A PROJECT REPORT ON

Realtime Web IDE with Code

Recommendation and Voice Recognition

SUBMITTED TO THE SAVITRIBAI PHULE PUNE UNIVERSITY ,

PUNE IN THE PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE AWARD OF THE DEGREE

BACHELOR OF ENGINEERING

(Computer Engineering)

Mr. Shirsath Shivanand

Exam No. B190114266

Ms. Rokade Deeksha

Exam No. B190114253

Ms. Kothawade Piyusha

Exam No. B190114236

Mr. Tuwar Vinayak

Exam No. B190114275

Under The Guidance of

Dr.M.R.Bendre

DEPARTMENT OF COMPUTER ENGINEERING

Pravara Rural Engineering College

Loni

SAVITRIBAI PHULE PUNE UNIVERSITY, PUNE

CERTIFICATE

This is to certify that the Project Entitled

Realtime Web IDE with Code

Recommendation and Voice Recognition

Submitted by

Mr. Shirsath Shivanand

Exam No. B190114266

Ms. Rokade Deeksha

Exam No. B190114253

Ms. Kothawade Piyusha

Exam No. B190114236

Mr. Tuwar Vinayak

Exam No. B190114275

is a bonafide work carried out by them under the supervision of

DR.M.R.Bendre and it is approved for the partial fulfillment of the

requirement of Savtribai Phule Pune university, Pune for the award of the

degree of Bachelorof Engineering (Computer Engineering).

Dr.M.R.Bendre Dr. M.R.Bendre

Guide H.O.D

Dept. of Computer Engg. Dept. of Computer Engg.

Dr.S.M.Ghulane

Principal

Pravara Rural Engineering College, Loni

Signature of Internal Examiner Signature of External Examiner

PROJECT APPROVAL SHEET

A Project Report Titled as

Realtime Web IDE with Code

Recommendation and Voice Recognition

Is verified for its originality in documentation, problem statement, proposed

work and implementation successfully completed by

Mr. Shirsath Shivanand

Exam No. B190114266

Ms. Rokade Deeksha

Exam No. B190114253

Ms. Kothawade Piyusha

Exam No. B190114236

Mr. Tuwar Vinayak

Exam No. B190114275

DEPARTMENT OF COMPUTER ENGINEERING

Pravara Rural Engineering Collee, Loni

SAVITRIBAI PHULE PUNE UNIVERSITY,PUNE

ACADEMIC YEAR 2019-2023

Dr.M.R.Bendre Dr. M.R.Bendre

Guide H.O.D

Dept. of Computer Engg. Dept. of Computer Engg.

Abstract

A real-time Integrated Development Environment (IDE) that incorporates

code recommendation and speech recognition features. The IDE is designed

to assist developers by providing real-time code suggestions as they type,

helping to reduce coding errors and increase efficiency. The speech

recognition feature allows developers to dictate code, navigate through the

codebase, and execute commands with ease, making coding accessible to

individuals with physical disabilities or those who prefer a hands-free

approach. The IDE utilizes machine learning algorithms to recommend

relevant code snippets based on the current context and the developer's

coding history. These suggestions are displayed in a user-friendly interface,

allowing developers to easily choose and insert the recommended code into

their projects. The speech recognition feature is powered by advanced

natural language processing technology, enabling the IDE to accurately

transcribe spoken commands and code. Overall, this real-time IDE with code

recommendation and speech recognition is designed to improve developer

productivity, reduce coding errors, and make coding more

accessible for everyone

Acknowledgments

It gives us great pleasure in presenting the preliminary project report on

Realtime Web IDE with Code Recommendation and Voice Recognition

I would like to take this opportunity to thank my internal guide and Head of

department

Dr.M.R.Bendre

for giving me all the help and guidance I

needed. I am really grateful to them for their kind support. Their valuable

suggestions were very helpful.

Shirsath Shivanand

Rokade Deeksha

Kothawade Piyusha

Tuwar Vinayak

(B.E. Computer Engg.)

Contents

Synopsis 1

1.1

Project Title

...........................................................................................

1.2

Project Option

......................................................................................

1.3

Internal Guide

.......................................................................................

1.4

Sponsorship and External Guide

.......................................................

1.5

Technical Keywords (As per ACM Keywords)

...................................

1.6

Problem Statement................................................................................ 2

1.7

Abstract

..................................................................................................

1.8

Goals and Objectives

............................................................................

1.9

Relevant mathematics associated with the Project

............................

1.10

Names of Conferences / Journals where papers can be published 3

1.11

Review of Conference/Journal Papers supporting Project idea 4

1.12

Plan of Project Execution

....................................................................

Technical Keywords 5

2.1

Area of Project

......................................................................................

2.2

Technical Keywords

..............................................................................

Introduction 7

3.1

Project Idea

...........................................................................................

3.2

Motivation of the Project......................................................................8

3.3

Literature Survey

..................................................................................

Problem Definition and scope 9

4.1

Problem Statement.............................................................................. 10

4.1.1

Goals and objectives

...............................................................

4.1.2

Statement of scope................................................................. 10

4.2

Major Constraints

...............................................................................

4.3

Methodologies of Problem solving and efficiency issues..................10

4.4

Outcome

...............................................................................................

4.5

Applications

.........................................................................................

4.6

Hardware Resources Required

............................................................

4.7

Software Resources Required

..............................................................

Project Plan 12

5.1

Project Estimates

................................................................................

5.1.1

Reconciled Estimates

.............................................................

5.1.2

Project Resources.................................................................... 13

5.2

Risk Management w.r.t. NP Hard analysis

......................................

5.2.1

Risk Identification

...................................................................

5.2.2

Risk Analysis

......................................................................

5.2.3

Overview of Risk Mitigation, Monitoring, Management 14

5.3

Project Schedule...................................................................................17

5.3.1

Project task set

.......................................................................

5.3.2

Task network

..........................................................................

5.3.3

Timeline Chart

........................................................................

5.4

Team Organization

.............................................................................

5.4.1

Team structure

.......................................................................

5.4.2

Management reporting and communication......................... 17

Software requirement specification 18

6.1

Introduction

..........................................................................................

6.1.1

Purpose and Scope of Document...........................................19

6.1.2

Overview of responsibilities of Developer

.............................

6.2

Usage Scenario..................................................................................... 19

6.2.1

User profiles

.............................................................................

6.2.2

Use-cases..................................................................................19

6.2.3

Use Case View

........................................................................

6.3

Data Model and Description

.............................................................

6.3.1

Data Description

.....................................................................

6.3.2

Data objects and Relationships

............................................

6.4

Functional Model and Description

...................................................

6.4.1

Data Flow Diagram

................................................................

6.4.2

Activity Diagram:

....................................................................

6.4.3

Non Functional Requirements:............................................... 21

6.4.4

State Diagram:

........................................................................

6.4.5

Design Constraints

.................................................................

6.4.6

Software Interface Description............................................... 22

Detailed Design Document using Annexure A and B 23

7.1

Introduction

..........................................................................................

7.2

Architectural Design

...........................................................................

7.3

Data design (using Annexures A and B)

.....................................

7.3.1

Internal software data structure

..............................................

7.3.2

Global data structure

...............................................................

7.3.3

Temporary data structure

.....................................................

7.3.4

Database description

..............................................................

7.4

Compoent Design................................................................................ 25

7.4.1

Class Diagram

.......................................................................

Project Implementation 27

8.1

Introduction.......................................................................................... 28

8.2

Tools and Technologies Used

.............................................................

8.3

Methodologies/Algorithm Details

.....................................................

8.3.1

Algorithm 1/Pseudo Code

.....................................................

8.3.2

Algorithm 2/Pseudo Code

.....................................................

8.4

Verification and Validation for Acceptance

.....................................

Software Testing 29

9.1

Type of Testing Used

..........................................................................

9.2

Test Cases and Test Results

..............................................................

