exposure to professional workflows and

development practices.

Coding Cloud primarily focuses on Web Development, App Development, and

AI/ML, and offers an encouraging and growth-oriented work environment. This

internship significantly deepened my understanding of Python’s versatility and

how it's integrated across various domains. The experience was both enriching

and insightful, providing me with a strong foundation in data science and

software development best practices.

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LIST OF FIGURES
Fig 1.1 Company Logo ................................................................ 1

Fig 2.4 Development Stages at Coding Cloud............................. 6

Fig 2.5 Stages of Production ........................................................ 8

Fig 3.6. Internship Timeline ......................................................... 17

Fig 5.1 Use Case Diagram .............................................................22

Fig 5.2 DFD Diagram ................................................................... 23

Fig 5.3 Class Diagram .................................................................. 26

Fig 5.4 Project Workflow Diagram.............................................. 27

Fig 6.1 Streamlit Page......................................................................33

Fig 6.2 User Requirements ............................................................. 33

Fig 6.3 Session Information ............................................................. 34

Fig 6.4 Application States(Langgraph) ............................................34

Fig 6.5 Functional Design ................................................................35

Fig 6.6 Code Generation................................................................. 35

Fig 6.7 Security State ...................................................................... 36

Fig 6.8 Security Guidelines.............................................................. 36

Fig 6.9 Testing State ........................................................................36

Fig 6.10 Final State ......................................................................... 37

Fig 6.11 Final Summary .................................................................. 37

Fig 7.1 Test Strategy........................................................................ 39

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LIST OF TABLES

Table 2.1 Software Requirements ...................................................... 05

Table 2.2 Hardware Requirements .................................................... 05

Table 8.1 Continuous Evaluation ....................................................... 44

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LIST OF ABBREVIATION:

HTML Hypertext Markup Language

CSS Cascading Stylesheet

LLM Large Language Model

API Application Programming Interface

REST Representational state transfer

DB Database

SQL Structured Query Language

NoSQL Not only SQL

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TABLE OF CONTENTS

Acknowledgement ............................................................................. i

Abstract............................................................................................ ii

List of figures ..................................................................................iii

List of Tables .................................................................................. iv

List of Abbreviations ........................................................................ v

Table of Contents ............................................................................ vi

Chapter 1 Overview of the Company .................................................... 1

1.1. History ...................................................................................... 1

1.2 Different products / scope of work .............................................. 1

1.3. Company Vision and Objective ................................................. 1

1.4. Services and work...................................................................... 2

Chapter 2 Overview of Different Department and Projects ................. 3

2.1. Work at Coding Cloud .............................................................. 3

2.2. Problem Statement .................................................................... 3

2.3. Hardware and Software Requirements ....................................... 5

2.4. Schematic layout that shows the sequence of operation for

manufacturing of end product ........................................................... 6

2.5. Stages of Production .................................................................. 8

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Chapter 3 Introduction to Internship .................................................. 10

3.1. Internship Summary ................................................................ 10

3.2. Purpose ................................................................................... 11

3.3. Objective ................................................................................. 11

3.4. Scope ...................................................................................... 11

3.5. Tools & Technologies ............................................................. 12

3.6. Internship Planning.................................................................. 14

3.6.1. Internship Approach & Justification ..................................... 14

3.6.2. Roles and Responsibilities .................................................... 16

3.6.3. Internship Scheduling ........................................................... 17

Chapter: 4 System Analysis.................................................................. 18

4.1. Study of Current System.......................................................... 18

4.2. Problem and Weaknesses of Current System ........................... 18

4.3. Requirements of New System .................................................. 19

4.4. Project Feasibility .................................................................... 20

4.5. Features of New System .......................................................... 21

Chapter: 5 System Design .................................................................... 22

5.1. System Design & Methodology ............................................... 22

Chapter: 6 Implementation .................................................................. 29

6.1. Implementation Platform/Environment .................................... 29

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6.2. Process and Technology .......................................................... 31

6.3. Results/Outcome ..................................................................... 33

Chapter: 7 Testing ................................................................................ 38

7.1. Testing Plan.............................................................................. 38

7.2. Test Strategy ........................................................................... 39

7.3. Test Methods ........................................................................... 39

7.4. Testing in SDLC ...................................................................... 41

Chapter: 8 Conclusion and Discussion ................................................ 42

8.1. Overall analysis of internship .................................................. 42

8.2. Dates of Continuous Evaluation............................................... 43

8.3. Conclusion .............................................................................. 44

Chapter: 9 References .......................................................................... 45

Chapter: 10 Daily Logs......................................................................... 46

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782957 Overview of Company

CHAPTER: 1

OVERVIEW OF THE COMPANY

1.1. HISTORY:

Fig 1.1: Company Logo

Established in 2015, Coding Cloud Private Limited is an IT service

provider company in India. We believe in innovations and excellence.
We have completed 250+ projects and we have delivered exceptional
experience with our partners, clients and community.

1.2. DIFFERENT PRODUCTS / SCOPE OF WORK:

Our aim is providing excellent satisfaction to our clients with our best
team efforts in area of Web development, Web designing, Mobile
development Software development and Artificial Intelligence. Goal to
satisfy our customer’s needs, whether it be through timely delivery,
providing the best value for the money, efficient and courteous service
and reliability of our services and that will provide quality output
ensuring value to our customer’s business and mark their profitability.

1.3. COMPANY VISION AND OBJECTIVE:

At coding cloud, we blend the latest technologies with a dedication to

building superior products for our clients. We have 8+ years of
experience and We help you shape your company’s digital future in tune
with the evolving customer expectations. Each of our websites and apps
is built with a progressive outlook with a focus on providing a seamless
experience.
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1.4. SERVICES AND WORK

Services they provide:

 UI / UX Design
Our impressive UI/UX designers craft interesting application user
interfaces for all your digital channels. A good UI goes beyond just
capturing the user’s attention, It builds the brand’s image. Hence, we
believe that every business should work towards an immersive UI
experience that improves the UX.

