Dataset

This dataset was created by g-dragon

Released under CC0: Public Domain

Contents

Albert-wang/create-test dataset hosted on Hugging Face and contributed by the HF Datasets community

Context

There are lots of datasets available for different machine learning tasks like NLP, Computer vision etc. However I couldn't find any dataset which catered to the domain of software testing. This is one area which has lots of potential for application of Machine Learning techniques specially deep-learning.

This was the reason I wanted such a dataset to exist. So, I made one.

Content

New version [28th Nov'20]- Uploaded testing related questions and related details from stack-overflow. These are query results which were collected from stack-overflow by using stack-overflow's query viewer. The result set of this query contained posts which had the words "testing web pages".

New version[27th Nov'20] - Created a csv file containing pairs of test case titles and test case description.

This dataset is very tiny (approximately 200 rows of data). I have collected sample test cases from around the web and created a text file which contains all the test cases that I have collected. This text file has sections and under each section there are numbered rows of test cases.

Acknowledgements

I would like to thank websites like guru99.com, softwaretestinghelp.com and many other such websites which host great many sample test cases. These were the source for the test cases in this dataset.

Inspiration

My Inspiration to create this dataset was the scarcity of examples showcasing the implementation of machine learning on the domain of software testing. I would like to see if this dataset can be used to answer questions similar to the following--> * Finding semantic similarity between different test cases ranging across products and applications. * Automating the elimination of duplicate test cases in a test case repository. * Cana recommendation system be built for suggesting domain specific test cases to software testers.

Introduction

This repository hosts the Testing Roads for Autonomous VEhicLes (TRAVEL) dataset. TRAVEL is an extensive collection of virtual roads that have been used for testing lane assist/keeping systems (i.e., driving agents) and data from their execution in state of the art, physically accurate driving simulator, called BeamNG.tech. Virtual roads consist of sequences of road points interpolated using Cubic splines.

Along with the data, this repository contains instructions on how to install the tooling necessary to generate new data (i.e., test cases) and analyze them in the context of test regression. We focus on test selection and test prioritization, given their importance for developing high-quality software following the DevOps paradigms.

This dataset builds on top of our previous work in this area, including work on

test generation (e.g., AsFault, DeepJanus, and DeepHyperion) and the SBST CPS tool competition (SBST2021),

test selection: SDC-Scissor and related tool

test prioritization: automated test cases prioritization work for SDCs.

Dataset Overview

The TRAVEL dataset is available under the data folder and is organized as a set of experiments folders. Each of these folders is generated by running the test-generator (see below) and contains the configuration used for generating the data (experiment_description.csv), various statistics on generated tests (generation_stats.csv) and found faults (oob_stats.csv). Additionally, the folders contain the raw test cases generated and executed during each experiment (test..json).

The following sections describe what each of those files contains.

Experiment Description

The experiment_description.csv contains the settings used to generate the data, including:

Time budget. The overall generation budget in hours. This budget includes both the time to generate and execute the tests as driving simulations.

The size of the map. The size of the squared map defines the boundaries inside which the virtual roads develop in meters.

The test subject. The driving agent that implements the lane-keeping system under test. The TRAVEL dataset contains data generated testing the BeamNG.AI and the end-to-end Dave2 systems.

The test generator. The algorithm that generated the test cases. The TRAVEL dataset contains data obtained using various algorithms, ranging from naive and advanced random generators to complex evolutionary algorithms, for generating tests.

The speed limit. The maximum speed at which the driving agent under test can travel.

Out of Bound (OOB) tolerance. The test cases' oracle that defines the tolerable amount of the ego-car that can lie outside the lane boundaries. This parameter ranges between 0.0 and 1.0. In the former case, a test failure triggers as soon as any part of the ego-vehicle goes out of the lane boundary; in the latter case, a test failure triggers only if the entire body of the ego-car falls outside the lane.

Experiment Statistics

The generation_stats.csv contains statistics about the test generation, including:

Total number of generated tests. The number of tests generated during an experiment. This number is broken down into the number of valid tests and invalid tests. Valid tests contain virtual roads that do not self-intersect and contain turns that are not too sharp.

Test outcome. The test outcome contains the number of passed tests, failed tests, and test in error. Passed and failed tests are defined by the OOB Tolerance and an additional (implicit) oracle that checks whether the ego-car is moving or standing. Tests that did not pass because of other errors (e.g., the simulator crashed) are reported in a separated category.