Results 31

10.1

Screen shots......................................................................................... 32

10.2

Outputs

................................................................................................

Deployment and Maintenance 33

11.1

Installation and un-installation

.........................................................

11.2

User help.............................................................................................. 34

Conclusion and Future Scope 35

References 36

Annexure A Laboratory assignments on Project Analysis of Al-

gorithmic Design

Annexure B Laboratory assignments on Project Quality and

Reliability Testing of Project Design

Annexure C Project Planner 43

Annexure D Reviewers Comments of Paper Submitted 45

Annexure E Plagiarism Report 47

Annexure F Term-II Project Laboratory Assignments 49

Annexure G Information of Project Group Members 51

List of Figures

6.1

Use case diagram.................................................................................20

6.2

State transition diagram

....................................................................

7.1

Architecture diagram

..........................................................................

7.2

Class Diagram

...................................................................................

List of Tables

4.1

Hardware

Requirements. . . . . . . . . . . . . . . .

. . . . .

5.1

Risk Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5.2

Risk Probability definitions [

] . . . . . . . . . . . . . . . . . .

5.3

Risk Impact definitions

[?]. . . . . . . . . . . . . . . . . .

6.1

Use

Cases . . . . . . . . . . . . . . . . . .

. . . . . . . . . .

A.1

IDEA

Matrix . . . . . . . . . . . . . . . . .

. . . . . . . . . .

CHAPTER 1

SYNOPSIS

1.1

Project Title

Realtime Web IDE with Code Recommendation and Voice Recognition

1.2

Project Option

Internal Project

1.3

Internal Guide

Dr.M.R.Bendre

1.4

Sponsorship and External Guide

None

1.5

Technical Keywords (As per ACM Key-

words)

Real time Integrated development environment, code recommendations, voice

recommendations, voice interaction,

code generation

1.6

Problem Statement

In IDE command recommendation, the cost of making a

wrong decision is low: the rejection of a good

recommendation preserves status quo in the development

process, while the acceptance, i.e., execution, of a bad

recommendation sometimes results in an undesired

modification of the artefacts under development, which

fortunately can be often reverted with minimal effort. If

command recommendations are presented adequately, the

time needed to take a decision can be short. The median

time spent for reading command recommendations was less

than 11 seconds stream of radar data into blocks and then

applied localization and classification algorithms to detect

individual characters. seeking to gain an understanding of

user instincts when giving vocal commands to a computer.

What commands are programmers inclined to give a

computer (using their voices) in order to write and edit code?

What are the largest points of friction, and the most difficult

tasks to complete? Our aim at identifying and presenting to

IDE users the right functionality that can help them to

accomplish their daily tasks, without proposing well known

or useless information. The number of recommended

commands that a user can learn from an our is not limited.

We aspire to build a system that will help people with

impaired limbs to use coding platform without hesitation.

Coding can be accessible in hands free mode with minimal

use of keyboard.

1.7

Abstract

A real-time Integrated Development Environment (IDE)

that incorporates code recommendation and speech

recognition features. The IDE is designed to assist

developers by providing real-time code suggestions as they

type, helping to reduce coding errors and increase efficiency.

The speech recognition feature allows developers to dictate

code, navigate through the codebase, and execute commands

with ease, making coding accessible to individuals with

physical disabilities or those who prefer a hands-free

approach. The IDE utilizes machine learning algorithms to

recommend relevant code snippets based on the current

context and the developer's coding history. These

suggestions are displayed in a user-friendly interface,

allowing developers to easily choose and insert the

recommended code into their projects. The speech

recognition feature is powered by advanced natural language

processing technology, enabling the IDE to accurately

transcribe spoken commands and code. Overall, this real-

time IDE with code recommendation and speech recognition

is designed to improve developer productivity, reduce

coding errors, and make coding more

accessible for everyone.

1.8

Goals and Objectives

To build a development environments that can reduce the

complexity tasks in an IDE with simple recommendations.

▪ To provide a platform for developers to code hands free.

▪ To allow multiple developers to collaborate at the same

time and helping out and correcting codes.

▪ To display errors and provide their solutions then and there

itself.

▪ To visualize the output in real time

▪ To allow user to save their work virtually using cloud

without having to actually spare their device storage.

1.9

Relevant mathematics associated with the

Project

1. Machine Learning and Natural Language Processing

(NLP):

- Probability and Statistics: Understanding probability

distributions, statistical analysis, and hypothesis testing is

crucial for training code recommendation models and

language models used in speech recognition.

- Linear Algebra: Concepts such as matrices, vectors,

and matrix operations are used in various machine

learning algorithms, including recommendation systems

and NLP tasks like word embeddings.

- Calculus: Knowledge of calculus is essential for

optimization algorithms used in machine learning, such

as gradient descent.

2. Algorithms and Data Structures:

- Data Structures: Understanding data structures like

arrays, linked lists, trees, and hash tables is important for

efficient code recommendation systems and data

representation.

- Graph Theory: Algorithms related to graphs, such as

depth-first search, breadth-first search, and shortest path

algorithms, can be useful for various aspects of the IDE,

including code analysis and recommendation.

3. Pattern Recognition and Analysis:

- Signal Processing: Techniques like Fourier

transforms, convolution, and filtering can be applied to

preprocess audio data in speech recognition systems.

- Pattern Recognition: Concepts like feature extraction,

clustering, and classification algorithms are relevant for

identifying patterns in code and speech data.

4. Computational Linguistics:

- Syntax and Grammar: Understanding the formal

syntax and grammar rules of programming languages is

essential for building code recommendation systems that

provide accurate suggestions.

- Natural Language Understanding (NLU): Concepts

like syntactic parsing, semantic analysis, and sentiment

analysis are important for speech recognition systems to

interpret and understand spoken commands.

5. Optimization:

- Optimization Theory: Understanding optimization

techniques, such as linear programming, convex

optimization, and nonlinear optimization, is relevant for

improving the performance of code recommendation

algorithms and speech recognition models.

1.10

Names of Conferences / Journals

wherepapers can be published

IEE

IJARIIE

1.11

Review of Conference/Journal

Papers sup-porting Project idea

Atleast 10 papers

White papers or web references

Brief literature survey [ Description containing important

description of atleast 10 papers]

1.12

Plan of Project Execution

1. Project Initiation:

a. Define the project objectives, scope, and deliverables.

b. Identify the stakeholders and establish

communication channels.

c. Formulate a project team with the necessary skill sets.

d. Allocate resources and create a project schedule.

2. Requirements Gathering:

a. Conduct meetings with stakeholders to understand

their needs.

b. Document functional and non-functional

requirements.

c. Define the user interface and user experience

expectations.

d. Identify the necessary code recommendation and

speech recognition functionalities.

3. Design and Architecture:

a. Develop an architectural design for the real-time web

IDE.

b. Define the data models, APIs, and interfaces required.

c. Design the user interface and workflows.

d. Create a technical design document outlining the

system components.

4. Development:

a. Implement the backend infrastructure for the web IDE.

b. Integrate code recommendation algorithms and

speech recognition libraries.

c. Develop the frontend components and user interface.

d. Implement real-time collaboration features.

e. Conduct thorough testing at each development stage.

5. Code Recommendation and Speech Recognition

Integration:

a. Train and fine-tune code recommendation models

based on relevant datasets.

b. Integrate the code recommendation system into the

IDE.

c. Implement speech recognition functionality using

suitable libraries or APIs.

d. Test and validate the accuracy and performance of

the recommendation and recognition systems.

6. Real-time Collaboration:

a. Implement collaborative editing and version control

features.

b. Ensure synchronization of code changes in real-time.

c. Test and refine the collaborative functionalities.

7. Testing and Quality Assurance:

a. Perform unit testing for individual components.

b. Conduct integration testing to ensure smooth system

interactions.

c. Test the system for performance, security, and

scalability.

d. Perform user acceptance testing with a group of users.

e. Identify and resolve any bugs or issues.

