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

This archive contains the test suites that were generated during the 2nd Competition on Software Testing (Test-Comp 2020) https://test-comp.sosy-lab.org/2020/

The competition was run by Dirk Beyer, LMU Munich, Germany. More information is available in the following article: Dirk Beyer. Second Competition on Software Testing: Test-Comp 2020. In Proceedings of the 23rd International Conference on Fundamental Approaches to Software Engineering (FASE 2020, Dublin, April 28-30), 2020. Springer. https://doi.org/10.1007/978-3-030-45234-6_25

Copyright (C) Dirk Beyer https://www.sosy-lab.org/people/beyer/

SPDX-License-Identifier: CC-BY-4.0 https://spdx.org/licenses/CC-BY-4.0.html

Contents:

LICENSE.txt specifies the license README.txt this file witnessFileByHash/ This directory contains test suites (witnesses for coverage). Each witness in this directory is stored in a file whose name is the SHA2 256-bit hash of its contents followed by the filename extension .zip. The format of each test suite is described on the format web page: https://gitlab.com/sosy-lab/software/test-format A test suite contains also metadata in order to relate it to the test problem for which it was produced. witnessInfoByHash/ This directory contains for each test suite (witness) in directory witnessFileByHash/ a record in JSON format (also using the SHA2 256-bit hash of the witness as filename, with .json as filename extension) that contains the meta data. witnessListByProgramHashJSON/ For convenient access to all test suites for a certain program, this directory represents a function that maps each program (via its SHA2256-bit hash) to a set of test suites (JSON records for test suites as described above) that the test tools have produced for that program. For each program for which test suites exist, the directory contains a JSON file (using the SHA2 256-bit hash of the program as filename, with .json as filename extension) that contains all JSON records for test suites for that program.

A similar data structure was used by SV-COMP and is described in the following article: Dirk Beyer. A Data Set of Program Invariants and Error Paths. In Proceedings of the 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR 2019, Montreal, Canada, May 26-27), pages 111-115, 2019. IEEE. https://doi.org/10.1109/MSR.2019.00026

Overview over archives from Test-Comp 2020 that are available at Zenodo:

https://doi.org/10.5281/zenodo.3678275 Witness store (containing the generated test suites) https://doi.org/10.5281/zenodo.3678264 Results (XML result files, log files, file mappings, HTML tables) https://doi.org/10.5281/zenodo.3678250 Test tasks, version testcomp20 https://doi.org/10.5281/zenodo.3574420 BenchExec, version 2.5.1

All benchmarks were executed for Test-Comp 2020, https://test-comp.sosy-lab.org/2020/ by Dirk Beyer, LMU Munich based on the components git@github.com:sosy-lab/sv-benchmarks.git testcomp20-0-gd6cd3e5dd4 git@gitlab.com:sosy-lab/test-comp/bench-defs.git testcomp19-84-gac76836 git@github.com:sosy-lab/benchexec.git 2.5.1-0-gffad635

Feel free to contact me in case of questions: https://www.sosy-lab.org/people/beyer/

Snapshot img

Software Testing Services Market Size 2025-2029

The software testing services market size is forecast to increase by USD 24487.3 billion, at a CAGR of 11.4% between 2024 and 2029.

The global mobile application testing services market continues to evolve in response to the increasing integration of mobile devices into daily digital interactions. A major driver of this momentum is the rising demand for seamless user experiences, which is accelerating the need for cross-platform compatibility and high-performance applications. As mobile applications multiply and user expectations heighten, testing services must ensure consistent functionality, usability, and responsiveness. The growing use of crowdsourced testing is another dynamic factor, enabling organizations to leverage distributed tester communities to accelerate delivery cycles and improve test coverage. This trend reflects the market's shift toward scalable, real-time testing approaches that match the speed of modern development.
Despite this progress, the availability of free and open-source testing tools presents a direct challenge to commercial service providers by reducing entry barriers and compressing margins. Additionally, the complexity of contemporary software applications and the continuous nature of agile development frameworks create operational strain on service providers, compelling them to continuously refine their offerings. Within this context, providers that prioritize test automation and advanced test data management are better positioned to navigate evolving user demands and retain strategic relevance.
The market has witnessed a shift from traditional testing frameworks to more agile-compatible models. The emphasis on real-time testing and crowdsourced platforms reflects a substantial behavioral and structural change. Simultaneously, the rise of open-source tools has altered the commercial dynamics, requiring providers to justify value through differentiated service capabilities and specialized expertise.

Major Market Trends & Insights

North America dominated the market and accounted for a 37% share in 2023
The market is expected to grow significantly in North America region as well over the forecast period.
Based on the Product the Functional segment led the market and was valued at USD 17220 billion of the global revenue in 2023
Based on the End-user the BFSI accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 156.80 Billion
Future Opportunities: USD 24487.3 Billion
CAGR (2024-2029): 11.4%
North America : Largest market in 2023

What will be the Size of the Software Testing Services Market during the forecast period?