The TRAVEL dataset also contains statistics about the failed tests, including the overall number of failed tests (total oob) and its breakdown into OOB that happened while driving left or right. Further statistics about the diversity (i.e., sparseness) of the failures are also reported.

Test Cases and Executions

Each test..json contains information about a test case and, if the test case is valid, the data observed during its execution as driving simulation.

The data about the test case definition include:

The road points. The list of points in a 2D space that identifies the center of the virtual road, and their interpolation using cubic splines (interpolated_points)

The test ID. The unique identifier of the test in the experiment.

Validity flag and explanation. A flag that indicates whether the test is valid or not, and a brief message describing why the test is not considered valid (e.g., the road contains sharp turns or the road self intersects)

The test data are organized according to the following JSON Schema and can be interpreted as RoadTest objects provided by the tests_generation.py module.

{ "type": "object", "properties": { "id": { "type": "integer" }, "is_valid": { "type": "boolean" }, "validation_message": { "type": "string" }, "road_points": { §\label{line:road-points}§ "type": "array", "items": { "$ref": "schemas/pair" }, }, "interpolated_points": { §\label{line:interpolated-points}§ "type": "array", "items": { "$ref": "schemas/pair" }, }, "test_outcome": { "type": "string" }, §\label{line:test-outcome}§ "description": { "type": "string" }, "execution_data": { "type": "array", "items": { "$ref" : "schemas/simulationdata" } } }, "required": [ "id", "is_valid", "validation_message", "road_points", "interpolated_points" ] }

Finally, the execution data contain a list of timestamped state information recorded by the driving simulation. State information is collected at constant frequency and includes absolute position, rotation, and velocity of the ego-car, its speed in Km/h, and control inputs from the driving agent (steering, throttle, and braking). Additionally, execution data contain OOB-related data, such as the lateral distance between the car and the lane center and the OOB percentage (i.e., how much the car is outside the lane).

The simulation data adhere to the following (simplified) JSON Schema and can be interpreted as Python objects using the simulation_data.py module.

{ "$id": "schemas/simulationdata", "type": "object", "properties": { "timer" : { "type": "number" }, "pos" : { "type": "array", "items":{ "$ref" : "schemas/triple" } } "vel" : { "type": "array", "items":{ "$ref" : "schemas/triple" } } "vel_kmh" : { "type": "number" }, "steering" : { "type": "number" }, "brake" : { "type": "number" }, "throttle" : { "type": "number" }, "is_oob" : { "type": "number" }, "oob_percentage" : { "type": "number" } §\label{line:oob-percentage}§ }, "required": [ "timer", "pos", "vel", "vel_kmh", "steering", "brake", "throttle", "is_oob", "oob_percentage" ] }

Dataset Content

The TRAVEL dataset is a lively initiative so the content of the dataset is subject to change. Currently, the dataset contains the data collected during the SBST CPS tool competition, and data collected in the context of our recent work on test selection (SDC-Scissor work and tool) and test prioritization (automated test cases prioritization work for SDCs).

SBST CPS Tool Competition Data

The data collected during the SBST CPS tool competition are stored inside data/competition.tar.gz. The file contains the test cases generated by Deeper, Frenetic, AdaFrenetic, and Swat, the open-source test generators submitted to the competition and executed against BeamNG.AI with an aggression factor of 0.7 (i.e., conservative driver).

    Name
    Map Size (m x m)
    Max Speed (Km/h)
    Budget (h)
    OOB Tolerance (%)
    Test Subject




    DEFAULT
    200 × 200
    120
    5 (real time)
    0.95
    BeamNG.AI - 0.7


    SBST
    200 × 200
    70
    2 (real time)
    0.5
    BeamNG.AI - 0.7

Specifically, the TRAVEL dataset contains 8 repetitions for each of the above configurations for each test generator totaling 64 experiments.

SDC Scissor

With SDC-Scissor we collected data based on the Frenetic test generator. The data is stored inside data/sdc-scissor.tar.gz. The following table summarizes the used parameters.

    Name
    Map Size (m x m)
    Max Speed (Km/h)
    Budget (h)
    OOB Tolerance (%)
    Test Subject




    SDC-SCISSOR
    200 × 200
    120
    16 (real time)
    0.5
    BeamNG.AI - 1.5

The dataset contains 9 experiments with the above configuration. For generating your own data with SDC-Scissor follow the instructions in its repository.