8. Deployment and Release:

a. Prepare the system for deployment on a production

environment.

b. Set up necessary servers, databases, and hosting

infrastructure.

c. Migrate the application to the production

environment.

d. Conduct final testing to ensure the system is ready for

release.

CHAPTER 2

TECHNICAL KEYWORDS

2.1

Area of Project

Code Editor, Code Recommender, Voice Recognizer

2.2

Technical Keywords

1. Machine Learning and Natural Language Processing

(NLP):

- Probability and Statistics: Understanding probability

distributions, statistical analysis, and hypothesis testing is

crucial for training code recommendation models and

language models used in speech recognition.

- Linear Algebra: Concepts such as matrices, vectors, and

matrix operations are used in various machine learning

algorithms, including recommendation systems and NLP

tasks like word embeddings.

- Calculus: Knowledge of calculus is essential for

optimization algorithms used in machine learning, such as

gradient descent.

2. Algorithms and Data Structures:

- Data Structures: Understanding data structures like

arrays, linked lists, trees, and hash tables is important for

efficient code recommendation systems and data

representation.

- Graph Theory: Algorithms related to graphs, such as

depth-first search, breadth-first search, and shortest path

algorithms, can be useful for various aspects of the IDE,

including code analysis and recommendation.

3. Pattern Recognition and Analysis:

- Signal Processing: Techniques like Fourier transforms,

convolution, and filtering can be applied to preprocess audio

data in speech recognition systems.

- Pattern Recognition: Concepts like feature extraction,

clustering, and classification algorithms are relevant for

identifying patterns in code and speech data.

4. Computational Linguistics:

- Syntax and Grammar: Understanding the formal syntax

and grammar rules of programming languages is essential for

building code recommendation systems that provide accurate

suggestions.

- Natural Language Understanding (NLU): Concepts like

syntactic parsing, semantic analysis, and sentiment analysis

are important for speech recognition systems to interpret and

understand spoken commands.

5. Optimization:

- Optimization Theory: Understanding optimization

techniques, such as linear programming, convex optimization,

and nonlinear optimization, is relevant for improving the

performance of code recommendation algorithms and speech

recognition models.

6. Graphical User Interface (GUI) Design:

- Geometry: Principles of geometry, including coordinate

systems, transformations, and spatial relationships, are

relevant for designing and positioning graphical elements in

the IDE's user interface.

7. Data Analysis and Visualization:

- Descriptive and Inferential Statistics: Applying

statistical techniques to analyze and summarize data

generated by user interactions, code recommendations, and

speech recognition accuracy.

- Data Visualization: Representing data and results using

charts, graphs, and visualizations to provide insights and

facilitate decision-making.

CHAPTER 3

INTRODUCTION

3.1

Project Idea

•Code Collaboration Platform: Create an IDE that facilitates

real-time collaboration among developers working on the

same project. It could include features like live code editing,

shared debugging sessions, and synchronized project

management tools.

•Cloud-Based IDE: Build an IDE that operates entirely in the

cloud, allowing developers to work on their projects from

any device with an internet connection. It could include

features like cloud storage, automatic project backups, and

seamless integration with version control systems.

•Voice-Enabled IDE: Create an IDE that supports voice

commands and natural language processing, allowing

developers to interact with the environment using voice input.

This could streamline certain development tasks and enhance

accessibility for individuals with disabilities.

•Cross-Language IDE: Build an IDE that supports multiple

programming languages seamlessly. Developers would be

able to work on projects involving different languages

without the need for separate environments or switching

between different tools.

3.2

Motivation of the Project

IDEs have yet to be fully supported by easy-to-use speech-

to-text software

Coding remains inaccessible to those who struggle with or

cannot use standard keyboards.

Current coding practice implicitly requires the use of a

keyboard. This requirement prevents a person who cannot

type on a keyboard

3.3 Literature Survey

1.IDE Interaction Support With Command Recommender

Systems (Journal IEEE Access)

Date Of Publication: Year 2020 Methods Used: Real time

database Machine learning Artificial intelligence

Visualization Auto completion Native work Hands free

coding Acceptable command recommendation

2.VocalIDE : An IDE for Programming via Speech

Recognition(ASSETS'17, Oct. 29–Nov. 1 2017, Baltimore,

MD, USA) Context Color Editing, or CCE CCE will be

expanded into more visual feedback vocal navigation

selection tools

3.CodeHelper: A Web-Based Lightweight IDE for E-

Mentoring in Online Programming Courses (University of

Glasgow Downloaded on August 17,2021 at 09:25:21 UTC

from IEEE Xplore) A messenger module is developed for

real-time code update and notification of code marking

4.Recommendation System Development Based on

Intelligent Search, NLP and Machine Learning Method Illia

Balush, Victoria Vysotska and Solomiia Albota Lviv

Polytechnic National University, S. Bandera street, 12, Lviv,

79013, Ukrain

5.Aroma: Code Recommendation via Structural Code Search

arXiv:1812.01158v4 [cs.SE] 17 Oct 2019 proc. ACM

Program. Lang., Vol. 3, No. OOPSLA, Article 152.

Publication date: October 2019

6.Introduction to Various Algorithms of Speech Recognition:

Hidden Markov Model, Dynamic Time Warping and

Artificial Neural Networks 2014 IJEDR | Volume 2, Issue 4 |

ISSN: 2321-9939

CHAPTER 4

PROBLEM DEFINITION AND

SCOPE

4.1

Problem Statement

IDE’s having bad user experience affects the learning curve

and hence the productivity of the user

Using an IDE is not intuitive for new users, many ides have a

higher learning curve

Online dependency installation requires high disk space

Low end computer systems are unable to program

productively



IDE is not barrier free

4.1.1

Goals and objectives

To build a development environments that can reduce the

complexity tasks in an IDE with simple recommendations.

To provide a platform for developers to code hands free.

To allow multiple developers to collaborate at the same

time and helping out and correcting codes.

To display errors and provide their solutions then and there

itself.

To visualize the output in real time

To allow user to save their work virtually using cloud

without having to actually spare their device storage.

4.1.2 Statement of scope

Web 2.0 Internet is an emerging trade and is widely used

over world, so we think that roles of developers are

heightened due to massive need of software and

applications.

Real time coding can be used to visualize the output and can

be used faster than usual methods which can be proven

time saving.

Hands free coding can help to various personals that cannot

access keyboard due to various physical issues.

We are providing a platform for learning as well as coding

and visualizing your code in simple manner without

performing hectic tasks.

The existing IDEs are very complex and works only on

console that are hard for initial developers to understand.

We provide a platform where you can code and learn

without have to worry about the interfaces of the system.

4.2

Major Constraints

This project will be developed using web technologies and

various Artificial intelligence algorithms

1. Speech API will be used for speech recognition

2. Technologies that will be used : React JS, Vanilla JS, Html,

CSS, JSON, XML, NoSQL, NLP

3. Algorithms that will be used :

4. Searching – for implementing search function throughout the

code

5. Map Reduce – AI related function works for coding block

6. Sorting – For the auto completion and formatting.

Optimizing imports

7. Recursive Algorithm – For looping the formatting work.

8. Divide and Conquer- Functions

9. Backtracking- Folding codes and comments, functions,

classes

4.3

Methodologies of Problem solving and

efficiency issues

• This project will be developed using web technologies and

various Artificial intelligence algorithms

• Speech API will be used for speech recognition

• Technologies that will be used : React JS, Vanilla JS, Html,

CSS, JSON, XML, NoSQL, NPL

• Algorithms that will be used :

• Searching - for implementing search function throughout the

code

• Map Reduce – AI related function works for coding block

• Sorting – For the auto completion and formatting. Optimizing

imports

• Recursive Algorithm – For looping the formatting work.