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In today's complex software ecosystem, organizations are leveraging structured test plan development to align with evolving project needs and risk tolerances. Incorporating risk-based testing ensures resource prioritization across high-impact areas, while proven test estimation techniques help allocate effort precisely, especially in static and dynamic analysis testing cycles. As testing advances, test execution monitoring is used to validate ongoing test activities against quality assurance metrics and core software quality attributes. Tools for test coverage analysis, defect analysis reporting, and software vulnerability assessment are essential to identify gaps early, particularly when integrating penetration testing in security-focused pipelines.
A reliable QA strategy includes robust code review processes, end-to-end understanding of the software development lifecycle, and strict adherence to requirements traceability. Automation in test script development and intelligent test data generation supports repeatability and consistency. Infrastructure components like software configuration management, version control systems, and test environment provisioning form the backbone of scalable QA operations. Testing across platforms demands cloud-based testing, mobile device testing, and cross-browser testing to validate real-world compatibility, supported by a browser compatibility matrix. Operational metrics like performance tuning and scalability testing ensure long-term reliability, while continuous security hardening activities reduce production vulnerabilities.

How is this Software Testing Services Industry segmented?

The software testing services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Service

  Functional
  Digital testing
  Specialized offering


End-user

  BFSI
  Telecom and media
  Manufacturing
  Retail
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK

Test-Comp 2025

Test Suites

This file describes the contents of an archive of the 7th Competition on Software Testing (Test-Comp 2025). https://test-comp.sosy-lab.org/2025/

The competition was organized by Dirk Beyer, LMU Munich, Germany. More information is available in the following article: Dirk Beyer. Advances in Automatic Software Testing: Test-Comp 2025. In Proceedings of the 28th International Conference on Fundamental Approaches to Software Engineering (FASE 2025, Paris, May 3–8), 2025. Springer.

Copyright (C) 2025 Dirk Beyer https://www.sosy-lab.org/people/beyer/

SPDX-License-Identifier: CC-BY-4.0 https://spdx.org/licenses/CC-BY-4.0.html

Contents

• LICENSE.txt: specifies the license
• README.txt: this file
• fileByHash/: This directory contains test suites (witnesses for coverage). Each test witness in this directory is stored in a file whose name is the SHA2 256-bit hash of its contents followed by the filename extension .zip. The format of each test suite is described on the format web page: https://gitlab.com/sosy-lab/software/test-format A test suite contains also metadata in order to relate it to the test task for which it was produced.
• witnessInfoByHash/: This directory contains for each test suite (witness) in directory witnessFileByHash/ a record in JSON format (also using the SHA2 256-bit hash of the witness as filename, with .json as filename extension) that contains the meta data.
• witnessListByProgramHashJSON/: For convenient access to all test suites for a certain program, this directory represents a function that maps each program (via its SHA2256-bit hash) to a set of test suites (JSON records for test suites as described above) that the test-generation tools have produced for that program. For each program for which test suites exist, the directory contains a JSON file (using the SHA2 256-bit hash of the program as filename, with .json as filename extension) that contains all JSON records for test suites for that program.

This is a reduced data set, in which the 40 000 largest test suites were excluded.

A similar data structure was used by SV-COMP and is described in the following article: Dirk Beyer. A Data Set of Program Invariants and Error Paths. In Proceedings of the 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR 2019, Montreal, Canada, May 26-27), pages 111-115, 2019. IEEE. https://doi.org/10.1109/MSR.2019.00026

Related Archives

Overview of archives from Test-Comp 2025 that are available at Zenodo:

• https://doi.org/10.5281/zenodo.15034431 Test Suites from Test-Comp 2025 Test-Generation Tools. Store of coverage witnesses (containing the generated test suites)
• https://doi.org/10.5281/zenodo.15055359 Testers and Validators: FM-Tools Data Set for Test-Comp 2025. Metadata snapshot of the evaluated tools (DOIs, options, etc.)
• https://doi.org/10.5281/zenodo.15034433 Results of the 7th Intl. Competition on Software Testing (Test-Comp 2025). Results (XML result files, log files, file mappings, HTML tables)
• https://doi.org/10.5281/zenodo.15034421 SV-Benchmarks: Benchmark Set of Test-Comp 2025. Test-generation tasks, version testcomp25
• https://doi.org/10.5281/zenodo.15007216 BenchExec, version 3.29. Benchmarking framework
• https://doi.org/10.5281/zenodo.11193690 CoVeriTeam, version 1.2.1. Remote execution and continuous integration of testers

All benchmarks were executed for Test-Comp 2025 https://test-comp.sosy-lab.org/2025/ by Dirk Beyer, LMU Munich, based on the following components:

• https://gitlab.com/sosy-lab/benchmarking/fm-tools 2.2
• https://gitlab.com/sosy-lab/benchmarking/sv-benchmarks testcomp25
• https://gitlab.com/sosy-lab/test-comp/bench-defs testcomp25
• https://gitlab.com/sosy-lab/software/benchexec 3.29
• https://gitlab.com/sosy-lab/software/benchcloud 1.3.0
• https://gitlab.com/sosy-lab/software/fm-weck 1.4.5
• https://gitlab.com/sosy-lab/benchmarking/competition-scripts testcomp25
• https://gitlab.com/sosy-lab/test-comp/test-format testcomp25
• https://gitlab.com/sosy-lab/software/coveriteam 1.2.1

Contact

Feel free to contact me in case of questions: https://www.sosy-lab.org/people/beyer/

Airport Synthetic Data Generation Market Outlook

According to the latest research, the global airport synthetic data generation market size in 2024 is valued at USD 1.42 billion. The market is experiencing robust growth, driven by the increasing adoption of artificial intelligence and machine learning in airport operations. The market is projected to reach USD 6.81 billion by 2033, expanding at a remarkable CAGR of 18.9% from 2025 to 2033. One of the primary growth factors is the escalating need for high-quality, diverse datasets to train AI models for security, passenger management, and operational efficiency within airport environments.