Dataset Statistics

Here is an overview of the TRAVEL dataset: generated tests, executed tests, and faults found by all the test generators grouped by experiment configuration. Some 25,845 test cases are generated by running 4 test generators 8 times in 2 configurations using the SBST CPS Tool Competition code pipeline (SBST in the table). We ran the test generators for 5 hours, allowing the ego-car a generous speed limit (120 Km/h) and defining a high OOB tolerance (i.e., 0.95), and we also ran the test generators using a smaller generation budget (i.e., 2 hours) and speed limit (i.e., 70 Km/h) while setting the OOB tolerance to a lower value (i.e., 0.85). We also collected some 5, 971 additional tests with SDC-Scissor (SDC-Scissor in the table) by running it 9 times for 16 hours using Frenetic as a test generator and defining a more realistic OOB tolerance (i.e., 0.50).

Generating new Data

Generating new data, i.e., test cases, can be done using the SBST CPS Tool Competition pipeline and the driving simulator BeamNG.tech.

Extensive instructions on how to install both software are reported inside the SBST CPS Tool Competition pipeline Documentation;

Synthetic Test Data Generation Market Outlook

According to our latest research, the global synthetic test data generation market size reached USD 1.56 billion in 2024. The market is experiencing robust growth, with a recorded CAGR of 18.9% from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a substantial value of USD 7.62 billion. This accelerated expansion is primarily driven by the increasing demand for high-quality, privacy-compliant test data across industries such as BFSI, healthcare, and IT & telecommunications, as organizations strive for advanced digital transformation while adhering to stringent regulatory requirements.

One of the most significant growth factors propelling the synthetic test data generation market is the rising emphasis on data privacy and security. As global regulations like GDPR and CCPA become more stringent, organizations are under immense pressure to eliminate the use of sensitive real data in testing environments. Synthetic test data generation offers a viable solution by creating realistic, non-identifiable datasets that closely mimic production data without exposing actual customer information. This not only reduces the risk of data breaches and non-compliance penalties but also accelerates the development and testing cycles by providing readily available, customizable test datasets. The growing adoption of privacy-enhancing technologies is thus a major catalyst for the market’s expansion.

Another crucial driver is the rapid advancement and adoption of artificial intelligence (AI) and machine learning (ML) technologies. Training robust AI and ML models requires massive volumes of diverse, high-quality data, which is often difficult to obtain due to privacy concerns or data scarcity. Synthetic test data generation bridges this gap by enabling the creation of large-scale, varied datasets tailored to specific model requirements. This capability is especially valuable in sectors like healthcare and finance, where real-world data is both sensitive and limited. As organizations continue to invest in AI-driven innovation, the demand for synthetic data solutions is expected to surge, fueling market growth further.

Additionally, the increasing complexity of modern software applications and IT infrastructures is amplifying the need for comprehensive, scenario-driven testing. Traditional test data generation methods often fall short in replicating the intricate data patterns and edge cases encountered in real-world environments. Synthetic test data generation tools, leveraging advanced algorithms and data modeling techniques, can simulate a wide range of test scenarios, including rare and extreme cases. This enhances the quality and reliability of software products, reduces time-to-market, and minimizes costly post-deployment defects. The confluence of digital transformation initiatives, DevOps adoption, and the shift towards agile development methodologies is thus creating fertile ground for the widespread adoption of synthetic test data generation solutions.

From a regional perspective, North America continues to dominate the synthetic test data generation market, driven by the presence of major technology firms, early adoption of advanced testing methodologies, and stringent regulatory frameworks. Europe follows closely, fueled by robust data privacy regulations and a strong focus on digital innovation across industries. Meanwhile, the Asia Pacific region is emerging as a high-growth market, supported by rapid digitalization, expanding IT infrastructure, and increasing investments in AI and cloud technologies. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a relatively slower pace, as organizations in these regions recognize the strategic value of synthetic data in achieving operational excellence and regulatory compliance.