 Web & Mobile App Development

We develop domain-specific web applications that are customized as

per the requirements of our clients. Developing web pages with
consultation, customized strategy and development. We put together an
action plan that is in line with your organization to achieve your goals.

 REST API Development

We offer REST API development solutions to meet the ever growing
demands of the industry with the best capable minds in the field. Our
engineers offer the best practices that help in building APIs that are
easy to scale up and adapt to the future.
 Android Development

Our experts from the Android app development team are excellent at
what they do. Our developers have designed impressive apps right from
the conceptualization to offer the best applications to the consumers. At
Coding Cloud, we truly have an unique approach to designing mobile
apps by using the Kotlin programming language to create Android
apps.
 AI / ML
The AI/ML solutions our experts provide are tailored precisely to your
unique requirements, transforming your ideas into intelligent, data-
driven realities for your business. We are truly your one-stop
destination for AI/ML development and optimizing your operations
through smart automation and predictive insights.

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782957 Overview of Different Department

CHAPTER: 2

OVERVIEW OF DIFFERENT DEPARTMENT AND PROJECTS:

2.1 WORK AT CODING CLOUD:

Coding Cloud have a diversified approach when it comes to choose a tech stack and some
other technologies, we use in order to work at Coding Cloud are:

 Python Development

 DevOps

 Large Language Model

 App Development

 MERN Stack

 Java Full Stack Development

 Data Science

 WordPress

 C# Development

 Flask API/ Fast API Development

2.2 PROBLEM STATEMENT

2.2.1 INTRODUCTION TO THE PROBLEM:

In modern software development, managing the Software Development Life Cycle (SDLC)
effectively is critical for delivering high-quality software products. However, traditional
SDLC processes often involve siloed stages, manual handoffs, delays in feedback loops,
and repetitive tasks that slow down delivery and increase the chances of human error. From
requirement gathering to testing and deployment, inefficiencies at any stage can lead to
compromised software quality, delayed timelines, and increased costs. With the rise of
Large Language Models (LLMs) and workflow orchestration tools, there is a significant
opportunity to revolutionize SDLC execution through automation.

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A system powered by LangGraph, OpenAI, and modern APIs can enable seamless
transitions between stages like user story generation, design suggestion, code generation,
testing, and review—bringing AI-powered intelligence and speed into every step of
development.

2.2.2 CHALLENGES FACED IN EXISTING SYSTEMS:

Some key issues faced by developers, project managers, and QA teams in traditional or
semi-automated SDLC pipelines include:

 Time-Consuming Processes – Repetitive tasks like writing boilerplate code,

documentation, or test cases slow down the development process.

 Lack of Integration – Tools used in different stages (e.g., design, development,

testing) are often disconnected, requiring manual coordination.

 Delayed Feedback Loops – Reviews and testing occur at later stages, leading to
increased rework and reduced agility.

 Limited Use of AI – Existing systems do not leverage the power of LLMs or graph-
based workflows to automate or enhance development tasks.

 Inefficient Resource Utilization – Time and effort are wasted on routine tasks that
could be automated using intelligent agents.

2.2.3 NEED OF SOLUTION:

To address these limitations, there is a clear need for an AI-driven SDLC Automation
System that:

 Uses LangGraph and LLMs – Automates each phase of SDLC including

requirement analysis, design suggestions, code generation, testing, security review,
and feedback incorporation.

 Reduces Human Intervention – Minimizes manual work through prompt-driven

agent workflows for faster turnaround times.

 Improves Collaboration & Traceability – Ensures that each phase communicates

effectively with the next using a defined and traceable graph-based structure.

 Provides Consistent Quality & Coverage – Ensures comprehensive testing and

secure code suggestions using built-in QA and security agents.

 Scalable & Modular – Can be integrated into existing CI/CD pipelines, IDEs, or
APIs to enhance enterprise development environments.

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2.3 HARDWARE AND SOFTWARE REQUIREMENTS:

2.3.1. SOFTWARE REQUIREMENTS:

Component Specification / Technology

Operating System Windows 10/11, Ubuntu 20.04+, or macOS

Monterey+
Programming Language Python 3.9+

Framework LangGraph

Web Framework (if UI) Streamlit / Flask / FastAPI (for user input
interface)
Code Editor / IDE PyCharm, VSCode, or Jupyter Notebook

Package Manager pip / conda

Libraries - langchain\n- networkx\n- pydantic\n-

openai or llama-index\n- pytest for testing

2.3.2. HARDWARE REQUIREMENTS:

Component Minimum Requirement Recommended Requirement

Processor Dual Core 2.0 GHz Quad Core 2.5+ GHz

RAM 8 GB 16 GB or higher

Storage 20 GB free disk space SSD with 50 GB+ free space

Graphics Integrated GPU Dedicated GPU (for LLM

models)
Internet Required for API Calls (e.g., Stable broadband connection
OpenAI)
Display 1366x768 resolution 1920x1080 or higher

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2.4. SCHEMATIC LAYOUT THAT SHOWS THE SEQUENCE OF

OPERATION FOR MANUFACTURING OF END PRODUCT:

Fig 2.1: Development Stages at Coding Cloud

STAGES:

Ideation:
It is essentially a strategy session (or sessions) that is a formalized part of the
development process. It brings together the entire squad in order to leverage the
insights, experiences, and ideas of product owners, designers, developers, QA, and
architects. The logic behind it is simple; collectively, a team has more knowledge,
experience, and insight than an individual. Product ideation channels this and applies
it to a particular product.

