A SYNOPSIS ON

TEST CASES GENERATOR

Submitted in partial fulfilment of the requirement for the award

of the degree of

BACHELOR OF TECHNOLOGY
IN
COMPUTER SCIENCE & TECHNOLOGY (SPECIALIZATION)
Submitted by:
Aksh Chaudhary 2019639
Abhishek Khaiwal 2019633
Adhyan Kumar 2019901
Mukul Verma 2019684

Under the Guidance of

Dr. Prakash Shrivastava
Associate Professor

Project Team ID: MP24CST015

Department of Computer Science and

Engineering
Graphic Era (Deemed to be University)
Dehradun, Uttarakhand
October-2024
 CANDIDATE’S DECLARATION
I/We hereby certify that the work which is being presented in the Synopsis entitled “Test
Cases Generator” in partial fulfillment of the requirements for the award of the Degree of
Bachelor of Technology in Computer Science and Technology (Specialization) in the
Department of Computer Science and Engineering of the Graphic Era (Deemed to be
University), Dehradun shall be carried out by the undersigned under the supervision of Dr.
Prakash Shrivastava, Associate Professor, Department of Computer Science and
Engineering, Graphic Era (Deemed to be University), Dehradun.

Aksh Chaudhary 2019639

Abhishek Khaiwal 2019633
Adhyan Kumar 2019901
Mukul Verma 2019684

The above mentioned students shall be working under the supervision of the undersigned on
the “Test Case Generator”

Signature Signature
Supervisor Head of the Department

Internal Evaluation (By DPRC Committee)

Status of the Synopsis: Accepted / Rejected

Any Comments:

Name of the Committee Members: Signature with Date

1.
2.
 Table of Contents

Chapter No. Description Page No.

Chapter 1 Introduction and Problem Statement 1-2
Chapter 2 Background/ Literature Survey 3-4
Chapter 3 Objectives 5
Chapter 4 Hardware and Software Requirements 6-7
Chapter 5 Possible Approach/ Algorithms 8-11
References 12
Chapter 1

Introduction and Problem Statement

In the following sections, a brief introduction and the problem statement for the work has
been included.

1.1 Introduction

The Test Cases Generator project aims to streamline and automate the process of creating
comprehensive test cases for software applications. By leveraging advanced algorithms and
techniques, this tool will significantly enhance the efficiency and effectiveness of testing
activities, ultimately leading to improved software quality.

The manual process of creating test cases is time-consuming and error-prone. It often
involves inefficient resource allocation, human error, and limited scalability. The Test Cases
Generator project aims to address these challenges by automating test case creation,
improving test case quality, enhancing testing efficiency, and increasing test coverage.

The tool will be designed to support various types of software applications and testing
methodologies. It will benefit software testers, quality assurance engineers, developers, and
project managers by reducing the workload, improving the quality of work, enhancing the
QA process, identifying and fixing defects early, and streamlining the testing process.

The project will follow a systematic approach that includes requirements gathering, design
and development, testing and validation, and deployment and maintenance. By following this
approach, the Test Cases Generator project will deliver a valuable tool that can significantly
improve the efficiency and effectiveness of software testing activities.

A robust test case management system is essential for effective testing. The Test Cases
Generator can be integrated with a test case management tool to provide features like test
case organization, tracking, and reporting. This integration can help teams manage test cases
efficiently, track progress, and generate comprehensive test reports.

Leveraging artificial intelligence (AI) can further enhance the capabilities of the Test Cases
Generator. AI algorithms can be used to analyze code, requirements, and existing test cases to
generate more intelligent and comprehensive test cases. AI-powered features can also help
identify potential defects and vulnerabilities that may be missed by traditional testing
methods.

By expanding the scope of the Test Cases Generator project to include these additional
features, we can create a more comprehensive and powerful tool that can significantly
improve the efficiency, effectiveness, and quality of software testing

1.2 Problem Statement

The process of creating test cases for software applications is a critical but often time-
consuming and error-prone task. Manual test case creation can be inefficient, leading to
missed defects and delays in project timelines. To address these challenges, automated test
case generation tools have emerged as valuable solutions. A Test Cases Generator is a
software tool that can automatically create test cases based on predefined specifications and
requirements.

The primary problem addressed by a Test Cases Generator is the manual test case creation
process, which is often inefficient, error-prone, and time-consuming. Manual test case
creation can lead to the following issues:

Inefficient resource allocation: A significant portion of testing resources is dedicated to the

creation and maintenance of test cases, diverting attention from other critical testing
activities.

Human error: Manual test case creation can introduce errors and inconsistencies, leading to
missed defects and reduced test coverage.