Component Analysis

The synthetic test data generation market is segmented by component into software and services. The software segment holds the largest share, underpinned by the proliferation of advanced data generation platforms and tools that automate the creation of realistic, privacy-compliant test datasets. These software solutions offer a wide range of functionalities, including data masking, data subsetting, scenario simulation, and integration with continuous testing pipelines. As organizations increasingly transition to agile and DevOps methodologies, the need for seamless, scalable, and automated test data generation solutions is becoming p

chriscelaya/create-dataset-test dataset hosted on Hugging Face and contributed by the HF Datasets community

Synthetic Test Data Generation Market Outlook

According to our latest research, the global synthetic test data generation market size reached USD 1.85 billion in 2024 and is projected to grow at a robust CAGR of 31.2% during the forecast period, reaching approximately USD 21.65 billion by 2033. The marketÂ’s remarkable growth is primarily driven by the increasing demand for high-quality, privacy-compliant data to support software testing, AI model training, and data privacy initiatives across multiple industries. As organizations strive to meet stringent regulatory requirements and accelerate digital transformation, the adoption of synthetic test data generation solutions is surging at an unprecedented rate.

A key growth factor for the synthetic test data generation market is the rising awareness and enforcement of data privacy regulations such as GDPR, CCPA, and HIPAA. These regulations have compelled organizations to rethink their data management strategies, particularly when it comes to using real data in testing and development environments. Synthetic data offers a powerful alternative, allowing companies to generate realistic, risk-free datasets that mirror production data without exposing sensitive information. This capability is particularly vital for sectors like BFSI and healthcare, where data breaches can have severe financial and reputational repercussions. As a result, businesses are increasingly investing in synthetic test data generation tools to ensure compliance, reduce liability, and enhance data security.

Another significant driver is the explosive growth in artificial intelligence and machine learning applications. AI and ML models require vast amounts of diverse, high-quality data for effective training and validation. However, obtaining such data can be challenging due to privacy concerns, data scarcity, or labeling costs. Synthetic test data generation addresses these challenges by producing customizable, labeled datasets that can be tailored to specific use cases. This not only accelerates model development but also improves model robustness and accuracy by enabling the creation of edge cases and rare scenarios that may not be present in real-world data. The synergy between synthetic data and AI innovation is expected to further fuel market expansion throughout the forecast period.

The increasing complexity of software systems and the shift towards DevOps and continuous integration/continuous deployment (CI/CD) practices are also propelling the adoption of synthetic test data generation. Modern software development requires rapid, iterative testing across a multitude of environments and scenarios. Relying on masked or anonymized production data is often insufficient, as it may not capture the full spectrum of conditions needed for comprehensive testing. Synthetic data generation platforms empower development teams to create targeted datasets on demand, supporting rigorous functional, performance, and security testing. This leads to faster release cycles, reduced costs, and higher software quality, making synthetic test data generation an indispensable tool for digital enterprises.

In the realm of synthetic test data generation, Synthetic Tabular Data Generation Software plays a crucial role. This software specializes in creating structured datasets that resemble real-world data tables, making it indispensable for industries that rely heavily on tabular data, such as finance, healthcare, and retail. By generating synthetic tabular data, organizations can perform extensive testing and analysis without compromising sensitive information. This capability is particularly beneficial for financial institutions that need to simulate transaction data or healthcare providers looking to test patient management systems. As the demand for privacy-compliant data solutions grows, the importance of synthetic tabular data generation software is expected to increase, driving further innovation and adoption in the market.

From a regional perspective, North America currently leads the synthetic test data generation market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the presence of major technology providers, early adoption of advanced testing methodologies, and a strong regulatory focus on data privacy. EuropeÂ’s stringent privacy regulations an

Overview of Data

The site includes data only for the two subjects: Ceu-pacific and JBilling. For both the subjects, the ".model" shows the model created from the business rules obtained from respective websites, and "_HighLevelTests.csv" shows the tests generated. Among csv files, we show tests generated by both BUSTER and Exhaust as well.

Paper Abstract

Test cases that drive an application under test via its graphical user interface (GUI) consist of sequences of steps that perform actions on, or verify the state of, the application user interface. Such tests can be hard to maintain, especially if they are not properly modularized—that is, common steps occur in many test cases, which can make test maintenance cumbersome and expensive. Performing modularization manually can take up considerable human effort. To address this, we present an automated approach for modularizing GUI test cases. Our approach consists of multiple phases. In the first phase, it analyzes individual test cases to partition test steps into candidate subroutines, based on how user-interface elements are accessed in the steps. This phase can analyze the test cases only or also leverage execution traces of the tests, which involves a cost-accuracy tradeoff. In the second phase, the technique compares candidate subroutines across test cases, and refines them to compute the final set of subroutines. In the last phase, it creates callable subroutines, with parameterized data and control flow, and refactors the original tests to call the subroutines with context-specific data and control parameters. Our empirical results, collected using open-source applications, illustrate the effectiveness of the approach.