Development:

After ideation, teams can start building the first iteration of the software. The
development phase includes all related production tasks in the SDLC, such as UX/UI
design, architecting, and coding. Developing the first iteration of a software product
is often the longest stage of the Agile application development lifecycle .

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Testing:
Development went smoothly and the team is happy with the first iteration of the app.
But before it’s released, it has to go through quality assurance. The Agile team tests
the app to ensure full functionality by:

 Checking that the code is clean

 Addressing bugs and errors

 Performing trial runs

Deployment:

Once the app is ready for release, the Agile team deploys it to the cloud or an on-
premise server. This means the product is live and accessible to customers.
Deployment tends to be the most celebratory moment in the SDLC: You did it! Pat
yourself on the back now, but there’s one more stage to go.

Operations:

Once the magic button is pushed, the work continues. Ongoing maintenance helps
squash bugs and maintain functionality. As users engage with the app, there will be
opportunities to collect feedback and make improvements that can be released in future
iterations

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2.5. STAGES OF PRODUCTION:

Fig 2.2: stages of production

 Conceptual Design:
This is the very first and most important stage of any product development. Concepts
are developed into the designs of the manufacturable product. Designs are developed
using different engineering design software. Different simulation and analysis
software are used to evaluate and analyze designed product functionality and
feasibility. Which ultimately reduces the burden of prototype development and
testing.
 Prototype Development:
Once the design is ready and analyzed using simulation software, the prototype is
developed for actual physical testing of the product. A sufficient number of
prototypes are developed for rigorous testing as per the criticality of product
functionalities.

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 Testing:
Testing is a very important and inevitable part of any product development process.
Approval of the product depends on testing results. Issues identified during testing
are resolved by making design changes and are again tested. Unless and until testing
gives expected and required results, the product won’t go for mass manufacturing.
 Manufacturing:
Once the product is approved in testing, mass production of the product starts. The
manufacturing line is set up with the required pieces of equipment and tools. The
product is manufactured and dispatched for distribution.
 Distribution:
In this stage, the product is distributed to the customer as per the requirement. A
large distribution network is involved to deliver products to end-user.
 Service:
In this stage of the product, the organization resolves issues or improvements
identified by the customer while using it. These issues are identified, the impact is
analyzed and the required suitable change is implemented.
 Recycle:
Any product has a limitation and it can work for a certain period of time. Over a
period of time, some parts of the product get’s worn out or the product stops giving
the required efficiency, and servicing a product won’t resolve the issue. In this case,
it’s better to get a new part. In this situation, an organization may take back existing
products and recycle them for reuse.
 Disposal:
This is the very last stage of any product. In this stage, the product retires from its
service and it will no longer available for use.

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782957 Introduction to Internship

CHAPTER: 3

INTRODUCTION TO INTERNSHIP

3.1. INTERNSHIP SUMMARY:

During my internship at The Coding Cloud, I worked on a project focused on

automating the Software Development Life Cycle (SDLC) using LangGraph, a
stateful workflow framework for AI-powered systems. The primary objective was
to streamline the entire software development process — from requirement
gathering to deployment — by integrating automation, traceability, and human-in-
the-loop feedback at each phase of the lifecycle.

Key stages automated within the project included:

 Requirement Gathering & User Story Generation: Designed a user-friendly
interface to capture requirements and convert them into detailed user
stories using LLM-based agents.
 Design & Review: Implemented structured review workflows for design
documents with feedback integration.
 Code Generation & Review: Developed code-generation pipelines and
integrated code review checkpoints for quality assurance.
 Security Testing: Embedded automated security validation checks and
loops for addressing vulnerabilities.
 QA Testing & Bug Fixing: Enabled test case generation and QA validation
as conditional nodes within the LangGraph.
 Deployment & Maintenance: Designed a post-deployment feedback
mechanism to initiate maintenance cycles.

Throughout the internship, I gained practical experience with:

 LangGraph for workflow design and state management,
 Python for back-end logic and integration,
 LLM APIs (e.g., OpenAI, Ollama, Groq) for intelligent agent tasks,
 Streamlit (optional UI) for collecting user inputs.

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This project allowed me to explore real-world applications of AI-powered

automation in DevOps and taught me how to manage scalable, collaborative
software workflows using modern tools. It also helped improve my software
engineering fundamentals, especially in agile methodology and SDLC best
practices.

3.2. PURPOSE:
 To get practical knowledge about professional world.
 To experience how things work and how end-to-end products are
delivered.
 To adapt quickly with the ever-growing world by building customer-
oriented products.
 To revolutionize and give back the knowledge you gained back to the
community.

3.3. OBJECTIVE:
 Internships are generally thought of to be reserved for college students looking
to gain experience in a particular field. However, a wide array of people can
benefit from Training Internships in order to receive real world experience and
develop their skills.
 An objective for this position should emphasize the skills you already possess
in the area and your interest in learning more
 Internships are utilized in a number of different career fields, including
architecture, engineering, healthcare, economics, advertising and many more.
 Some internship is used to allow individuals to perform scientific research
while others are specifically designed to allow people to gain first-hand
experience working.

3.4. SCOPE:
 An Internship Provides Real Life Experience and Exposure
 The opportunity to Learn More About Industry
 Get connected with Seniors and Develop your professional network
 To expand your knowledge with the learnings you get while working

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3.5. TOOLS & TECHNOLOGIES:

I was made familiar with various tools and technology over my time at the organization.
Some of them are mentioned below:
 Languages:

1. Python:
Python was the primary programming language used during the internship. It was
chosen for its simplicity, rich ecosystem, and strong community support in AI/ML
and automation. Python powered the entire backend logic — from handling user
input and calling LLM APIs to building and managing workflow states using
LangGraph. Its extensive libraries and readability made it ideal for rapid
development and experimentation.
2. HTML/CSS:
Cascading Style Sheets (CSS) is a style sheet language used for describing the
presentation of a document written in a markup language such as HTML.