Growth in the airport synthetic data generation market is primarily fueled by the aviation industry’s rapid digital transformation. Airports worldwide are increasingly leveraging synthetic data to overcome the limitations of real-world data, such as privacy concerns, data scarcity, and high labeling costs. The ability to generate vast amounts of representative, bias-free, and customizable data is empowering airports to develop and test AI-driven solutions for security, baggage handling, and passenger flow management. As airports strive to enhance operational efficiency and passenger experience, the demand for synthetic data generation solutions is expected to surge further, especially as regulatory frameworks around data privacy become more stringent.

Another significant driver is the growing sophistication of cyber threats and the need for advanced security and surveillance systems in airport environments. Synthetic data generation technologies enable the creation of diverse and complex scenarios that are difficult to capture in real-world datasets. This capability is crucial for training robust AI models for facial recognition, anomaly detection, and predictive maintenance, without compromising passenger privacy. The integration of synthetic data with real-time sensor and video feeds is also facilitating more accurate and adaptive security protocols, which is a top priority for airport authorities and government agencies worldwide.

Moreover, the increasing adoption of cloud-based solutions and the evolution of AI-as-a-Service (AIaaS) platforms are accelerating the deployment of synthetic data generation tools across airports of all sizes. Cloud deployment offers scalability, flexibility, and cost-effectiveness, enabling airports to access advanced synthetic data capabilities without significant upfront investments in infrastructure. Additionally, the collaboration between technology providers, airlines, and regulatory bodies is fostering innovation and standardization in synthetic data generation practices. This collaborative ecosystem is expected to drive further market growth by enabling seamless integration of synthetic data into existing airport management systems.

From a regional perspective, North America currently leads the airport synthetic data generation market, accounting for the largest share in 2024. This dominance is attributed to the presence of major technology vendors, high airport traffic, and early adoption of AI-driven solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid infrastructure development, increased air travel demand, and government initiatives to modernize airport operations. Europe, Latin America, and the Middle East & Africa are also exhibiting steady growth, supported by investments in smart airport projects and digital transformation strategies.

Component Analysis

The airport synthetic data generation market by component is segmented into software and services. Software solutions dominate the market, as they form the backbone of synthetic data generation, offering customizable platforms for data simulation, annotation, and validation. These solutions are crucial for generating large-scale, high-fidelity datasets tailored to specific airport applications, such as security, baggage handling, and passenger analytics. Leading software providers are continuou

Attribute Information

test case generation; unit testing; search-based software engineering; benchmark

Paper Abstract

Several promising techniques have been proposed to automate different tasks in software testing, such as test data generation for object-oriented software. However, reported studies in the literature only show the feasibility of the proposed techniques, because the choice of the employed artifacts in the case studies (e.g., software applications) is usually done in a non-systematic way. The chosen case study might be biased, and so it might not be a valid representative of the addressed type of software (e.g., internet applications and embedded systems). The common trend seems to be to accept this fact and get over it by simply discussing it in a threats to validity section. In this paper, we evaluate search-based software testing (in particular the EvoSuite tool) when applied to test data generation for open source projects. To achieve sound empirical results, we randomly selected 100 Java projects from SourceForge, which is the most popular open source repository (more than 300,000 projects with more than two million registered users). The resulting case study not only is very large (8,784 public classes for a total of 291,639 bytecode level branches), but more importantly it is statistically sound and representative for open source projects. Results show that while high coverage on commonly used types of classes is achievable, in practice environmental dependencies prohibit such high coverage, which clearly points out essential future research directions. To support this future research, our SF100 case study can serve as a much needed corpus of classes for test generation.

Test-Comp 2025

Competition Results

This file describes the contents of an archive of the 7th Competition on Software Testing (Test-Comp 2025). https://test-comp.sosy-lab.org/2025/

The competition was organized by Dirk Beyer, LMU Munich, Germany. More information is available in the following article: Dirk Beyer. Advances in Automatic Software Testing: Test-Comp 2025. In Proceedings of the 28th International Conference on Fundamental Approaches to Software Engineering (FASE 2025, Paris, May 3–8), 2025. Springer.

Copyright (C) 2025 Dirk Beyer https://www.sosy-lab.org/people/beyer/

SPDX-License-Identifier: CC-BY-4.0 https://spdx.org/licenses/CC-BY-4.0.html

To browse the competition results with a web browser, there are two options:

• start a local web server using php -S localhost:8000 in order to view the data in this archive, or
• browse https://test-comp.sosy-lab.org/2025/results/ in order to view the data on the Test-Comp web page.

Contents

• index.html: directs to the overview web page
• LICENSE-results.txt: specifies the license
• README-results.txt: this file
• results-validated/: results of validation runs
• results-verified/: results of test-generation runs and aggregated results

The folder results-validated/ contains the results from validation runs:

• *.results.txt: TXT results from BenchExec
• *.xml.bz2: XML results from BenchExec
• *.logfiles.zip: output from tools
• *.json.gz: mapping from files names to SHA 256 hashes for the file content

The folder results-verified/ contains the results from test-generation runs and aggregated results:

• index.html: overview web page with rankings and score table

• design.css: HTML style definitions

• *.results.txt: TXT results from BenchExec

• *.xml.bz2: XML results from BenchExec

• *.fixed.xml.bz2: XML results from BenchExec, status adjusted according to the validation results