The Test Data Generation Tools market is poised for significant expansion, projected to reach an estimated USD 1.5 billion in 2025 and exhibit a robust Compound Annual Growth Rate (CAGR) of approximately 15% through 2033. This growth is primarily fueled by the escalating complexity of software applications, the increasing demand for agile development methodologies, and the critical need for comprehensive and realistic test data to ensure application quality and performance. Enterprises across all sizes, from large corporations to Small and Medium-sized Enterprises (SMEs), are recognizing the indispensable role of effective test data management in mitigating risks, accelerating time-to-market, and enhancing user experience. The drive for cost optimization and regulatory compliance further propels the adoption of advanced test data generation solutions, as manual data creation is often time-consuming, error-prone, and unsustainable in today's fast-paced development cycles. The market is witnessing a paradigm shift towards intelligent and automated data generation, moving beyond basic random or pathwise techniques to more sophisticated goal-oriented and AI-driven approaches that can generate highly relevant and production-like data. The market landscape is characterized by a dynamic interplay of established technology giants and specialized players, all vying for market share by offering innovative features and tailored solutions. Prominent companies like IBM, Informatica, Microsoft, and Broadcom are leveraging their extensive portfolios and cloud infrastructure to provide integrated data management and testing solutions. Simultaneously, specialized vendors such as DATPROF, Delphix Corporation, and Solix Technologies are carving out niches by focusing on advanced synthetic data generation, data masking, and data subsetting capabilities. The evolution of cloud-native architectures and microservices has created a new set of challenges and opportunities, with a growing emphasis on generating diverse and high-volume test data for distributed systems. Asia Pacific, particularly China and India, is emerging as a significant growth region due to the burgeoning IT sector and increasing investments in digital transformation initiatives. North America and Europe continue to be mature markets, driven by strong R&D investments and a high level of digital adoption. The market's trajectory indicates a sustained upward trend, driven by the continuous pursuit of software excellence and the critical need for robust testing strategies. This report provides an in-depth analysis of the global Test Data Generation Tools market, examining its evolution, current landscape, and future trajectory from 2019 to 2033. The Base Year for analysis is 2025, with the Estimated Year also being 2025, and the Forecast Period extending from 2025 to 2033. The Historical Period covered is 2019-2024. We delve into the critical aspects of this rapidly growing industry, offering insights into market dynamics, key players, emerging trends, and growth opportunities. The market is projected to witness substantial growth, with an estimated value reaching several million by the end of the forecast period.

Dataset

This dataset was created by Shubhrit Jain

Contents

Test Data Generation Tools Market Outlook

According to our latest research, the global Test Data Generation Tools market size reached USD 1.85 billion in 2024, demonstrating a robust expansion driven by the increasing adoption of automation in software development and quality assurance processes. The market is projected to grow at a CAGR of 13.2% from 2025 to 2033, reaching an estimated USD 5.45 billion by 2033. This growth is primarily fueled by the rising demand for efficient and accurate software testing, the proliferation of DevOps practices, and the need for compliance with stringent data privacy regulations. As organizations worldwide continue to focus on digital transformation and agile development methodologies, the demand for advanced test data generation tools is expected to further accelerate.

One of the core growth factors for the Test Data Generation Tools market is the increasing complexity of software applications and the corresponding need for high-quality, diverse, and realistic test data. As enterprises move toward microservices, cloud-native architectures, and continuous integration/continuous delivery (CI/CD) pipelines, the importance of automated and scalable test data solutions has become paramount. These tools enable development and QA teams to simulate real-world scenarios, uncover hidden defects, and ensure robust performance, thereby reducing time-to-market and enhancing software reliability. The growing adoption of artificial intelligence and machine learning in test data generation is further enhancing the sophistication and effectiveness of these solutions, enabling organizations to address complex data requirements and improve test coverage.