Limited scalability: As projects grow in complexity and size, manually creating and
managing test cases becomes increasingly challenging and time-consuming.

Lack of standardization: Manual test cases can vary in quality, consistency, and
completeness, making it difficult to ensure comprehensive coverage and maintainability.

These challenges can ultimately lead to lower software quality, increased development costs,
and delayed project timelines. A Test Cases Generator aims to address these issues by
automating the test case creation process, improving test case quality, and enhancing overall
testing efficiency
Chapter 2

Background/ Literature Survey

In the present times, research work is going on in context of ……In this chapter some of the
major existing work in these areas has been reviewed.

Background

The concept of automated test case generation has been explored in academic research and
industry for several decades. Early research focused on developing algorithms and techniques
for generating test cases based on formal specifications and models. However, these early
approaches often faced limitations in terms of scalability and applicability to real-world
software systems.

In recent years, advancements in artificial intelligence, machine learning, and natural

language processing have enabled the development of more sophisticated and effective test
case generation tools. These tools leverage techniques such as genetic algorithms, neural
networks, and natural language understanding to generate test cases that are more
comprehensive, diverse, and relevant to the specific requirements of the software application.

Literature Review

A comprehensive literature survey reveals a growing body of research and development

efforts in the field of test case generation. Some key areas of focus include:

Model-based test case generation: This approach involves creating a formal model of the
software system and using model-based techniques to generate test cases. Research in this
area has explored various modeling languages and techniques, such as UML, Petri nets, and
statecharts.
Genetic algorithms: Genetic algorithms have been successfully applied to test case
generation, using evolutionary principles to search for optimal test cases.

Neural networks: Deep learning techniques have been used to train neural networks to
generate test cases based on historical data and patterns.

Natural language processing: Natural language processing techniques can be used to extract
requirements from natural language specifications and generate test cases based on those
requirements.

Hybrid approaches: Many researchers have explored hybrid approaches that combine
multiple techniques to generate test cases, such as combining model-based techniques with
genetic algorithms or neural networks.

Key Research Contributions

Several significant research contributions have been made in the field of test case generation,
including:

The development of formal methods for test case generation: Researchers have proposed
various formal methods, such as model checking and symbolic execution, for generating test
cases based on formal specifications.

The application of machine learning techniques: Machine learning algorithms have been used
to learn patterns from existing test cases and generate new test cases.

The integration of test case generation with software development processes: Researchers
have explored how test case generation tools can be integrated with software development
methodologies, such as Agile and DevOps.

The evaluation of test case generation tools: Researchers have conducted empirical studies to
evaluate the effectiveness and efficiency of different test case generation tools.
Chapter 3

Objectives
The objectives of the proposed work are as follows:

1. Automated Test Case Creation: The Test Cases Generator project aims to automate the
process of creating comprehensive test cases for software applications. By leveraging
advanced algorithms and techniques, the tool will significantly reduce the time and effort
required for manual test case creation, allowing testing teams to focus on higher-value
activities.

2. Improved Test Case Quality:The tool will ensure that generated test cases are accurate,
consistent, and relevant to the application under test. By minimizing human error and
inconsistencies, the Test Cases Generator will contribute to improved test case quality and
overall software quality.

3. Enhanced Testing Efficiency: The project will streamline the testing process by
automating repetitive and time-consuming tasks, such as test case creation and execution.
This will enable testing teams to execute more test cases in a shorter amount of time,
increasing testing efficiency and reducing the risk of defects.

4. Increased Test Coverage: The Test Cases Generator will help identify a wider range of
test scenarios, improving test coverage and reducing the likelihood of missed defects. By
ensuring that the application is thoroughly tested under various conditions, the tool will
contribute to higher software reliability.
Chapter 5

Hardware and Software Requirements

Hardware Requirements for a Test Case Generator Model

The hardware requirements for a test case generator model will depend on the complexity of
the model, the size of the datasets, and the desired performance. However, here are some
general guidelines:

Processor:
CPU: A multi-core processor with high clock speed is recommended for handling complex
models and large datasets.
GPU: If the model is computationally intensive, a GPU can significantly accelerate training
and inference.
Memory:
RAM: Sufficient RAM is needed to store the model, datasets, and intermediate results. The
amount of RAM required will depend on the size of the model and datasets.
Storage:
Hard Drive/SSD: A fast storage device is necessary to store the model, datasets, and
generated test cases. An SSD can provide significantly better performance compared to a
traditional hard drive.
Network Connectivity:
Internet: If the model relies on external data or cloud-based services, a stable internet
connection is required.