• Divide and Conquer- Functions

• Dynamic Programming –

• Backtracking- Folding codes and comments

4.4

Outcome

For speech recognition

Participants were asked to direct a “human computer” (a

researcher) to correct code that was provided to them. The

participant's screen mirrored the display of the “human

computer's” screen, who operated the text editor according to

a very literal interpretation of participant's speech. During

each of six levels, the participant was presented two blocks

of code. The right of their screen displayed the correct and

complete code, and the left displayed a similar code snippet

that contained some errors. Participants were instructed to

speak to the “human computer” to transform the incorrect

code into the correct code. Errors in the code were listed to

control for variation in debugging time and strategy

For command recommender

To evaluate the quality of IDE, assess its effect on user

behavior, and study user interaction with the smart-IDE, we

invited the students of the Introduction to Programming and

Advanced Programming courses to participate to our study.

The majority of the study participants installed the

monitoring tools, listed below, when the Advanced

Programming course began. Each user received

recommendations from each algorithm for two consecutive

weeks. The sequence of the algorithms was random, on the

individual level, but we assured that there were only small

differences between the overall numbers of participants who

received recommendations in a certain chronological order.

Users had no information on how recommendations were

generated

4.5

Applications

 Code collaboration platform

 Cloud-based IDE

 Voice-enabled IDE

4.6

Hardware Resources Required

1) Processor - Intel i5/i7

2) RAM - 4 GB(min)

3) Hard Disk - 40 GB

4) Microphone

4.7

Software Resources Required

Operating System - Windows 7/8/10

Front End – React

Back End SQLIT

CHAPTER 5

PROJECT PLAN

5.1

Project Estimates

Project Estimates for Real-time Web IDE with Code

Recommendation and Speech Recognition:

1. Resource Allocation:

a. Development Team: A team of experienced software

engineers, including frontend and backend developers,

machine learning engineers, and UX/UI designers.

b. Project Manager: To oversee the project, manage

resources, and ensure timely delivery.

c. QA/Testers: Testers dedicated to quality assurance and

testing activities.

2. Time Estimates:

a. Planning and Requirements Gathering: 2-4 weeks

b. Design and Architecture: 3-5 weeks

c. Backend Development: 8-12 weeks

d. Frontend Development: 8-12 weeks

e. Code Recommendation and Speech Recognition Integration: 4-

6 weeks

f. Real-time Collaboration: 4-6 weeks

g. Testing and Quality Assurance: Ongoing throughout the

development process

h. Deployment and Release: 2-4 weeks

i. User Training and Support: Ongoing post-deployment

3. Effort Estimates:

a. Development Team: A team of 8-12 members, including

developers, designers, and testers, working full-time for the

duration of the project.

b. Project Manager: Part-time involvement throughout the project

duration.

c. QA/Testers: Part-time involvement throughout the project

duration.

4. Cost Estimates:

a. Development Team Salaries: Based on the team size, experience

level, and prevailing industry rates.

b. Infrastructure and Hosting: Costs associated with servers,

databases, cloud services, and hosting solutions.

c. Third-Party Tools and Libraries: Costs for any required third-

party APIs, code recommendation models, speech

recognition libraries, or development tools.

d. Training and Support: Costs for training materials,

documentation, and ongoing user support.

5. Risk Assessment:

a. Project Risks: Identify potential risks that may impact the

project timeline, such as technical challenges, resource

constraints, and external dependencies.

b. Mitigation Strategies: Develop contingency plans and risk

mitigation strategies to address identified risks and minimize

their impact.

6. Monitoring and Evaluation:

a. Regular Progress Review: Conduct periodic reviews to track

project progress, assess adherence to timelines, and make

necessary adjustments.

b. User Feedback and Testing: Gather feedback from users during

the development process and incorporate it into the iterative

development cycle.

c. Quality Assurance: Conduct rigorous testing to ensure

the functionality, performance, and security of the web IDE.

5.1.1

Reconciled Estimates

The project schedule is the tool that communicates what

work needs to be performed, which resources of the

organization will perform the work and the timeframes in

which that work needs to be performed. The project

schedule should reflect all of the work associated with

delivering the project on time. Without a full and

complete schedule, the project manager will be unable to

communicate the complete effort, in terms of cost and

resources, necessary to deliver the project. Online project

management software allows project managers to track

project schedules, resources, budgets and project related

assets in real time. The project schedule can be viewed

and updated by team members associated with the project,

keeping everyone well informed on the overall project

status.

5.1.2

Project Resources

Project resources [People, Hardware, Software, Tools and

other resources] based on Memory Sharing, IPC, and

Concurrency derived using annexures to be referred.

5.2

Risk Management w.r.t.

NP Hard

anal-ysis

This section discusses Project risks and the approach to

managing them.

5.2.1

Risk Identification

Managers face many difficulties when it comes to identifying

and naming the risks that occur when undertaking projects.

These risks could be resolved through structured or

unstructured brainstorming or strategies. It's important to

understand that risks pertaining to the project can only be

handled by the project manager and other stakeholders of the

project. Risks, such as operational or business risks will be

handled by the relevant teams.

The risks that often impact a project are supplier risk,

resource risk and budget risk. Supplier risk would refer to

risks that can occur in case the supplier is not meeting the

timeline to supply the resources required. Resource risk

occurs when the human resource used in the project is not

enough or not skilled enough.

Budget risk would refer to risks that can occur if the costs are

more than what was budgeted

5.2.2

Risk Analysis

The risks for the Project can be analyzed within the

constraints of time andquality

Risk Description

Probability

Impact

Schedule

Quality

Overall

Speech recognition failure

Low

High

Accuracy low

Low

High

Table 5.1: Risk Table

Probability

Value

Description

High

Probability of occurrence is

> 75%

Medium

Probability of occurrence is

26 − 75%

Low

Probability of occurrence is

< 25%

Table 5.2: Risk Probability definitions [

]

5.2.3

Overview of Risk Mitigation, Monitoring,

Management

Following are the details for each

Impact

Value

Speech recognition failure

Very high

> 10%

Schedule impact or Unacceptable quality

High

−

10%

Schedule impact or Some parts of the

project havelow quality

Medium

< 5%

Schedule impact or Barely noticeable

degradation in quality Low Impact on

schedule or Quality canbe incorporated

Risk ID

Risk Description

Accuracy Low

Category

Requirements

Source

Software Design Specification documentation

review.

Probability

Low

Impact

High

Response

Mitigate

Strategy

Precise algorithm will resolve this issue.

Risk Status

Identified

5.3

Project Schedule

5.3.1

Project task set

1. Requirement Gathering and analysis: All possible

requirements of the system to be developed are captured

in this phase and documented in a requirement

specification doc.

2. System Design: The requirement specifications from

first phase are studied in this phase and system design is

prepared. System Design helps in specifying hardware

and system requirements and also helps in defining

overall system architecture. Figure : Waterfall Model

3. Implementation: With inputs from system design, the

system is first developed in small programs called units,

which are integrated in the next phase. Each unit is

developed and tested for its functionality which is referred

to as Unit Testing.

4. Integration and Testing: All the units developed in the

implementation phase are integrated into a system after

testing of each unit. Post integration the entire system is

tested for any faults and failures.

5. Deployment of system: Once the functional and non-

functional testing is done, the product is deployed in the

customer environment or released into the market.

6. Maintenance: There are some issues which come up in

the client environment. To fix those issues patches are

released. Also to enhance the product some better versions

are released. Maintenance is done to deliver these changes

in the customer environment.