• *.logfiles.zip: output from tools

• *.json.gz: mapping from files names to SHA 256 hashes for the file content

• *.xml.bz2.table.html: HTML views on the detailed results data as generated by BenchExec’s table generator

• : HTML views of the full benchmark set (all categories) for each tester

• META_*.table.html: HTML views of the benchmark set for each meta category for each tester, and over all testers

• : HTML views of the benchmark set for each category over all testers

• *.xml: XML table definitions for the above tables

• results-per-tool.php: List of results for each tool for review process in pre-run phase

• : List of results for a tool in HTML format with links

• quantilePlot-*: score-based quantile plots as visualization of the results

• quantilePlotShow.gp: example Gnuplot script to generate a plot

• score*: accumulated score results in various formats

The hashes of the file names (in the files *.json.gz) are useful for

• validating the exact contents of a file and
• accessing the files from the witness store.

Related Archives

Overview of archives from Test-Comp 2025 that are available at Zenodo:

• https://doi.org/10.5281/zenodo.15034431 Test Suites from Test-Comp 2025 Test-Generation Tools. Store of coverage witnesses (containing the generated test suites)
• https://doi.org/10.5281/zenodo.15055359 Testers and Validators: FM-Tools Data Set for Test-Comp 2025. Metadata snapshot of the evaluated tools (DOIs, options, etc.)
• https://doi.org/10.5281/zenodo.15034433 Results of the 7th Intl. Competition on Software Testing (Test-Comp 2025). Results (XML result files, log files, file mappings, HTML tables)
• https://doi.org/10.5281/zenodo.15034421 SV-Benchmarks: Benchmark Set of Test-Comp 2025. Test-generation tasks, version testcomp25
• https://doi.org/10.5281/zenodo.15007216 BenchExec, version 3.29. Benchmarking framework
• https://doi.org/10.5281/zenodo.11193690 CoVeriTeam, version 1.2.1. Remote execution and continuous integration of testers

All benchmarks were executed for Test-Comp 2025 https://test-comp.sosy-lab.org/2025/ by Dirk Beyer, LMU Munich, based on the following components:

• https://gitlab.com/sosy-lab/benchmarking/fm-tools 2.2
• https://gitlab.com/sosy-lab/benchmarking/sv-benchmarks testcomp25
• https://gitlab.com/sosy-lab/test-comp/bench-defs testcomp25
• https://gitlab.com/sosy-lab/software/benchexec 3.29
• https://gitlab.com/sosy-lab/software/benchcloud 1.3.0
• https://gitlab.com/sosy-lab/software/fm-weck 1.4.5
• https://gitlab.com/sosy-lab/benchmarking/competition-scripts testcomp25
• https://gitlab.com/sosy-lab/test-comp/test-format testcomp25
• https://gitlab.com/sosy-lab/software/coveriteam 1.2.1

Contact

Feel free to contact me in case of questions: https://www.sosy-lab.org/people/beyer/

This file describes the contents of an archive of the 1st Competition on Software Testing (Test-Comp 2019) https://test-comp.sosy-lab.org/2019/

The competition was run by Dirk Beyer, LMU Munich, Germany. More information is available in the following article: Dirk Beyer. First International Competition on Software Testing: Test-Comp 2019. International Journal on Software Tools for Technology Transfer, 2020.

Copyright (C) Dirk Beyer https://www.sosy-lab.org/people/beyer/

SPDX-License-Identifier: CC-BY-4.0 https://spdx.org/licenses/CC-BY-4.0.html

Contents:

LICENSE.txt specifies the license README.txt this file witnessFileByHash/ This directory contains test suites (witnesses for coverage). Each witness in this directory is stored in a file whose name is the SHA2 256-bit hash of its contents followed by the filename extension .zip. The format of each test suite is described on the format web page: https://gitlab.com/sosy-lab/software/test-format A test suite contains also metadata in order to relate it to the test problem for which it was produced. witnessInfoByHash/ This directory contains for each test suite (witness) in directory witnessFileByHash/ a record in JSON format (also using the SHA2 256-bit hash of the witness as filename, with .json as filename extension) that contains the meta data. witnessListByProgramHashJSON/ For convenient access to all test suites for a certain program, this directory represents a function that maps each program (via its SHA2 256-bit hash) to a set of test suites (JSON records for test suites as described above) that the test tools have produced for that program. For each program for which test suites exist, the directory contains a JSON file (using the SHA2 256-bit hash of the program as filename, with .json as filename extension) that contains all JSON records for test suites for that program.

A similar data structure was used by SV-COMP and is described in the following article: Dirk Beyer. A Data Set of Program Invariants and Error Paths. In Proceedings of the 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR 2019, Montreal, Canada, May 26-27), pages 111-115, 2019. IEEE. https://doi.org/10.1109/MSR.2019.00026

Overview over archives from Test-Comp 2019 that are available at Zenodo:

https://doi.org/10.5281/zenodo.3856669 Witness store (containing the generated test suites) https://doi.org/10.5281/zenodo.3856661 Results (XML result files, log files, file mappings, HTML tables) https://doi.org/10.5281/zenodo.3856478 Test tasks, version testcomp19 https://doi.org/10.5281/zenodo.2561835 BenchExec, version 1.18

All benchmarks were executed for Test-Comp 2019, https://test-comp.sosy-lab.org/2019/ by Dirk Beyer, LMU Munich based on the components git@github.com:sosy-lab/sv-benchmarks.git testcomp19-0-g6a770a9c1 git@gitlab.com:sosy-lab/test-comp/bench-defs.git testcomp19-0-g1677027 git@github.com:sosy-lab/benchexec.git 1.18-0-gff72868

Feel free to contact me in case of questions: https://www.sosy-lab.org/people/beyer/

Snapshot img

System-On-Chip (SOC) Test Equipment Market Size 2025-2029

The system-on-chip (SOC) test equipment market size is forecast to increase by USD 2.5 billion at a CAGR of 9.5% between 2024 and 2029.