Software Requirements for a Test Case Generator Model

The software requirements will depend on the specific model and programming language
used. However, here are some common components:
Programming Language and Libraries:
Python: Python is a popular choice for machine learning and data science tasks, with
libraries like TensorFlow, PyTorch, and Keras.
R: R is another popular language for statistical computing and data analysis.
Java: Java is often used for enterprise applications and can be used for developing test case
generation tools.
Machine Learning Framework:
TensorFlow: A popular open-source platform for machine learning, including deep learning.
PyTorch: Another popular open-source platform for machine learning, especially for
dynamic neural networks.
Keras: A high-level API that can run on top of TensorFlow or Theano.
Data Preprocessing Tools:
Pandas: A Python library for data manipulation and analysis.
NumPy: A Python library for numerical computing.
Scikit-learn: A Python library for machine learning tasks, including preprocessing.
Version Control System:
Git: A popular version control system for tracking changes to code and data.
Cloud Platform (Optional):
AWS, GCP, Azure: Cloud platforms can provide scalable computing resources and storage.
Chapter 4

Possible Approach/ Algorithms

Step 1: Import Required Libraries and Configure Environment

Import dependencies:
streamlit, PIL.Image, io, google.generativeai, os, and dotenv.

Load environment variables using load_dotenv().

Configure Google Gemini model:

Get the API key from environment variables.

Instantiate the model using the appropriate model name.

Step 2: Create Directory to Store Uploaded Files

Define the target directory:

target_directory = "uploaded_files".

Check if the directory exists:

If not, create the directory using os.makedirs().

Step 3: Function to Save Uploaded Documents (save_docs)

Input: List of uploaded documents (docs).

Initialize an empty list for storing file paths.

For each document in docs:

Generate the full file path using os.path.join().

Append the file path to the list of file paths.

Display a success message in Streamlit.

Open the uploaded image using Image.open(io.BytesIO(doc.getbuffer())).

Save the image to the specified directory using image.save().

Return the list of saved file paths.

Step 4: Function to Upload Files to Google Gemini (upload_to_gemini)

Input: List of saved file paths.

Initialize an empty list for storing uploaded image references.

For each file in file_paths:

Upload the file to Google Gemini using genai.upload_file().

Display a success message for each uploaded file.

Store the uploaded file reference (URI) in the images list.

Return the list of uploaded images.

Step 5: Function to Generate Test Cases (describe)

Input: Query string (query) and list of uploaded images.

For each image in the images list:

Generate a detailed prompt based on the user query and image content.

Use the Gemini model to generate content by passing the prompt and the image
reference.

Display the generated test cases in Markdown format on the Streamlit page.

Step 6: Main Function (main)

Set the Streamlit page configuration.

Display the header: "Test Cases Generator".

Provide an input field for user queries (optional context).

Sidebar for document upload:

Display a file uploader using st.file_uploader() with multi-upload enabled.

Call save_docs() to save the uploaded files and return the file paths.

Call upload_to_gemini() to upload the files and return the image references.

Generate Test Cases Button:

 When the button is clicked, call the describe() function with the user query and
uploaded images.

Step 7: Program Execution

Check if the script is run as the main program.

Call the main() function to start the Streamlit app.

FLOWCHART
References
1. Model-Based Testing with TLA+
Authors: Leslie Lamport
Book: Specifying Systems: Compressing Ideas and Enhancing Clarity (Chapter 13)
Publisher: Addison-Wesley Professional
Year: 2014

2. A Test Case Generation Process and Technique

Authors: Nicha Kosindrdecha and Jirapun Daengdej
Journal: Science Alert
Volume: 4
Pages: 265-287
Year: 2010 (https://www.geeksforgeeks.org/software-testing-boundary-value-analysis/)

3. Automatic Test Case Generation for Programs that Are Coded Against Interfaces and Annotations
Authors: Islam, N., Bultan, T., & Reps, T.
Conference Proceedings: Proceedings of the 2014 International Symposium on Software Testing and Analysis
(ISSTA 2014)
Pages: 321-332
Year: 2014 (https://websites.uta.edu/oit/2024/05/31/microsoft-and-uta-celebrate-successful-generative-ai-
prompt-a-thon/)

4. Test Case Generation - ProB Documentation

Website: University of Düsseldorf, HHU (https://rtg.cis.upenn.edu/testgen/)
Accessed: [Access date today] (year необязательно [necessarily] included for websites)

5. Model-Driven Testing and Model-Based Testing

Authors: Amrita Maharia and Rajiv Gupta
Book Chapter: Software Testing (Chapter 14)
Publisher: Dreamtech Press
Year: 2010