5.3.2

Timeline Chart

5.4

Team Organization

Organizational Structure and Reporting Mechanisms for

Real-time Web IDE with Code Recommendation and Speech

Recognition:

1. Organizational Structure: The organizational structure for

the development and maintenance of the real-time web IDE

with code recommendation and speech recognition may

follow a typical software development team structure. It can

include the following roles:

a. Project Manager: Responsible for overall project planning,

coordination, and ensuring timely delivery of the IDE. The

project manager oversees the development process,

manages resources, and communicates with stakeholders.

b. Development Team: Comprised of software engineers,

developers, and designers who work collaboratively to build

and enhance the IDE. This team is responsible for

implementing the code editing features, integrating the

code recommendation and speech recognition

functionalities, and ensuring the overall functionality and

performance of the IDE.

c. Quality Assurance (QA) Team: Dedicated to testing and

quality assurance activities. This team performs thorough

testing to identify and report any bugs, issues, or usability

concerns. They work closely with the development team to

ensure the IDE meets the required quality standards.

d. User Experience (UX) Team: Focused on designing an

intuitive and user-friendly interface for the IDE. The UX

team conducts user research, creates wireframes and

mockups, and collaborates with the development team to

ensure a seamless user experience.

e. Technical Support: Responsible for providing assistance to

IDE users and addressing their queries or issues. They may

handle user feedback, bug reports, and feature requests,

providing timely and effective support to ensure user

satisfaction.

5.4.1

Management reporting and

communication

Reporting Mechanisms:

To facilitate effective communication and reporting within

the organization, several mechanisms can be established:

a. Regular Team Meetings: Conducted to discuss the

progress, challenges, and upcoming tasks related to the

development of the IDE. These meetings allow team

members to provide updates, share ideas, and address any

issues or roadblocks.

b. Issue Tracking System: Implementing an issue tracking

system such as JIRA or Bugzilla allows team members to

report and track bugs, feature requests, and other project-

related tasks. This system ensures transparency and provides

a centralized platform for managing and prioritizing tasks.

c. Project Management Tools: Utilizing project management

tools like Trello, Asana, or Basecamp enables the team to

track tasks, assign responsibilities, set deadlines, and monitor

progress. These tools provide visibility into project status and

facilitate collaboration.

d. Communication Channels: Establishing communication

channels such as Slack, Microsoft Teams, or other

collaboration platforms promotes real-time communication

among team members. It allows for quick discussions,

sharing of updates, and addressing queries or concerns.

e. User Feedback Channels: Implementing mechanisms to

gather user feedback, such as user surveys, feedback forms

within the IDE, or community forums, allows users to report

issues, suggest improvements, and provide general feedback.

This feedback is then collected and shared with the relevant

teams for analysis and action.

f. Documentation and Knowledge Sharing: Creating

comprehensive documentation, including technical

specifications, user guides, and FAQs, helps ensure that

information is easily accessible to the team. Knowledge

sharing sessions or internal wikis can also be employed to

disseminate relevant information and best practices.

g. Escalation Process: Establishing an escalation process

ensures that critical issues or high-priority tasks are escalated

to the appropriate stakeholders promptly. This process

defines the hierarchy and channels through which urgent

matters are reported and addressed.

By implementing a well-defined organizational structure and

effective reporting mechanisms, the development team can

foster communication, collaboration, and transparency.

These practices contribute to efficient development, timely

issue resolution, and continuous improvement of the real-

time web IDE with code recommendation and speech

recognition.

CHAPTER 6

SOFTWARE REQUIREMENT

SPECIFICATION

6.1

Introduction

Introduction:

In today's fast-paced software development world,

developers often require efficient and versatile tools to

streamline their coding process. A real-time web Integrated

Development Environment (IDE) with code recommendation

and speech recognition capabilities emerges as a powerful

solution to meet these needs. This sophisticated IDE

combines the convenience of a web-based platform with

intelligent code suggestions and the flexibility of voice

commands, enhancing productivity and collaboration among

developers.

The real-time web IDE offers a comprehensive set of

features designed to optimize the coding experience. It

provides a secure user authentication mechanism, allowing

developers to create personalized accounts and securely

access their projects. Within the IDE, developers have access

to a feature-rich code editor, equipped with syntax

highlighting, auto-indentation, and code formatting options.

These functionalities contribute to an enhanced coding

experience, promoting cleaner and more organized code.

One of the standout features of this IDE is its real-time

collaboration capabilities. Multiple developers can work on

the same codebase simultaneously, enabling seamless

teamwork and eliminating version control conflicts. This

collaborative environment fosters efficient code review,

knowledge sharing, and faster project completion.

Moreover, the real-time web IDE incorporates intelligent

code recommendation functionality, utilizing machine

learning algorithms to suggest code completions based on the

current context. This feature assists developers in writing

code more efficiently, reducing errors, and boosting

productivity. Additionally, the IDE integrates speech

recognition technology, enabling developers to dictate code

or utilize voice commands for seamless navigation and

control.

To enhance project management, the IDE incorporates

version control system integration, allowing developers to

commit, push, and pull changes to code repositories directly

from the interface. It also provides project organization

features, enabling developers to create, open, and manage

files and folders effortlessly. The IDE includes robust error

detection and debugging tools, facilitating the identification

and resolution of coding issues during the development

process.

Furthermore, the real-time web IDE supports the build and

deployment of web applications. It provides options for

compiling, bundling, and deploying code to various

environments, simplifying the deployment process and

improving workflow efficiency. Collaboration tools, such as

built-in chat or comments, enable seamless communication

and collaboration among developers working on the same

project.

In conclusion, the real-time web IDE with code

recommendation and speech recognition capabilities

presents an innovative solution for developers

seeking a streamlined and collaborative coding

experience. By leveraging advanced features like real-time

collaboration, code recommendations, and speech

recognition, this IDE empowers developers to write code

more efficiently, reduce errors, and enhance productivity.

With its versatile functionality, the real-time web IDE is

poised to revolutionize the way developers work, enabling

them to build exceptional software with ease and speed.

6.1.1

Purpose and Scope of Document

Purpose:

The purpose of developing a real-time web IDE with code

recommendation and speech recognition capabilities is to

provide developers with a powerful and efficient tool that

enhances their coding experience, promotes collaboration,

and boosts productivity. This IDE aims to address the

challenges faced by developers in today's fast-paced

software development landscape by integrating cutting-

edge features and technologies.

The primary purpose of the real-time web IDE is to offer a

feature-rich code editing environment that allows

developers to write, edit, and manage code seamlessly. By

providing essential functionalities such as syntax

highlighting, auto-indentation, and code formatting, the

IDE aims to optimize code readability and maintainability.

The intuitive user interface and smooth navigation

contribute to a user-friendly experience, enabling

developers to focus on their coding tasks.

The code recommendation feature within the IDE serves

the purpose of assisting developers in writing code more

efficiently. By analyzing the context and utilizing

machine learning algorithms, the IDE suggests relevant

code completions, reducing manual effort and minimizing

errors. This feature not only saves time but also promotes

good coding practices, fostering code consistency and

adherence to best practices.

Another crucial purpose of the real-time web IDE is to

enable seamless collaboration among developers. The

real-time collaboration functionality allows multiple users

to work on the same codebase simultaneously, fostering

teamwork, knowledge sharing, and faster project

completion. Through features like integrated chat or

comments, developers can communicate and discuss code

changes within the IDE, enhancing collaboration and

ensuring that everyone stays on the same page.

The speech recognition capability incorporated into the

IDE serves the purpose of providing an alternative input

method for developers. By enabling voice commands and

code dictation, developers can interact with the IDE in a

hands-free manner. This feature increases accessibility

and allows developers to navigate the code, execute

commands, and make changes using voice instructions,

providing a more intuitive and efficient coding experience.

6.1.2 Overview of responsibilities of Developer

Software Development: Developers are responsible for

writing clean, efficient, and maintainable code. They

implement the features and functionalities of the IDE,

ensuring its proper functioning and adherence to coding

standards.

Frontend Development: Frontend developers focus on

creating the user interface (UI) of the IDE. They work

with HTML, CSS, and JavaScript to design and develop

responsive and visually appealing UI components,

ensuring a seamless user experience.

Backend Development: Backend developers handle the

server-side components of the IDE. They develop and

maintain APIs, manage data storage, integrate external

services, and ensure the smooth functioning of the

backend infrastructure.