The market experiences robust growth, driven by the escalating demand for SOCs due to their benefits, including power efficiency, reduced form factor, and enhanced performance. This trend is further fueled by the adoption of Field-Programmable Gate Array (FPGA) and embedded testing technologies, enabling real-time testing and debugging of complex SOC designs. However, regulatory hurdles impact adoption, with stringent regulations governing the production and testing of SOCs in various industries. Additionally, supply chain inconsistencies temper growth potential, as the globalized supply chain for SOC components and test equipment presents challenges in terms of quality, reliability, and delivery. A significant challenge emerging in the market is the growing risk of cybersecurity threats from foreign electronic Original Equipment Manufacturers (OEMs), necessitating robust security measures to protect intellectual property and maintain data confidentiality. Key trends include the integration of advanced processor technologies to reduce energy waste, the rise of 5G technology and the Internet of Things (IoT) driving increased investments, and the reliance of SOC companies on IP core providers.
Companies seeking to capitalize on market opportunities and navigate challenges effectively must focus on innovation, regulatory compliance, and supply chain resilience. Power consumption and efficiency remain critical concerns, with the need for continuous innovation to meet the demands of AI and computing activities.

What will be the Size of the System-On-Chip (SOC) Test Equipment Market during the forecast period?

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The SOC test equipment market is experiencing significant activity and trends, driven by the increasing complexity of integrated circuits and the need for efficient and accurate testing. Test cost optimization is a key focus, with test environment and design-for-testability (DFT) playing crucial roles in reducing testing costs. Functional verification, test analysis, and system integration require advanced test software and reporting tools to ensure thorough testing and quick identification of issues. Thermal verification and test process improvement are essential for ensuring reliable operation in extreme temperatures and reducing testing time. Test data generation, test automation tools, and test infrastructure are vital components of the test process, enabling efficient and effective testing of performance verification, power verification, and test optimization. Multi-chip systems, and Power management systems. SOCs are utilized in various applications such as IT, telecommunication, laptops, Macs, iPads, database management, fraud detection systems, cybersecurity, and more
Firmware development and security verification require specialized tools and techniques, including fault coverage analysis, test case management, test scripting, and fault simulation. Test hardware and test development are integral to design validation, with built-in self-test (BIST) and reliability verification ensuring the integrity of the silicon. Power verification and performance optimization are critical for meeting the demands of modern applications, while test metrics and test results databases enable data-driven test strategy decisions and continuous improvement. In the realm of software development, test automation tools and test scripting are essential for efficient and effective testing of embedded software.
Overall, the SOC test equipment market is dynamic and evolving, with a focus on improving testing efficiency, accuracy, and cost-effectiveness while addressing the challenges of increasing design complexity and the need for advanced verification capabilities.

How is this System-On-Chip (SOC) Test Equipment Industry segmented?

The system-on-chip (SOC) test equipment industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Consumer electronics
  IT and telecommunication
  Automotive
  Others


End-user

  Integrated device manufacturer
  Foundry
  Design house


Deployment

  On-premises
  Cloud-based
  Hybrid


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The consumer electronics segment is estimated to witness significant growth during the forecast period. The SOC test equipment market encompasses various applications, including test results analysis, design

Airport Synthetic Data Generation Market Outlook

According to the latest research, the global airport synthetic data generation market size in 2024 is valued at USD 1.42 billion. The market is experiencing robust growth, driven by the increasing adoption of artificial intelligence and machine learning in airport operations. The market is projected to reach USD 6.81 billion by 2033, expanding at a remarkable CAGR of 18.9% from 2025 to 2033. One of the primary growth factors is the escalating need for high-quality, diverse datasets to train AI models for security, passenger management, and operational efficiency within airport environments.

Growth in the airport synthetic data generation market is primarily fueled by the aviation industry’s rapid digital transformation. Airports worldwide are increasingly leveraging synthetic data to overcome the limitations of real-world data, such as privacy concerns, data scarcity, and high labeling costs. The ability to generate vast amounts of representative, bias-free, and customizable data is empowering airports to develop and test AI-driven solutions for security, baggage handling, and passenger flow management. As airports strive to enhance operational efficiency and passenger experience, the demand for synthetic data generation solutions is expected to surge further, especially as regulatory frameworks around data privacy become more stringent.

Another significant driver is the growing sophistication of cyber threats and the need for advanced security and surveillance systems in airport environments. Synthetic data generation technologies enable the creation of diverse and complex scenarios that are difficult to capture in real-world datasets. This capability is crucial for training robust AI models for facial recognition, anomaly detection, and predictive maintenance, without compromising passenger privacy. The integration of synthetic data with real-time sensor and video feeds is also facilitating more accurate and adaptive security protocols, which is a top priority for airport authorities and government agencies worldwide.