Another significant driver is the increasing regulatory scrutiny surrounding data privacy and security, particularly with regulations such as GDPR, HIPAA, and CCPA. Organizations are under pressure to minimize the use of sensitive production data in testing environments to mitigate risks related to data breaches and non-compliance. Test data generation tools offer anonymization, masking, and synthetic data creation capabilities, allowing companies to generate realistic yet compliant datasets for testing purposes. This not only ensures adherence to regulatory standards but also fosters a culture of data privacy and security within organizations. The heightened focus on data protection is expected to continue fueling the adoption of advanced test data generation solutions across industries such as BFSI, healthcare, and government.

Furthermore, the shift towards agile and DevOps methodologies has transformed the software development lifecycle, emphasizing speed, collaboration, and continuous improvement. In this context, the ability to rapidly generate, refresh, and manage test data has become a critical success factor. Test data generation tools facilitate seamless integration with CI/CD pipelines, automate data provisioning, and support parallel testing, thereby accelerating development cycles and improving overall productivity. With the increasing demand for faster time-to-market and higher software quality, organizations are investing heavily in modern test data management solutions to gain a competitive edge.

From a regional perspective, North America continues to dominate the Test Data Generation Tools market, accounting for the largest share in 2024. This leadership is attributed to the presence of major technology vendors, early adoption of advanced software testing practices, and a mature regulatory environment. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid digitalization, expanding IT and telecom sectors, and increasing investments in enterprise software solutions. Europe also represents a significant market, supported by stringent data protection laws and a strong focus on quality assurance. The Middle East & Africa and Latin America regions are gradually catching up, with growing awareness and adoption of test data generation tools among enterprises seeking to enhance their software development capabilities.

This dataset was created using LeRobot Data Studio.

  Dataset Structure

meta/info.json: { "codebase_version": "v2.1", "robot_type": "koch_screwdriver_follower", "total_episodes": 7, "total_frames": 1142, "total_tasks": 1, "total_videos": 0, "total_chunks": 0, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:7"}, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":… See the full description on the dataset page: https://huggingface.co/datasets/jackvial/create-dataset-test-background-tasks.

Test Data Generation AI Market Outlook

According to our latest research, the global Test Data Generation AI market size reached USD 1.29 billion in 2024 and is projected to grow at a robust CAGR of 24.7% from 2025 to 2033. By the end of the forecast period in 2033, the market is anticipated to attain a value of USD 10.1 billion. This substantial growth is primarily driven by the increasing complexity of software systems, the rising need for high-quality, compliant test data, and the rapid adoption of AI-driven automation across diverse industries.

The accelerating digital transformation across sectors such as BFSI, healthcare, and retail is one of the core growth factors propelling the Test Data Generation AI market. Organizations are under mounting pressure to deliver software faster, with higher quality and reduced risk, especially as business models become more data-driven and customer expectations for seamless digital experiences intensify. AI-powered test data generation tools are proving indispensable by automating the creation of realistic, diverse, and compliant test datasets, thereby enabling faster and more reliable software testing cycles. Furthermore, the proliferation of agile and DevOps practices is amplifying the demand for continuous testing environments, where the ability to generate synthetic test data on demand is a critical enabler of speed and innovation.

Another significant driver is the escalating emphasis on data privacy, security, and regulatory compliance. With stringent regulations such as GDPR, HIPAA, and CCPA in place, enterprises are compelled to ensure that non-production environments do not expose sensitive information. Test Data Generation AI solutions excel at creating anonymized or masked data sets that maintain the statistical properties of production data while eliminating privacy risks. This capability not only addresses compliance mandates but also empowers organizations to safely test new features, integrations, and applications without compromising user confidentiality. The growing awareness of these compliance imperatives is expected to further accelerate the adoption of AI-driven test data generation tools across regulated industries.

The ongoing evolution of AI and machine learning technologies is also enhancing the capabilities and appeal of Test Data Generation AI solutions. Advanced algorithms can now analyze complex data models, understand interdependencies, and generate highly realistic test data that mirrors production environments. This sophistication enables organizations to uncover hidden defects, improve test coverage, and simulate edge cases that would be challenging to create manually. As AI models continue to mature, the accuracy, scalability, and adaptability of test data generation platforms are expected to reach new heights, making them a strategic asset for enterprises striving for digital excellence and operational resilience.