Integration of Code Recommendation: Developers

collaborate with machine learning engineers and data

scientists to integrate code recommendation features.

They implement the necessary algorithms and

mechanisms to provide intelligent code suggestions based

on the context and user input.

Integration of Speech Recognition: Developers work

closely with natural language processing (NLP) experts to

integrate speech recognition capabilities into the IDE.

They implement the necessary libraries and technologies

to convert spoken commands or code dictation into

actionable instructions within the IDE.

6.2

Usage Scenario

Coding Workshops and Hackathons:

In a coding workshop or hackathon, participants use the real-

time web IDE to collaboratively work on projects.

Code recommendation helps beginners by suggesting

common code snippets and best practices.

Speech recognition allows participants to quickly navigate

through code files, execute commands, and focus on coding

without the need for manual typing.

Remote Team Collaboration:

Real-time collaboration features enable team members to

work simultaneously, review code changes, and provide

feedback in real-time.

Code recommendation assists team members by suggesting

code snippets, improving coding consistency across the

project.

6.2.1

User profiles

User Profile: Coding Student

Name: Sara Rajale

Age: 19

Location: Mumbai

Occupation: Student

Background: Sara is a student who is highly passionate of

learning coding. She has a strong interest in problem

solving and is always looking for innovative ways to write

day

to day life related code. Sara is new learner and does not

know much about coding, algorithms, and best practices.

6.2.2 Use-cases

All use-cases for the software are presented. Description of

all main Use cases

using use case template is to be

provided.

Table 6.1: Use Cases

Sr.

No.

Use Case

Description

Actors

Assumpt

ions

User logs in using

the authentication

portal

user

successf

ul or

failed

6.3

Data Model and Description

6.3.1

Data Description

Data objects that will be managed/manipulated by the

software are described

in this section. The database entities

or files or data structures required to be described. For

data objects details can be given as below

User: Represents an individual user of the online web IDE.

Contains attributes such as user ID, name, email, and

password.

Project: Represents a project created by a user within the

web IDE. Contains attributes such as project ID, name,

description, and creation date.

File: Represents a file within a project. Contains attributes

such as file ID, name, extension, content, and last modified

date. Each file belongs to a specific project.

SpeechRecording: Represents a recorded speech input by the

user. Contains attributes such as recording ID, text transcript,

duration, and timestamp.

CodeRecommendation: Represents a recommended code

snippet or suggestion based on the user's input. Contains

attributes such as recommendation ID, text, relevance score,

and category.

6.3.2 Data objects and Relationships

User-Project: One user can have multiple projects. This is a

one-to-many relationship. Each project is associated with a

specific user.

Project-File: One project can have multiple files. This is a

one-to-many

relationship. Each file is associated with a specific project.

Project-SpeechRecording: One project can have multiple

speech recordings. This is a one-to-many relationship. Each

speech recording is associated with a specific project.

File-SpeechRecording: One file can have multiple speech

recordings. This is a one-to-many relationship. Each speech

recording is associated with a specific file.

User-CodeRecommendation: One user can have multiple

code recommendations. This is a one-to-many relationship.

Each code recommendation is associated with a specific user.

Project-CodeRecommendation: One project can have

multiple code recommendations. This is a one-to-many

relationship. Each code recommendation is associated with a

specific project.

6 Functional Model and Description

A description of each major software function, along with

data flow (struc- tured analysis) or class hierarchy

(Analysis Class diagram with class descrip-tion for object

oriented system) is presented.

6.3

Data Flow Diagram

6.4

State Diagram:

State Machine Diagram

6.5

Software Interface Description

The real-time web IDE interface is designed to provide

developers with a user-friendly environment for writing,

editing, and executing code. The main interface consists of a

central code editor panel that supports syntax highlighting

and code autocompletion. As developers type, the system

analyzes their code and provides relevant suggestions based

on context, language syntax, and coding patterns. These

suggestions appear as pop-up windows or inline suggestions

near the code being written. The menu bar, located at the top

of the interface, offers options such as File, Edit, View, Run,

Debug, and Tools, enabling users to save, open files, run

code, and configure IDE settings. The toolbar, situated near

the menu bar, provides quick access to frequently used

features like saving code, running code, accessing code

libraries, managing files, and toggling IDE features. Side

panels, which can be expanded or collapsed, include a file

explorer panel, a console or terminal panel for viewing

program output, a debugging panel, and a panel for

managing code snippets or libraries. Collaboration features

allow multiple users to work on the same code

simultaneously, offering shared editing and communication

capabilities through chat or comments. The speech

recognition feature allows developers to interact with the

IDE using voice commands, enabling code dictation,

command execution, and navigation through the interface.

The status bar, located at the bottom of the interface,

provides information on the current state of the IDE, such as

the programming language, cursor position, code

compilation or execution status, and any relevant error

messages or warnings. Users can customize the IDE through

the settings or preferences menu, adjusting options such as

code formatting, syntax highlighting themes, keyboard

shortcuts, and code completion preferences. The interface is

designed with responsive design principles, ensuring

usability on various devices, including desktop computers,

laptops, tablets, and smartphones. Overall, this real-time web

IDE interface combines a powerful code editor with code

recommendation, speech recognition capabilities,

collaboration features, and a user-friendly layout, providing

developers with a productive and efficient environment for

coding tasks.

CHAPTER 7

DETAILED DESIGN DOCUMENT

USING ANNEXURE A AND B

7.1

Introduction

The purpose of this design document is to outline the

architectural and functional design of an online web

Integrated Development Environment (IDE) with speech

recognition and code recommendation features. The system

aims to provide users with an efficient and user-friendly

platform for coding, leveraging speech input for code

creation and offering intelligent code recommendations. This

document follows the guidelines provided in Annex A and

Annex B.

7.2

Architectural Design

7.2.1

Class Diagram

CHAPTER 8

PROJECT IMPLEMENTATION

8.1

Introduction

The implementation of the online web IDE with

speech recognition and code recommendation involves a

series of steps to bring the envisioned system to life. This

section provides an overview of the implementation process

and highlights the key aspects involved in turning the

concept into a functional and user-friendly platform.

The implementation of the web IDE begins with

setting up the development environment, including installing

the necessary tools, frameworks, and libraries. This ensures

that the project has a solid foundation for building and

testing the system.

Next, the user interface (UI) design phase begins,

where the visual elements and interactions of the web IDE

are crafted. The UI design aims to create an intuitive and

user-friendly experience, enabling developers to seamlessly

navigate, create, and edit projects and files. Elements such as

project management, file explorer, file editor, speech input,

and code recommendation panel are designed and

implemented using HTML, CSS, and JavaScript.

The backend functionality is a crucial component of

the implementation process. It involves developing the

server-side logic using a chosen programming language and

framework. User authentication and authorization

mechanisms are implemented to secure user data and ensure

proper access control. APIs and endpoints are created to

handle user management, project management, file

management, speech recognition, and code recommendation

functionalities. Integration with speech recognition engines

or libraries is established to convert recorded speech into text

transcripts. Additionally, code recommendation algorithms

are implemented based on user input and contextual analysis.

The database design plays a vital role in storing and

managing user, project, file, speech recording, and code

recommendation data. A suitable database management

system (DBMS) is chosen, and tables are created to represent

the various data entities. Database operations for creating,

reading, updating, and deleting data are implemented using

appropriate SQL queries or ORM (Object-Relational

Mapping) frameworks.

8.2

Tools and Technologies Used

 Programming Languages:

HTML, CSS, JavaScript: These languages are used for front-

end web development, creating the user interface, and

handling user interactions.

Python, Node.js, Java, or any other preferred language:

These languages are used for back-end development,

implementing server-side logic, APIs, and database

operations.

Web Frameworks:

Front-end Frameworks: Popular frameworks like React,

Angular, or Vue.js can be used to build dynamic and

interactive user interfaces.