Moreover, the increasing adoption of cloud-based solutions and the evolution of AI-as-a-Service (AIaaS) platforms are accelerating the deployment of synthetic data generation tools across airports of all sizes. Cloud deployment offers scalability, flexibility, and cost-effectiveness, enabling airports to access advanced synthetic data capabilities without significant upfront investments in infrastructure. Additionally, the collaboration between technology providers, airlines, and regulatory bodies is fostering innovation and standardization in synthetic data generation practices. This collaborative ecosystem is expected to drive further market growth by enabling seamless integration of synthetic data into existing airport management systems.

From a regional perspective, North America currently leads the airport synthetic data generation market, accounting for the largest share in 2024. This dominance is attributed to the presence of major technology vendors, high airport traffic, and early adoption of AI-driven solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid infrastructure development, increased air travel demand, and government initiatives to modernize airport operations. Europe, Latin America, and the Middle East & Africa are also exhibiting steady growth, supported by investments in smart airport projects and digital transformation strategies.

Component Analysis

The airport synthetic data generation market by component is segmented into software and services. Software solutions dominate the market, as they form the backbone of synthetic data generation, offering customizable platforms for data simulation, annotation, and validation. These solutions are crucial for generating large-scale, high-fidelity datasets tailored to specific airport applications, such as security, baggage handling, and passenger analytics. Leading software providers are continuously enh

Test Suites

This file describes the contents of an archive of the 3rd Competition on Software Testing (Test-Comp 2021).
https://test-comp.sosy-lab.org/2021/

The competition was run by Dirk Beyer, LMU Munich, Germany.
More information is available in the following article:
Dirk Beyer. Status Report on Software Testing: Test-Comp 2021. In Proceedings of the 24th International Conference on Fundamental Approaches to Software Engineering (FASE 2021, Luxembourg, March 27 - April 1), 2021. Springer.

Copyright (C) Dirk Beyer
https://www.sosy-lab.org/people/beyer/

SPDX-License-Identifier: CC-BY-4.0
https://spdx.org/licenses/CC-BY-4.0.html

Contents

• LICENSE.txt: specifies the license
• README.txt: this file
• witnessFileByHash/: This directory contains test suites (witnesses for coverage). Each witness in this directory is stored in a file whose name is the SHA2 256-bit hash of its contents followed by the filename extension .zip. The format of each test suite is described on the format web page: https://gitlab.com/sosy-lab/software/test-format A test suite contains also metadata in order to relate it to the test problem for which it was produced.
• witnessInfoByHash/: This directory contains for each test suite (witness) in directory witnessFileByHash/ a record in JSON format (also using the SHA2 256-bit hash of the witness as filename, with .json as filename extension) that contains the meta data.
• witnessListByProgramHashJSON/: For convenient access to all test suites for a certain program, this directory represents a function that maps each program (via its SHA2256-bit hash) to a set of test suites (JSON records for test suites as described above) that the test tools have produced for that program. For each program for which test suites exist, the directory contains a JSON file (using the SHA2 256-bit hash of the program as filename, with .json as filename extension) that contains all JSON records for test suites for that program.

A similar data structure was used by SV-COMP and is described in the following article:
Dirk Beyer. A Data Set of Program Invariants and Error Paths. In Proceedings of the 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR 2019, Montreal, Canada, May 26-27), pages 111-115, 2019. IEEE.
https://doi.org/10.1109/MSR.2019.00026

Other Archives

Overview over archives from Test-Comp 2021 that are available at Zenodo:

• https://doi.org/10.5281/zenodo.4459466 Witness store (containing the generated test suites)
• https://doi.org/10.5281/zenodo.4459470 Results (XML result files, log files, file mappings, HTML tables)
• https://doi.org/10.5281/zenodo.4459132 Test tasks, version testcomp21
• https://doi.org/10.5281/zenodo.4317433 BenchExec, version 3.6

All benchmarks were executed for Test-Comp 2021 https://test-comp.sosy-lab.org/2021/
by Dirk Beyer, LMU Munich, based on the following components:

• https://gitlab.com/sosy-lab/test-comp/archives-2021 testcomp21-0-gdacd4bf
• https://gitlab.com/sosy-lab/software/sv-benchmarks testcomp21-0-gefea738258
• https://gitlab.com/sosy-lab/software/benchexec 3.6-0-gb278ebbb
• https://gitlab.com/sosy-lab/benchmarking/competition-scripts testcomp21-0-g8339740
• https://gitlab.com/sosy-lab/test-comp/bench-defs testcomp21-0-g9d532c9

Contact

Feel free to contact me in case of questions: https://www.sosy-lab.org/people/beyer/

IntroductionLinking free-text addresses to unique identifiers in a structural address database [the Ordnance Survey unique property reference number (UPRN) in the United Kingdom (UK)] is a necessary step for downstream geospatial analysis in many digital health systems, e.g., for identification of care home residents, understanding housing transitions in later life, and informing decision making on geographical health and social care resource distribution. However, there is a lack of open-source tools for this task with performance validated in a test data set.MethodsIn this article, we propose a generalisable solution (A Framework for Linking free-text Addresses to Ordnance Survey UPRN database, FLAP) based on a machine learning–based matching classifier coupled with a fuzzy aligning algorithm for feature generation with better performance than existing tools. The framework is implemented in Python as an Open Source tool (available at Link). We tested the framework in a real-world scenario of linking individual’s (n=771,588) addresses recorded as free text in the Community Health Index (CHI) of National Health Service (NHS) Tayside and NHS Fife to the Unique Property Reference Number database (UPRN DB).ResultsWe achieved an adjusted matching accuracy of 0.992 in a test data set randomly sampled (n=3,876) from NHS Tayside and NHS Fife CHI addresses. FLAP showed robustness against input variations including typographical errors, alternative formats, and partially incorrect information. It has also improved usability compared to existing solutions allowing the use of a customised threshold of matching confidence and selection of top n candidate records. The use of machine learning also provides better adaptability of the tool to new data and enables continuous improvement.DiscussionIn conclusion, we have developed a framework, FLAP, for linking free-text UK addresses to the UPRN DB with good performance and usability in a real-world task.