3. JavaScript:
JavaScript (JS) is a lightweight, interpreted, or just-in-time compiled programming
language with first-class functions.

 Framework:

1. Langgraph:
LangGraph was the core framework used in the project. It allowed us to define
stateful, node-based workflows that represent each phase of the SDLC — from
requirement gathering to deployment and maintenance. Each node acted as a
function with input/output tracking and the ability to loop back based on conditions
(e.g., feedback or errors). It brought automation, modularity, and clarity to complex
processes.
2. Langchain:
LangChain was used to build LLM-powered chains and agents capable of handling
tasks like summarizing user requirements, writing user stories, suggesting code
snippets, and generating test cases. It also handled prompt templates, memory, and
tool integrations to make the workflows context-aware and interactive.

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3. Streamlit/FastAPI:
To enable interaction with users, a basic UI was created using either Streamlit (for
fast prototyping) or FastAPI (for more scalable API-based setups). This allowed
users to submit requirements, view progress, and interact with the SDLC flow in a
user-friendly manner.

 Databases:

1. MongoDB:
MongoDB, a NoSQL document-oriented database, was used (optionally) to store
structured and unstructured data related to workflow states, feedback logs, and user
inputs.

 Tools:

1. GitHub:
GitHub, Inc. is a provider of Internet hosting for software development and version
control using Git. It offers the distributed version control and source code
management functionality of Git, plus its own features
2. Visual studio code:
Visual Studio Code, also commonly referred to as VS Code, is a source- code editor
made by Microsoft for Windows, Linux and macOS. Features include support for
debugging, syntax highlighting, intelligent code completion, snippets, code
refactoring, and embedded Git

3. OpenAI API / Other LLM APIs

4. Jupyter Notebook:

The Jupyter Notebook is the original web application for creating and sharing
computational documents. It offers a simple, streamlined, document-centric
experience.

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3.6. INTERNSHIP PLANNING:

My Internship Started from 20th of January and will end on 19nd of May. I have
attached my tasks logs and necessary documents as well.

3.6.1. INTERNSHIP APPROACH AND JUSTIFICATION:

Project planning is a discipline addressing how to complete a project in a certain time
frame, usually with defined stages and designated resources.

Tools used for the scheduling parts of a plan include Gantt charts and PERT charts.
Step 1: Identify & Meet with Stakeholders.

Step 2: Set & Prioritize Goals.

Step 3: Define Deliverables.

Step 4: Create the Project Schedule.

Step 5: Identify Issues and Complete a Risk Assessment.

Step 6: Present the Project Plan to Stakeholders.

Project planning is a crucial discipline that focuses on how to effectively complete a

project within a defined timeline, structured in stages and utilizing allocated
resources. For this SDLC automation project using LangGraph, project planning
played a key role in streamlining the various development phases — from user story
creation to final QA verification. Tools such as Gantt charts and task-tracking boards
were helpful in visualizing dependencies and timelines across stages. The process
began by identifying key stakeholders, followed by setting project goals, defining
clear deliverables, creating the project schedule, identifying potential risks, and
finally presenting a comprehensive plan to all involved.

The main objective was to accurately allocate time, cost, and resources to each phase
of the SDLC automation process while minimizing risk and ensuring smooth
integration of LangGraph flows and AI agents. The final output of this planning was
a well-defined project roadmap that guided every stage of development and
automation.

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My internship at The Coding Cloud was thoughtfully structured into two major
phases:

Learning Phase:
In the initial weeks, I was introduced to various tools and technologies aligned with
the company's workflow. The training sessions were intensive yet engaging,
designed to help me adapt quickly to real-world expectations. I gained deep insights
into technologies such as LangChain, LangGraph, Anaconda, FastAPI, and OpenAI
APIs, and learned how to build intelligent automation pipelines. The learning phase
also included practical exposure to cloud deployment and how SDLC stages can be
modeled as AI-powered agents.

Implementation Phase:

Once familiar with the stack, I actively contributed to the actual implementation of
the SDLC automation project. This involved designing agent flows, integrating APIs,
testing different stages like QA and feedback loops, and refining workflows based on
team reviews. I collaborated closely with mentors and teammates, ensuring that each
module was robust and scalable. This phase gave me hands-on experience in
problem-solving, debugging, and deploying intelligent systems in a near-industry
environment. Overall, the internship offered a balanced blend of theoretical learning
and practical application, equipping me with both technical and professional skills
needed for future engineering challenges.

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3.6.2. ROLES AND RESPONSIBILITIES:

During the internship, my role and responsibilities were as below:
 Understood the project scope and goals by collaborating with mentors and team
members.

 Participated in discussions to identify key SDLC components to be automated using

LangGraph.

 Helped document user requirements and convert them into actionable user stories.
 Gained hands-on experience with tools such as LangGraph, LangChain, OpenAI API,
FastAPI, and Anaconda.
 Underwent structured training sessions on workflow automation, agent design, and AI-
based decision-making in software development.
 Built mini-projects to understand integration patterns between different modules.
 Contributed to the design and structuring of the SDLC flow including stages like:
User Story Generation, Design Suggestions, Code Drafting Testing & QA Security
Checks, Feedback Loops
 Used flow diagramming tools to visualize and document the entire lifecycle.
 Implemented different components of the SDLC pipeline using LangGraph.
 Integrated AI agents for tasks like code generation, test case creation, and design
feedback using OpenAI.
 Built API endpoints using FastAPI to connect frontend and backend components.
 Performed unit testing and integration testing to ensure smooth data flow between
LangGraph nodes.
 Debugged and fixed errors in agent responses and logic inconsistencies in flow
execution.
 Validated security prompts and API responses to ensure robustness..
 Maintained proper documentation of code, workflows, and architecture decisions.
 Prepared weekly progress reports and presented demo sessions to mentors.
 Took regular feedback from mentors and iterated on tasks accordingly.
 Participated in team stand-ups and contributed ideas to improve the workflow.