Back-end Frameworks: Frameworks like Django (Python),

Express.js (Node.js), Spring Boot (Java), or Laravel (PHP)

can be used to handle routing, server logic, and API

development.

 Speech Recognition:

Google Cloud Speech-to-Text API: A cloud-based service

for converting speech into text. It provides accurate and real-

time speech recognition capabilities.

Mozilla DeepSpeech: An open-source automatic speech

recognition (ASR) engine that can be trained on user-specific

data to provide speech recognition functionality.

 Code Recommendation:

Machine Learning Libraries: Libraries like TensorFlow,

PyTorch, or scikit-learn can be used for developing machine

learning models to provide code recommendation based on

user input and context.

Rule-Based Techniques: Custom rule-based algorithms can

be implemented to offer code recommendations using pattern

matching, code analysis, and other programming language-

specific techniques.

 Database Management System (DBMS):

MySQL, PostgreSQL, SQLite, or any other preferred DBMS:

These systems are used to store and manage user data,

project details, files, speech recordings, and code

recommendations.

8.3

Methodologies/Algorithm Details

 Agile Software Development Methodology:

Agile methodology can be employed to facilitate iterative

and incremental development, allowing for flexibility and

adaptability throughout the implementation process.

The development process is divided into sprints, each

focusing on delivering a working increment of the web IDE.

Regular meetings, such as daily stand-ups and sprint

planning sessions, enable collaboration, communication, and

adaptation to changing requirements.

Agile methodologies like Scrum or Kanban can be utilized to

manage the development process effectively.

 Speech Recognition Algorithms:

Automatic Speech Recognition (ASR) algorithms are used to

convert speech input into text.

Popular ASR algorithms include Hidden Markov Models

(HMMs), Deep Neural Networks (DNNs), and Recurrent

Neural Networks (RNNs).

These algorithms leverage large datasets to train models that

can accurately transcribe speech into text.

Speech recognition engines like Google Cloud Speech-to-

Text or open-source solutions like Mozilla DeepSpeech can

be utilized, which implement these algorithms.

 Code Recommendation Algorithms:

Code recommendation algorithms utilize various techniques

to provide relevant suggestions and snippets based on user

input and context.

Machine Learning-based Approaches: Machine learning

models, such as deep learning models or statistical models,

can be trained on code repositories or user-specific data.

Natural Language Processing (NLP) Techniques: NLP

techniques can be employed to analyze code syntax,

semantics, and patterns to provide accurate code

recommendations.

8.3.1

Algorithm /Pseudo Code

function generate_text(input_text, num_tokens):

tokens = tokenize(input_text)

numerical_tokens =

convert_tokens_to_numerical(tokens)

for i in range(num_tokens):

next_token =

predict_next_token(numerical_tokens)

numerical_tokens.append(next_token)

generated_text =

decode_numerical_tokens(numerical_tokens)

return generated_text

function predict_next_token(numerical_tokens):

input_sequence =

select_input_sequence(numerical_tokens)

next_token_logits = gpt_model(input_sequence)

next_token =

sample_token_from_logits(next_token_logit

return next_token

function

select_input_sequence(numerical_tokens):

max_seq_length = gpt_model.max_input_length

if length(numerical_tokens) > max_seq_length:

start_index = length(numerical_tokens) -

max_seq_length

input_sequence =

numerical_tokens[start_index:]

else:

input_sequence = numerical_tokens

return input_sequence

function sample_token_from_logits(token_logits):

token_probs = softmax(token_logits)

next_token =

sample_from_distribution(token_probs)

return next_token

function

sample_from_distribution(probabilities):

cumulative_probs =

calculate_cumulative_probs(probabilities)

random_value =

generate_random_number_between(0, 1):

sampled_token =

find_token_with_cumulative_prob(cumulativ

e_probs, random_value)

return sampled_token

function

decode_numerical_tokens(numerical_tokens):

tokens =

convert_numerical_tokens_to_tokens(numeri

cal_tokens)

text = detokenize(tokens)

return text

function tokenize(text):

// Tokenization algorithm

function convert_tokens_to_numerical(tokens):

// Convert tokens to numerical

representations

function softmax(logits):

// Apply softmax function

function

calculate_cumulative_probs(probabilities):

// Calculate cumulative probabilities

function

find_token_with_cumulative_prob(cumulativ

e_probs, random_value):

// Find token based on cumulative

probabilities and random value

function

convert_numerical_tokens_to_tokens(numeri

cal_tokens):

// Convert numerical tokens back to original

tokens

function detokenize(tokens):

// Join tokens back into text

CHAPTER 9

SOFTWARE TESTING

9.1

Type of Testing Used

User Interface (UI) Testing:

UI testing is conducted to ensure that the user interface

elements of the web IDE are displayed correctly, and user

interactions work as expected.

It includes validating the layout, responsiveness, and

usability of the web IDE across different browsers and

devices.

Tools such as Selenium, Cypress, or Puppeteer can be used

for automated UI testing.

Functional Testing:

Functional testing verifies that the web IDE meets the

specified functional requirements.

It involves testing various features and functionalities such as

project creation, file management, speech recognition, and

code recommendation.

Test cases are designed to cover different scenarios and edge

cases to ensure the correct functioning of the system.

Performance Testing:

Performance testing measures the responsiveness, scalability,

and resource usage of the web IDE under different workloads.

It checks the system's ability to handle multiple concurrent

users, large code files, and heavy speech recognition and

recommendation computations.

Performance testing tools like JMeter, LoadRunner, or

Gatling can be used to simulate realistic workloads and

measure the system's performance metrics.

Security Testing:

Security testing aims to identify and address vulnerabilities

in the web IDE to ensure the protection of user data and

system integrity.

It includes testing for potential security risks such as SQL

injection, cross-site scripting (XSS), cross-site request

forgery (CSRF), and authentication vulnerabilities.

Tools like OWASP ZAP or Nessus can be used to perform

security scans and identify vulnerabilities.

Usability Testing:

Usability testing focuses on evaluating the user experience of

the web IDE.

It involves gathering feedback from actual users and

conducting usability tests to identify any usability issues or

areas of improvement.

9.2

Test Cases and Test Results

- Test case 1: Verify that code recommendations are

displayed as the user types

- Test case 2: Test the accuracy of code

recommendations by checking if they are relevant to the

current context.

- Test case 3: Test code recommendation performance

with a large codebase or complex programming language

constructs.

- Test case 4: Validate that the IDE accurately converts

spoken commands into text.

- Test case 5: Verify that speech recognition works with

different accents and speech patterns.

- Test case 6: Test speech recognition in noisy

environments to ensure it can filter out background noise.

- Test case 7: Verify that the code recommendation

feature works seamlessly with the speech recognition

feature.

- Test case 8: Test the IDE's ability to generate code

based on spoken instructions.

- Test case 9: Validate that the IDE can understand and

execute spoken commands related to code execution and

debugging.

- Test case 10: Verify that the code editor panel provides

a smooth and responsive typing experience.

- Test case 11: Test the visibility and readability of code

recommendations and speech recognition feedback in the

user interface.

- Test case 12: Validate that the user interface elements

for code recommendation and speech recognition are

intuitive and easy to use.

- Test case 13: Test the web IDE's compatibility with

different web browsers and versions.

- Test case 14: Validate that the code recommendation

and speech recognition features work on different operating

systems.

- Test case 15: Test the web IDE's performance on

various devices, such as desktop computers, laptops, tablets,

and smartphones.

- Test case 16: Test the behavior of code

recommendation and speech recognition when

encountering code syntax errors or incomplete statements.

- Test case 17: Validate the IDE's response when there

are no code recommendations available for a specific

context.

- Test case 18: Test the handling of unusual or

unexpected input during speech recognition.