Report Overview

The Global API Testing Market is on a remarkable growth trajectory, projected to reach USD 12.4 billion by 2033, up from USD 1.5 billion in 2023. This impressive expansion reflects a Compound Annual Growth Rate (CAGR) of 23.5% from 2024 to 2033. In 2023, North America emerged as the leading regional market, securing a 35.4% market share with revenues of approximately USD 0.5 billion. This dominance is driven by the region's strong technological infrastructure, early adoption of advanced testing solutions, and the presence of key industry players.

API testing is a software testing practice that evaluates the performance, functionality, reliability, and security of application programming interfaces (APIs). Unlike user interface (UI) testing, which examines the visual aspects of an application, API testing delves into the backend, assessing the core functional components without a graphical interface. It involves sending various requests to the API and assessing the responses to ensure that the API behaves as expected under different conditions, including extreme edge cases and under load​

The API testing market is expanding significantly due to the growing importance of APIs in modern software development. APIs facilitate the interaction between different software programs, making them fundamental to creating flexible, scalable applications. As businesses increasingly rely on integrated technologies, the demand for robust API testing solutions that can ensure APIs function correctly and securely continues to rise. This demand is spurred by the need for faster development cycles in agile and DevOps environments, where API testing plays a critical role in continuous integration and deployment pipelines​.

One of the primary drivers of the API testing market is the necessity for secure and efficient communication between various software components and microservices. As companies digitize and integrate more systems, the complexity of APIs increases, highlighting the need for thorough testing to avoid potential breakdowns and security breaches. Additionally, the shift towards cloud-based solutions and microservices architecture boosts the need for API testing to manage and monitor the interactions and data exchanges between decentralized services effectively​.

The demand for API testing is influenced by its ability to identify and resolve issues early in the development cycle, which significantly reduces costs and time to market. Organizations are looking for API testing solutions that offer automation, ease of use, and integration with existing tools. High demand is especially noted in sectors that rely heavily on software reliability and security, such as finance, healthcare, and e-commerce​.

The growing trend towards automation in testing presents substantial opportunities in the API testing market. Tools that facilitate automated, continuous testing within the development pipeline are particularly valuable. There's also a rising opportunity for tools that support newer API protocols like GraphQL and WebSockets, catering to modern application needs. As businesses continue to adopt agile methodologies and DevOps practices, the integration of advanced API testing tools that can provide real-time feedback and support rapid deployment cycles will become crucial​.

Technological advancements are profoundly shaping the API testing field. The integration of artificial intelligence (AI) and machine learning (ML) is improving the efficiency and effectiveness of testing processes. These technologies help automate the generation of test cases, predict...

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Automated Test Equipment (ATE) Market Size 2025-2029

The automated test equipment (ate) market size is forecast to increase by USD 2.59 billion, at a CAGR of 5.7% between 2024 and 2029.

The market is driven by the increasing demand for augmented production of electronic goods, particularly in the automotive industry with the adoption of wireless technologies. The integration of advanced technologies such as Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) in vehicles is fueling the need for rigorous testing and quality assurance. However, the high cost of ATE remains a significant challenge for market growth. Companies must invest in cost-effective solutions to reduce the financial burden and make ATE more accessible to small and medium-sized enterprises (SMEs) and startups.
Furthermore, the market faces the challenge of keeping up with the rapid technological advancements and ensuring compatibility with the latest devices and systems. To capitalize on the market opportunities, companies must focus on innovation, cost reduction, and collaboration with technology providers to deliver advanced testing solutions that cater to the evolving needs of the electronics industry.

What will be the Size of the Automated Test Equipment (ATE) Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by advancements in technology and the expanding application landscape. Verification and validation are crucial aspects of the design process, and ATE plays a pivotal role in ensuring the accuracy and reliability of electronic components and systems. Equipment calibration is an ongoing process to maintain the precision of ATE, ensuring parametric test limits are met. Analog circuit testing and digital test generation are essential for assessing the functionality of various electronic systems. Functional safety testing, power integrity simulation, and RF testing techniques are increasingly important for ensuring the safety and performance of complex electronic systems.

Data acquisition systems and ATE software integration enable seamless data collection and analysis, while failure analysis reporting and test result reporting provide valuable insights into system performance. Pin electronics and test head design are critical components of ATE, ensuring accurate signal measurement and test fixture design. Mixed-signal testing, test program development, and DFT techniques are essential for addressing the challenges of testing complex systems. Test time reduction and throughput optimization are key priorities for manufacturers, while system diagnostics and functional test coverage are essential for ensuring system reliability. For instance, a leading semiconductor company reported a 25% increase in production efficiency through the implementation of advanced ATE solutions.

Industry growth in the ATE market is expected to reach 7% annually, driven by the increasing demand for advanced testing solutions across various sectors.

How is this Automated Test Equipment (ATE) Industry segmented?