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782957 Introduction to Internship

3.6.3. INTERNSHIP SCHEDULING:

As mentioned in above I had two Internship phases. And below chart
represents the timeline of my period over there.

Fig: 3.6 Internship Timeline

Gujarat Technological University 17 GEC, Modasa

782957 System Analysis

CHAPTER: 4
SYSTEM ANALYSIS:

4.1. STUDY OF CURRENT SYSTEM:

The traditional Software Development Life Cycle (SDLC) involves multiple sequential
and iterative stages, such as requirement gathering, design, development, testing,
deployment, and maintenance. In most organizations, these stages are handled using a
mix of manual documentation, siloed tools, static workflows, and human coordination
— often leading to delays, miscommunication, and inefficiencies.

1) Manual Workflow Management

2) Low Integration Between Stages

3) Limited Use of AI

4) Lack of Dynamic Feedback Loops

5) Inadequate Tracking and Visualization

6) Security and Quality Assurance as Afterthoughts

7) Lack of Automation Systems

8) Workers or professional replacable

4.2. PROBLEM AND WEAKNESSES OF CURRENT SYSTEM:

The current software development life cycle (SDLC) systems suffer from several
fundamental problems and inefficiencies that limit productivity and increase the
complexity of delivering high-quality software. One of the major issues is the lack of
automation — many critical tasks such as generating user stories, reviewing code, and
writing test cases are performed manually, resulting in delays and human errors.
Additionally, the tools used across different stages of the SDLC are often disjointed,
with little to no integration between requirement gathering, development, testing, and
deployment platforms. This fragmentation leads to poor visibility and communication
gaps among cross-functional teams.

The workflows in these systems are typically static and rigid, lacking the flexibility to
adapt dynamically based on feedback or changing requirements. Feedback from
design reviews or quality assurance processes is often delayed, causing costly rework
and wasted time. Moreover, the minimal use of artificial intelligence and intelligent
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782957 System Analysis
automation in these systems means that repetitive and cognitive tasks are not being
optimized. Security practices are also a concern, as they are usually incorporated only
in the final stages, making it harder to catch vulnerabilities early in the development
cycle.

Furthermore, current systems offer limited traceability and real-time visibility into the
project’s progress, making it difficult to track changes, assess the status of individual
modules, or audit the feedback received. They also struggle with scalability and are
not well-suited for modern agile or CI/CD methodologies, requiring frequent manual
coordination and updates whenever changes occur. All these factors highlight the
pressing need for a more integrated, automated, and intelligent SDLC framework —
one that the proposed LangGraph-based system seeks to address.

4.3. REQUIREMENTS OF NEW SYSTEM:

The new system aims to overcome the limitations of the traditional SDLC by
introducing a smart, automated, and interconnected development workflow powered by
LangGraph and AI tools. The system should be capable of dynamically modeling each
phase of the SDLC — from requirement gathering and user story generation to
development, testing, security review, and deployment — as interconnected nodes
within a stateful graph. It must enable automation of repetitive and time-consuming
tasks using large language models (LLMs), such as generating test cases, suggesting
design improvements, or conducting initial code reviews. Integration and seamless flow
between each phase are essential, allowing feedback loops to return to earlier stages
when issues are detected.

The system should also include robust tracking and visualization capabilities to monitor
progress across nodes, including approvals, revisions, and completions. It must support
real-time collaboration between stakeholders and ensure proper data management using
lightweight or scalable database solutions like SQLite or MongoDB. In addition, it
should incorporate security and quality assurance early in the lifecycle through
automated checks and AI-driven suggestions. The system must be user-friendly,
accessible via a simple interface or API, and adaptable to agile and CI/CD development
methodologies. Overall, the new system should promote faster development cycles,
enhanced collaboration, improved code quality, and a smarter, feedback-driven
approach to software engineering.

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782957 System Analysis

4.4. PROJECT FEASIBILITY STUDY:

 Technical Feasibility
The proposed system is technically feasible and well-supported by modern
development tools and frameworks. It uses LangGraph for modeling complex
workflows and integrates with LangChain and LLM APIs such as OpenAI for AI-
driven automation. These technologies are open-source, well-documented, and
scalable for future enhancements. The backend is built using Python and FastAPI,
which are lightweight, efficient, and commonly used in modern software projects.
Additionally, the system supports integration with databases like SQLite and
MongoDB, which are reliable and suitable for different use cases. With this tech
stack, developers can rapidly build, test, and deploy applications while maintaining
modularity and flexibility.

 Economic Feasibility

Economically, the project is highly viable. Most of the tools and technologies used
are free or have generous open-source or freemium tiers. For instance, LangGraph,
LangChain, and Python libraries do not require licensing costs, while platforms like
OpenAI offer pay-as-you-go models suitable for controlled API usage during
development. The reduction in manual labor due to automation (e.g., AI-generated
test cases, user stories, and design suggestions) results in long-term cost savings.
Moreover, the minimal infrastructure requirements (like local development
environments or low-cost cloud deployment) make the system affordable even for
startups, academic projects, or internship-based deployments.