CHAPTER 10

RESULTS

10.1

Screen shots

10.2

Outputs

CHAPTER 11

DEPLOYMENT AND

MAINTENANCE

11.1

Installation and un-installation

As this is a realtime web IDE, but we have also made it’s

functionalities downloadable as android APK, console app,

IOS app, macOS app as well as linux package

11.1.1 Steps for installation :

 Open smart-ide.up.railway.app

 Click on the install icon

Android – (click on hamburger icon and then click on

install)

o Windows – (Find install button in the searchbar right corner

and click on the install button)

macOS – (Find install button in the searchbar right corner

and click on the install button)

o IOS – (click on hamburger icon and then click on install)

Linux – (Find install button in the searchbar right corner

and click on the install button)

11.1.2 Steps for un-installation :

 Same uninstallation for every platform (use default un-

installation)

CHAPTER 12

CONCLUSION AND FUTURE

SCOPE

The Smart IDE with code recommendation and voice

recognition provides an approach towards a better hands free

experience while coding, It can give solutions to various real

world problems and its collaborative features can be proven

effective for virtual classrooms or virtual coding tests where

minimal code recommendation is required

It has a low learning curve which enables the users to interact

with the system and hence providing a better user experience

Smart IDE attempts to solve real world problems in a given

scope to justify the users needs and provide an optimal

solution based on the particular problem

Web 2.0 : Internet is an emerging trade and is widely used

over world, so we think that roles of developers are

heightened due to massive need of software and applications.

▪ Real time coding can be used to visualize the output and

can be used faster than usual methods which can be proven

time saving.

▪ Hands free coding can help to various personals that

cannot access keyboard due to various physical issues.

▪ We are providing a platform for learning as well as coding

and visualizing your code in simple manner without

performing hectic tasks.

▪ The existing IDEs are very complex and works only on

console that are hard for initial developers to understand. We

provide a platform where you can code and learn without

have to worry about the interfaces of the system.

REFERENCES

1. https://ieeexplore.ieee.org/document/8963716

2. https://ieeexplore.ieee.org/document/9486772

3. P. Bourque, and R. E. Fairley, Guide to the Software

Engineering Body of Knowledge. Washington, DC, USA:

IEEE Computer Society Press, 2014.

4. A. Ahmed, Software Project Management: A Process-

Driven Approach. Bengaluru, India: Taylor & Francis, 2011.

5. L. C. L. Kats, R. G. Vogelij, K. T. Kalleberg, and E.

Visser, “Software development environments on the web", in

Proceedings of the ACM international symposium on New

ideas, new paradigms, and reflections on programming and

software - Onward! ’12, (2012), pp. 99

6. M. Goldman, “Role-based interfaces for collaborative

software development", in Proceedings of the 24th Annual

ACM Symposium Adjunct on User Interface Software and

Technology - UIST ’11 Adjunct, (2011), pp. 23.

7. F. Frößler, "A Practice Theoretical Analysis of Real Time

Collaboration Technology: Skype and Sametime in Software

Development Projects", Göttingen: Cuvillier, (2008).

8. S. Klein, N. Vehring, and M. Kramer, “Introducing Real

Time Communication: Frames, Modes & Rules", in

Proceedings 23nd Bled eConference eTrust: Implications for

the Individual, (2010), pp. 591–606-

ANNEXURE A

LABORATORY ASSIGNMENTS ON

PROJECT ANALYSIS OF

ALGORITHMIC DESIGN

Assignment 1: Requirements Gathering and Analysis

Objective: Understand the project requirements and analyze

the algorithmic design for a real-time web IDE.

Tasks:

1. Research and gather information about existing web IDEs

and their functionalities.

2. Identify the key requirements for a real-time web IDE,

such as code editing, collaboration, version control, and live

preview.

3. Analyze the algorithmic design needed to implement real-

time collaboration, synchronization, and version control

features.

4. Create a document outlining the project requirements

and the algorithmic design considerations.

Assignment 2: User Interface Design

Objective: Design the user interface for the real-time web

IDE.

Tasks:

1. Research modern web design trends and best practices

for creating an intuitive and user-friendly interface.

2. Create wireframes and mockups for the various

components of the web IDE, including code editor,

collaboration features, and live preview.

3. Apply the principles of responsive design to ensure the

IDE works well across different devices and screen sizes.

4. Document the user interface design decisions and present

them along with the wireframes and mockups.

Assignment 3: Algorithmic Design for Real-time

Collaboration

Objective: Design the algorithms and data structures

required for real-time collaboration in the web IDE.

Tasks:

1. Identify the challenges of real-time collaboration, such as

conflict resolution, concurrency control, and

synchronization.

2. Design algorithms for handling concurrent edits and

resolving conflicts in real-time.

3. Choose appropriate data structures and data

synchronization techniques for efficient collaboration.

4. Implement a proof-of-concept prototype to demonstrate

real-time collaboration using the designed algorithms.

Assignment 4: Version Control and Code Repository

Objective: Design the version control system and code

repository for the web IDE.

Tasks:

1. Research different version control systems and their

features (e.g., Git, Mercurial, SVN).

2. Design a version control system tailored for the web IDE,

considering features like branching, merging, and conflict

resolution.

3. Implement the code repository using the chosen version

control system.

4. Integrate the version control system into the web IDE,

allowing users to manage and collaborate on code using

version control features.

Assignment 5: Performance Optimization

Objective: Analyze and optimize the performance of the

web IDE.

Tasks:

1. Identify potential performance bottlenecks in the web IDE,

such as slow code compilation, high memory usage, or slow

response times.

2. Analyze the algorithms and data structures used in the

IDE and identify areas for improvement.

3. Optimize the identified algorithms and data structures to

improve performance.

4. Measure and benchmark the performance of the

optimized web IDE and document the improvements

achieved.

Assignment 6: Security and Error Handling

Objective: Design and implement security measures and

error handling mechanisms for the web IDE.

Tasks:

1. Identify potential security vulnerabilities in the web IDE,

such as code injection, cross-site scripting, or unauthorized

access.

2. Design and implement security measures, such as input

validation, authentication, and access control.

3. Implement error handling mechanisms to provide

meaningful error messages and handle unexpected

exceptions.

4. Conduct penetration testing and code reviews to ensure

the security and robustness of the web IDE.

ANNEXURE B

REVIEWERS COMMENTS OF

PAPER SUBMITTED

Paper Title: Realtime web IDE with speech

recognition and code recommendation

Name of the Conference/Journal where paper

submitted : 2nd National Conference Recent Trends in

Engineering and Technology (PRECCON2023), IEEE BS

Technovation

Paper accepted/rejected : Accepted

Review comments by reviewer : - No

Corrective actions if - No

ANNEXURE C

PLAGIARISM REPORT

ANNEXURE D

INFORMATION OF PROJECT

GROUP MEMBERS

Name : Shirsath Shivanand S

Date of Birth : 25/11/2001

Gender : Male

Permanent Address : Kolhar kh, Rahuri, Maharashtra

E-Mail : Shiva.S.Shirsath@gmail.com

Mobile/Contact No. : 9130057189

Placement Details : Not yet

Paper Published :

Yes

Name : Rokade Deeksha D

Date of Birth : 24/01/2002

Gender : Female

Permanent Address : Bhistabaug road, savedi, Ahmednagar

E-Mail : deeksharokade@gmail.com

Mobile/Contact No. : 9423840134

Placement Details : Not yet

Paper Published : Yes

Name : Kothawade Piyusha P

Date of Birth : 18/9/2001

Gender : Female

Permanent Address : Gujjar galli, Navapur

E-Mail : piyukothawade789@gmail.com

Mobile/Contact No. : 8080118660

Placement Details : Not yet

Paper Published : yes

Name : Tuwar Vinayak B

Date of Birth : 22/5/2000

Gender : Male

Permanent Address : A/P-Pachegaon Tal-Nevasa Dist-Ahmednagar

E-Mail : vinayaktuwarvt@gmail.com

Mobile/Contact No. : 7447815210

Placement Details : Not Yet

Paper Published : Yes