Regionally, North America continues to dominate the Test Data Generation AI market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, is at the forefront due to its advanced technology ecosystem, early adoption of AI solutions, and the presence of leading software and cloud service providers. However, Asia Pacific is emerging as a high-growth region, fueled by rapid digitalization, expanding IT infrastructure, and increasing investments in AI research and development. Europe remains a key market, underpinned by strong regulatory frameworks and a growing focus on data privacy. Latin America and the Middle East & Africa, while still nascent, are exhibiting steady growth as enterprises in these regions recognize the value of AI-driven test data solutions for competitive differentiation and compliance assurance.

Component Analysis

The Test Data Generation AI market by component is segmented into Software and Services, each playing a pivotal role in driving the overall market expansion. The software segment commands the lion’s share of the market, as organizations increasingly prioritize automation and scalability in their test data generation processes. AI-powered software platforms offer a suite of features, including data profiling, masking, subsetting, and synthetic data creation, which are integral to modern DevOps and continuous integration/continuous deployment (CI/CD) pipelines. These platforms are designed to seamlessly integrate with existing testing tools, datab

A primer on your dataset's description (to be edited)

The influence of proper documentation on the reusability for research data should not be underestimated!
In order to help others understand how to interpret and reuse your data, we provide you with a few questions to help you structure your dataset's description (though please don't feel obligated to stick to them):

Context and methodology

• What is the research domain or project in which this dataset was created?
• Which purpose does this dataset serve?
• How was this dataset created?

Technical details

• What is the structure of this dataset? Do the folders and files follow a certain naming convention?
• Is any specific software required to open and work with this dataset?
• Are there any additional resources available regarding the dataset, e.g. documentation, source code, etc.?

Here are a few use cases for this project:

1. Board Game Assistance: This computer vision model can be used to assist players during "Between Two Cities" card games. By identifying different card types, the model can offer suggestions for the next move, enhancing player strategy and improving engagement.

2. Digital Game Creation: Game developers can use the model during the creation of digital versions of this game. By interpreting the different card classes, it can be programmed to handle different gameplay scenarios accurately.

3. Online Game Streaming: Streamers can use the model for real-time card classification during live board game streams. This would allow audience members to understand the game better, as the system could provide simultaneous analysis and commentary.

4. Automated Game Scoring: The model can be used to develop an automatic scoring system during live gameplay. By identifying the cards, the system could calculate scores based on the rules of the game, reducing manual efforts.

5. Game Tutorial Creation: By using the model to identify cards, content developers can create interactive tutorials or demonstration videos for the game. The identified cards can provide context, making it easier for new players to understand the game rules and strategies.

AI-Generated Test Data Market Outlook

According to our latest research, the global AI-Generated Test Data market size reached USD 1.12 billion in 2024, driven by the rapid adoption of artificial intelligence across software development and testing environments. The market is exhibiting a robust growth trajectory, registering a CAGR of 28.6% from 2025 to 2033. By 2033, the market is forecasted to achieve a value of USD 10.23 billion, reflecting the increasing reliance on AI-driven solutions for efficient, scalable, and accurate test data generation. This growth is primarily fueled by the rising complexity of software systems, stringent compliance requirements, and the need for enhanced data privacy across industries.

One of the primary growth factors for the AI-Generated Test Data market is the escalating demand for automation in software development lifecycles. As organizations strive to accelerate release cycles and improve software quality, traditional manual test data generation methods are proving inadequate. AI-generated test data solutions offer a compelling alternative by enabling rapid, scalable, and highly accurate data creation, which not only reduces time-to-market but also minimizes human error. This automation is particularly crucial in DevOps and Agile environments, where continuous integration and delivery necessitate fast and reliable testing processes. The ability of AI-driven tools to mimic real-world data scenarios and generate vast datasets on demand is revolutionizing the way enterprises approach software testing and quality assurance.

Another significant driver is the growing emphasis on data privacy and regulatory compliance, especially in sectors such as BFSI, healthcare, and government. With regulations like GDPR, HIPAA, and CCPA imposing strict controls on the use and sharing of real customer data, organizations are increasingly turning to AI-generated synthetic data for testing purposes. This not only ensures compliance but also protects sensitive information from potential breaches during the software development and testing phases. AI-generated test data tools can create anonymized yet realistic datasets that closely replicate production data, allowing organizations to rigorously test their systems without exposing confidential information. This capability is becoming a critical differentiator for vendors in the AI-generated test data market.