 Operational Feasibility
Operationally, the system is designed for ease of use and efficient collaboration. It
fits seamlessly into modern agile development processes and can be integrated with
CI/CD pipelines for continuous deployment. The workflow is dynamic and state-
based, allowing users to navigate easily between stages like design, QA, security
testing, and code generation. Real-time updates, feedback loops, and visual tracking
enhance collaboration among stakeholders. Additionally, the use of AI agents for
repetitive tasks improves developer productivity and reduces errors. The intuitive
structure ensures that even non-technical users or interns can understand and
contribute effectively with minimal training.

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782957 System Analysis

4.5. FEATURES OF NEW SYSTEM:

The proposed SDLC system, powered by LangGraph and AI technologies, introduces

a range of innovative features that enhance automation, collaboration, and software
quality throughout the development lifecycle. One of the key features is AI-driven
workflow automation, where each stage of the SDLC — from user story generation
and system design to testing and deployment — is handled by intelligent agents. These
agents use large language models (LLMs) to generate, evaluate, and refine outputs,
significantly reducing manual effort and increasing consistency.

The system also supports dynamic and stateful flow management using LangGraph,
which allows stages to be interconnected like a graph rather than following a rigid
linear flow. This enables seamless transitions between phases, automated feedback
loops, and revisits to earlier stages when required — for example, if QA or security
testing identifies issues that need changes in design or code. Another major feature is
real-time collaboration and traceability, where every decision, change, and update is
logged and visually tracked, allowing teams to stay aligned and ensuring
accountability across the project lifecycle.

Additionally, the system integrates security and quality checks at every stage, rather
than treating them as final steps. AI agents can conduct automated security reviews,
generate test cases, and even analyze coverage gaps. The system also features
integration with version control and CI/CD tools, enabling automatic testing and
deployment pipelines. It offers a user-friendly interface or API access so that team
members with varying technical backgrounds can interact with the system easily.

In summary, the new system offers intelligent automation, flexible flow control, deep
integration across SDLC stages, and a strong focus on quality and security — all of
which contribute to faster delivery, better code quality, and enhanced team
productivity.

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782957 System Design

CHAPTER 5
SYSTEM DESIGN
5.1. SYSTEM DESIGN & METHODOLOGY:

Use Case Diagrams:

Fig 5.1: Use Case Diagram

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782957 System Design
Data Flow Diagrams:

Context-Level (Level 0) DFD

Fig 5.2: DFD 0 Level Diagram

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782957 System Design
Level 1 DFD

Fig 5.3: DFD 1 level Diagram

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782957 System Design

Level 2 DFD

Fig 5.4: DFD 2 level Diagram

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782957 System Design
Class Diagram

Fig 5.5: Class Diagram

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782957 System Design

Project Workflow Diagram

Product Owner

Fig 5.6 : Business Analyst & Product Owner

Software Developer

Fig 5.7 : Software Engineer & Software Tester

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782957 System Design

Quality Assurance Engineer

Fig 5.8: QA Engineer & DevOps Engineer

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782957 Implementation

CHAPTER: 6

IMPLEMENTATION:

6.1 IMPLEMENTATION PLATFORM/ ENVIRONMENT:

At the beginning of my internship, I was provided with company’s system where I

configured all the necessary dependencies and tools.

So, in order to implement or execute the daily tasks that were based totally on

Python the platform was to be configured using:

1. Python:

To Build some projects that required Python 3.10. I have used Django to built

API’s. Hence Python was required to build backend.

2. Jupyter notebook:

Jupyter Notebook is probably the first Python IDE we used for data science. Its

simplicity makes it great for beginners, but whenever you need some advanced

functionality, you'll see that Jupyter Notebook has a lot of limitations.

3. Pycharm:

PyCharm is a dedicated Python Integrated Development Environment (IDE)

providing a wide range of essential tools for Python developers, tightly

integrated to create a convenient environment for productive Python, web, and

data science development.

4. Vscode:

Visual Studio Code is a streamlined code editor with support for development

operations like debugging, task running, and version control. It aims to provide

just the tools a developer needs for a quick code-build-debug cycle and leaves

more complex workflows to fuller featured IDEs, such as Visual Studio IDE.

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782957 Implementation

5. Git:

Git is software for tracking changes in any set of files, usually used for

coordinating work among programmers collaboratively developing source code

during software development. Its goals include speed, data integrity, and support

for distributed, non-linear workflows.

6. Anaconda Environment:

Used for creating and managing isolated virtual environments with all necessary

dependencies for the project. It simplifies package management and ensures

reproducibility of the environment.

7. LangChain:

Acts as the bridge between the application and LLMs (like OpenAI). It provides

tools to build AI agents, manage prompts, and handle memory across

conversation/state transitions.

8. Langgraph:

Used to construct the SDLC pipeline as a dynamic, state-based graph where

each node represents a software lifecycle task like design, code generation,

testing, or QA.

9. Open AI GPT API:

Provides the LLM capabilities needed to generate user stories, test cases, design

patterns, and provide suggestions or reviews..

10. FastAPI:

A high-performance web framework used to build REST APIs for handling

inputs/outputs from the SDLC pipeline, managing node triggers, and integrating

with frontend or external tools.

11. Swagger UI:

Provides interactive API documentation that helps developers test endpoints.

Gujarat Technological University 30 GEC, Modasa

782957 Implementation
6.2 PROCESS AND TECHNOLOGY:

 Languages:

1. Python:
Python was the primary programming language used during the internship. It
was chosen for its simplicity, rich ecosystem, and strong community support
in AI/ML and automation. Python powered the entire backend logic — from
handling user input and calling LLM APIs to building and managing
workflow states using LangGraph. Its extensive libraries and readability
made it ideal for rapid development and experimentation.
2. HTML/CSS:
Cascading Style Sheets (CSS) is a style sheet language used for describing the
presentation of a document written in a markup language such as HTML.

3. JavaScript:
JavaScript (JS) is a lightweight, interpreted, or just-in-time compiled
programming language with first-class functions.
 Framework:

4. Langgraph:
LangGraph was the core framework used in the project. It allowed us to
define stateful, node-based workflows that represent each phase of the SDLC
— from requirement gathering to deployment and maintenance. Each node
acted as a function with input/output tracking and the ability to loop back
based on conditions (e.g., feedback or errors). It brought automation,
modularity, and clarity to complex processes.
5. Langchain:
LangChain was used to build LLM-powered chains and agents capable of
handling tasks like summarizing user requirements, writing user stories,
suggesting code snippets, and generating test cases. It also handled prompt
templates, memory, and tool integrations to make the workflows context-
aware and interactive.
6. Streamlit/FastAPI:
To enable interaction with users, a basic UI was created using either Streamlit
(for fast prototyping) or FastAPI (for more scalable API-based setups). This
allowed users to submit requirements, view progress, and interact with the
SDLC flow in a user-friendly manner.

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782957 Implementation

 Databases:

1. MongoDB:
MongoDB, a NoSQL document-oriented database, was used (optionally) to
store structured and unstructured data related to workflow states, feedback
logs, and user inputs.
 Tools:

1. GitHub:
GitHub, Inc. is a provider of Internet hosting for software development and
version control using Git. It offers the distributed version control and source
code management functionality of Git, plus its own features
2. Visual studio code:
Visual Studio Code, also commonly referred to as VS Code, is a source- code
editor made by Microsoft for Windows, Linux and macOS. Features include
support for debugging, syntax highlighting, intelligent code completion,
snippets, code refactoring, and embedded Git

3. OpenAI API / Other LLM APIs

4. Jupyter Notebook:

The Jupyter Notebook is the original web application for creating and sharing
computational documents. It offers a simple, streamlined, document-centric
experience.

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782957 Implementation

6.3 RESULTS/OUTCOME:

1. Streamlit Page:

Fig 6.1: Project: Streamlit Page

2. User Requirements :

Fig 6.2: Project: User Requirements

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782957 Implementation

3. Session Information:

Fig 6.3: Project Session

4. User Stories:

Fig: Project: Application States

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782957 Implementation

Fig 6.4: Project: User Stories

5. Design Document State:

Fig 6.5: Project: Functional Design

6. Generate Code and also Download the Code:

Fig 6.6: Project: Code Generation

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782957 Implementation

7. Security State:

Fig 6.7: Project: Security Guidelines of the Project

8. Testing State:

Fig 6.8: Project: Test Cases Generation

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782957 Implementation
9. Final State:

Fig 6.9: Project: Deployment Ready

10. Artifact Download:

Fig 6.10: Project: Final Summary

11. Uploading to GitHub:

Fig 6.11: Project: Export the project to GitHub

Gujarat Technological University 37 GEC, Modasa

782957 Testing

CHAPTER: 7

TESTING

7.1 TESTING PLAN:

A Test Plan refers to a detailed document that catalogues the test strategy, objectives,
schedule, estimations, deadlines, and the resources required for completing that
particular project. Think of it as a blueprint for running the tests needed to ensure the
software is working properly – controlled by test managers.

A well-crafted test plan is a dynamic document that changes according to

progressions in the project and stays current at all times. It is the point of reference,
based on which testing activities are executed and coordinated among a QA team.

Depending on the product and the responsibility of the organization to which the test
plan applies, a test plan may include one or more of the following:

1. Design Verification or Compliance test ‐ to be performed during the

development or approval stages of the product, typically on a small
sample of units.
2. Manufacturing or Production test ‐ to be performed during preparation or
assembly of the product in an ongoing manner for purposes of
performance verification and quality control.
3. Acceptance or Commissioning test ‐ to be performed at the time of
delivery or installation of the product.
4. Regression test ‐ to be performed on an existing operational product, to
verify that existing functionality didn't get broken when other aspects of
the environment are changed (e.g., upgrading the platform on which an
existing application runs).

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782957 Testing

7.2 TEST STRATEGY:

Fig 7.1: Testing Strategy

 The testing strategy followed by the company is unique in its own way.
 The developer first takes into account the UNIT Testing.
 Then the Integration testing is conducted to check the over functionality of the
system.
 Then the Validation Testing is performed once the whole project is done. Alpha and
Beta testing are done once by the testing team and the clients respectively.
 Then the over System testing is done and after that Acceptance testing is done.

7.3 TEST METHODS:

Software testing methods are traditionally divided into white and black‐box testing.

Unit Testing

 Black Box Testing - Whether the particular class meets the requirements
mentioned in the specification.
 White Box Testing - The tester looks inside that class and checks if there is
error in the code which is not found while testing the class as a black box.

Integration Testing

 User Interface Testing - Testing is done by moving through each and every
menu item in the interface either in top‐down manner or bottom‐up manner.
 Interaction Testing - When the system performs data processing, Interaction
between various classes is tested.

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782957 Testing

Validation Testing

 For Validation Testing stage, we have performed functional test cases and the
results are compared in the form of actual and expected outcomes.
 The testing proved that the Validation was compliant with the requirements
as specified in the Use Case and SRS (Software Requirement Specification).

System Testing

 It is carried to see that functionality related sets of units used together function
as designed.
 The system test specifications, incorrect operation of the system is narrowed
down to incorrect operation of unit(s) and is taken care of by filing the units.
 Test data covers the possible values of each parameter based on the
requirements.

Acceptance Testing

 After each module completion, the system tester tested the system to check
user acceptance and changes are made accordingly as per requirements.