The proliferation of complex, data-intensive applications across industries further amplifies the need for sophisticated test data generation solutions. Sectors such as IT and telecommunications, retail and e-commerce, and manufacturing are witnessing a surge in digital transformation initiatives, resulting in intricate software architectures and interconnected systems. AI-generated test data solutions are uniquely positioned to address the challenges posed by these environments, enabling organizations to simulate diverse scenarios, validate system performance, and identify vulnerabilities with unprecedented accuracy. As digital ecosystems continue to evolve, the demand for advanced AI-powered test data generation tools is expected to rise exponentially, driving sustained market growth.

From a regional perspective, North America currently leads the AI-Generated Test Data market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the high concentration of technology giants, early adoption of AI technologies, and a mature regulatory landscape. Meanwhile, Asia Pacific is emerging as a high-growth region, propelled by rapid digitalization, expanding IT infrastructure, and increasing investments in AI research and development. Europe maintains a steady growth trajectory, bolstered by stringent data privacy regulations and a strong focus on innovation. As global enterprises continue to invest in digital transformation, the regional dynamics of the AI-generated test data market are expected to evolve, with significant opportunities emerging across developing economies.

Componen

Test Make

## Overview

Test Make is a dataset for object detection tasks - it contains Lift annotations for 1,574 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Block-based programming environments like SCRATCH are widely used in introductory programming courses. They facilitate learning pivotal programming concepts by eliminating syntactical errors, but logical errors that break the desired program behaviour are nevertheless possible. Finding such errors requires testing, i.e., running the program and checking its behaviour. In many programming environments this step can be automated by providing executable tests as code; in SCRATCH testing can only be done manually by clicking the green flag icon and observing the rendered stage. While this is arguably sufficient for learners, the lack of automated testing may be inhibitive for teachers having to provide feedback on their students’ solutions. In order to address this issue, we introduce a new category of blocks in SCRATCH that enables the creation of automated tests. This allows students and teachers alike to create tests for SCRATCH programs within the SCRATCH environment using familiar block-based programming logic. Furthermore, we extended the SCRATCH user interface with an accompanying test interface that facilitates the creation of test suites as well as batch processing sets of student solutions. We evaluated the SCRATCH test interface with 28 teachers, who created tests for a popular SCRATCH game and then used these tests to assess and provide feedback to student implementations of the same game. An overall accuracy of 0.93 of the teachers’ tests on all aspects of functionality of 21 student solutions demonstrates that teachers are able to create and use effective tests. Furthermore, a subsequent survey confirms that teachers consider the block-based test approach useful.

Edge Computing has refined data processing paradigm

Automated test generation - the use of tools to create all or part of test cases - has a critical role in controlling the cost of testing. A particular area of focus in automated test generation research is unit testing. Unit tests are intended to test the functionality of a small isolated unit of code - typically a class.

In automated test generation research, it is not abnormal to compare the effectiveness of the test cases generated by automation to those written by humans. Indeed, a common premise of automation research - implicitly or explicitly - is that effective automation can replace human effort. The hypothesis postulated is that, if we make enough advances, a tool could replace the tremendous effort expended by a human tester to create those unit tests.

This observation leads to two natural questions. Do the tests produced by humans and automation differ in the types of faults they detect? If so, in what ways are the tests produced and the faults detected different? Understanding when and how to deploy automation requires a clearer understanding of how the tests produced by humans and automation are different, and how those differences in turn affect the ability of those test cases to detect faults. Insight into the differences between human and automation-produced test cases could lead not only to improvements in the ability of automation to replace human effort, but improvements in our ability to use automation to augment human effort. The goal of this study is to explore and attempt to quantify those differences.

In this study, we make use of the EvoSuite test generation framework for Java. We generate test suites targeting two configurations - a traditional single-criterion configuration targeting Branch Coverage over the source code and a more sophisticated multi-objective configuration targeting eight criteria. Controlling for coverage level, we compare the suites generated by EvoSuite to those written by humans for five mature, popular open-source systems in terms of both their syntactic structure and their ability to detect 45 different types of faults. Our goal is not to declare a "winner'", but to identify the areas where humans and automation differ in their capabilities, and - in turn - to make recommendations on how human and automation effort can be combined to overcome gaps in the coverage of the other. We aim to identify lessons that will improve human practices, lead to the creation of more effective automation, and present natural opportunities to both augment and replace human effort.