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782957 Testing

7.4 TESTING IN SDLC:

Testing played a crucial role in ensuring that each stage of the SDLC automation flow
worked as intended. Our project combined various modern technologies, and so the
testing strategy included both unit testing and functional testing, using tools suited to
the tech stack we employed.

 We utilized Python's built-in unittest framework to validate individual functions and

logic inside each LangGraph node (e.g., user story generation, code writing, QA,
and feedback loop). Test classes were written in a class-based approach and
followed the structure provided by the unittest module. These tests ensured that
prompt logic, API calls, and internal data transformations within the graph executed
correctly.
 For FastAPI integration, we used pytest along with FastAPI's TestClient to
simulate API calls and test route responses. This allowed us to validate:
Response status codes ,Payload validation, Agent execution output for each
route
 Although our main focus was backend automation, parts of the system
included user interaction components built with React (or planned in the
future). For this, React Testing Library (RTL) was proposed for: Component
rendering validation ,UI interaction simulation (clicks, inputs) ,Ensuring
feedback or results from LangGraph appeared correctly on the frontend
 Though not used directly, a similar approach was adopted for locating tests
structured across different directories and modules. A discovery-based runner
helped organize tests cleanly across components (LangGraph nodes, utility
functions, FastAPI routes).
 If the system were to extend into Django (future scope), tools like django-
webtest would be ideal for functional end-to-end tests mimicking the actual
user experience. This would be helpful in testing flows where LangGraph
outputs affect rendered templates or UI behavior.

Gujarat Technological University 41 GEC, Modasa

782957 Conclusion and Discussion

CHAPTER 8:

CONCLUSION AND DISCUSSION

8.1 OVERALL ANALYSIS OF INTERNSHIP:

Internship training plays a crucial role in bridging the gap between theoretical
academic knowledge and real-world industrial practices. My internship at The
Coding Cloud offered an enriching experience that helped me understand how
cutting-edge technologies like LangGraph, LangChain, and OpenAI APIs can be
integrated to automate and enhance the Software Development Life Cycle (SDLC).
This hands-on experience gave me the opportunity to work on actual modules of a
live project, interact with mentors, and collaborate with a development team in a
real-time environment.

The internship was not limited to just coding; it provided exposure to design thinking,
API integration, testing strategies, security awareness, and workflow automation. I
learned how software engineering is applied in the real world — from planning user
stories and designing system architecture to testing and iterating with feedback loops.
This opportunity helped me understand not only the technical aspects but also project
management practices, time management, and the importance of team coordination
and communication.

The most memorable moment of my internship was seeing the complete SDLC graph
flow in action — starting from the AI-generated user stories to the automatic QA
testing and feedback loop. Witnessing the transition of our design into a working
system with intelligent automation was truly rewarding. Overall, this internship
significantly improved my technical knowledge, problem-solving skills, and
confidence to tackle large-scale engineering challenges, shaping me into a more
capable and industry-ready software engineer.

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782957 Conclusion and Discussion

8.2 DATES OF CONTINUOUS EVALUATION (IT):

Table 8.1 Continuous Evaluation

Review Date of Evaluation

Review I 15th February 2025
Review II 29th March 2025
Review III 24th April 2025

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 782957 Conclusion and Discussion

8.3 CONCLUSION:

Overall, this internship at The Coding Cloud has been an eye-opening and incredibly
valuable experience for me. It gave me the chance to step into the real world of
software development and work hands-on with cutting-edge technologies like
LangGraph, LangChain, and FastAPI. I’ve learned so much — from building and
debugging actual SDLC flows to understanding how important time management,
clear communication, and self-motivation are in a team environment. There were
moments when I struggled, especially with some technical tasks that needed multiple
revisions, but those challenges helped me grow. I now understand how crucial it is to
take ownership of my work, ask the right questions, and stay committed to delivering
quality. While there’s still a lot to learn, this experience has made me more confident
and better prepared for future opportunities in the tech industry.

8.4 LIMITATION AND FUTURE ENHANCEMENT:

While the current system built using LangGraph successfully automates and
streamlines the Software Development Life Cycle (SDLC), there is still ample scope
for future enhancements and added functionality. One limitation is that the current
flow primarily focuses on basic SDLC stages like user story generation, code design,
QA testing, and feedback loops. In future iterations, the system can be extended to
include automated code deployment, CI/CD pipeline integration, and real-time
performance monitoring. Another potential improvement is to incorporate a user
dashboard that tracks the progress of each SDLC node, visualizes logs, and shows
metrics such as time saved or issues resolved. Adding a role-based access control
(RBAC) system would also enhance security and allow multiple teams (e.g.,
developers, testers, managers) to collaborate within the same environment. Moreover,
the system could integrate with platforms like GitHub, Slack, or Jira to send
automated notifications, issue creation, and task updates. Finally, enhancing the AI
agents to handle code refactoring, unit test generation, or even security audits would
make the system much more dynamic, intelligent, and production-ready. As the field
of AI continues to evolve, this project has great potential to grow into a fully
autonomous and scalable software engineering assistant.

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CHAPTER 9:

REFERENCES:

1. https://docs.python.org/

2. https://www.w3schools.com/python/

3. https://docs.djangoproject.com/

4. https://www.geeksforgeeks.org/django-tutorial/

5. https://html.com/

6. https://www.w3schools.com/css/

7. https://www.javatpoint.com/javascript-tutorial

8. https://getbootstrap.com/

9. https://www.langchain.com/langgraph

10. https://docs.streamlit.io/

11. https://huggingface.co/

12. https://www.langchain.com/

13. https://fastapi.tiangolo.com/

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CHAPTER: 10

Daily Logs:

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