The automated test equipment (ate) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Consumer electronics
  Telecommunications
  Aerospace and defense
  Others


Product

  Non-memory ATE
  Memory
  Discrete


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    Australia
    China
    Japan
    South Korea


  Rest of World (ROW)

By End-user Insights

The consumer electronics segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth, particularly in the consumer electronics segment. This expansion is driven by the rising disposable incomes, decreasing costs of consumer electronics, and evolving lifestyles in developing countries. According to the Capgemini Research Institutes World Wealth Report 2024, the global high-net-worth individuals (HNWI) population grew by 5.1% in 2023, reaching 22.8 million, with a combined wealth increase of 4.7%, totaling USD86.8 trillion. This economic growth signifies the increasing purchasing power of individuals for consumer electronics. Mixed-signal integrated circuits (ICs) are increasingly being adopted in this segment due to their cost-effectiveness, low power consumption, and superior performance compared to traditional ICs.

The consumer electronics segment's growing year-over-year growth rate in the global ATE market is primarily due to the high demand for mobile devices such as smartphones and tablet

This file describes the contents of an archive of the 1st Competition on Software Testing (Test-Comp 2019) https://test-comp.sosy-lab.org/2019/

The competition was run by Dirk Beyer, LMU Munich, Germany. More information is available in the following article: Dirk Beyer. First International Competition on Software Testing: Test-Comp 2019. International Journal on Software Tools for Technology Transfer, 2020.

Copyright (C) Dirk Beyer https://www.sosy-lab.org/people/beyer/

SPDX-License-Identifier: CC-BY-4.0 https://spdx.org/licenses/CC-BY-4.0.html

To browse the competition results with a web browser, there are two options: - start a local web server using php -S localhost:8000 in order to view the data in this archive, or - browse https://test-comp.sosy-lab.org/2019/results/ in order to view the data on the Test-Comp web page.

Contents:

index.html directs to the overview web page LICENSE.txt specifies the license README.txt this file results-validated/ results of validation runs results-verified/ results of verification runs and aggregated results

The folder results-validated/ contains the results from validation runs:

• *.xml.bz2 XML results from BenchExec
• *.logfiles.zip output from tools
• *.json.gz mapping from files names to SHA 256 hashes for the file content

The folder results-verified/ contains the results from test-generation runs and aggregated results:

index.html overview web page with rankings and score table design.css HTML style definitions *.xml.bz2 XML results from BenchExec *.merged.xml.bz2 XML results from BenchExec, status adjusted according to the validation results *.logfiles.zip output from tools *.json.gz mapping from files names to SHA 256 hashes for the file content *.xml.bz2.table.html HTML views on the detailed results data as generated by BenchExec's table generator .All.table.html HTML views of the full benchmark set (all categories) for each tool META_.table.html HTML views of the benchmark set for each meta category for each tool, and over all tools *.table.html HTML views of the benchmark set for each category over all tools iZeCa0gaey.html HTML views per tool

quantilePlot-* score-based quantile plots as visualization of the results quantilePlotShow.gp example Gnuplot script to generate a plot score* accumulated score results in various formats

The hashes of the file names (in the files *.json.gz) are useful for - validating the exact contents of a file and - accessing the files from the witness store.

Overview over archives from Test-Comp 2019 that are available at Zenodo:

https://doi.org/10.5281/zenodo.3856669 Witness store (containing the generated test suites) https://doi.org/10.5281/zenodo.3856661 Results (XML result files, log files, file mappings, HTML tables) https://doi.org/10.5281/zenodo.3856478 Test tasks, version testcomp19 https://doi.org/10.5281/zenodo.2561835 BenchExec, version 1.18

All benchmarks were executed for Test-Comp 2019, https://test-comp.sosy-lab.org/2019/ by Dirk Beyer, LMU Munich based on the components git@github.com:sosy-lab/sv-benchmarks.git testcomp19-0-g6a770a9c1 git@gitlab.com:sosy-lab/test-comp/bench-defs.git testcomp19-0-g1677027 git@github.com:sosy-lab/benchexec.git 1.18-0-gff72868

Feel free to contact me in case of questions: https://www.sosy-lab.org/people/beyer/

Competition Results

This file describes the contents of an archive of the 3rd Competition on Software Testing (Test-Comp 2021). https://test-comp.sosy-lab.org/2021/

The competition was run by Dirk Beyer, LMU Munich, Germany. More information is available in the following article: Dirk Beyer. Status Report on Software Testing: Test-Comp 2021. In Proceedings of the 24th International Conference on Fundamental Approaches to Software Engineering (FASE 2021, Luxembourg, March 27 - April 1), 2021. Springer.

Copyright (C) Dirk Beyer https://www.sosy-lab.org/people/beyer/

SPDX-License-Identifier: CC-BY-4.0 https://spdx.org/licenses/CC-BY-4.0.html

To browse the competition results with a web browser, there are two options:

start a local web server using php -S localhost:8000 in order to view the data in this archive, or

browse https://test-comp.sosy-lab.org/2021/results/ in order to view the data on the Test-Comp web page.

Contents

index.html: directs to the overview web page

LICENSE.txt: specifies the license

README.txt: this file

results-validated/: results of validation runs

results-verified/: results of test-generation runs and aggregated results

The folder results-validated/ contains the results from validation runs:

*.xml.bz2: XML results from BenchExec

*.logfiles.zip: output from tools

*.json.gz: mapping from files names to SHA 256 hashes for the file content

The folder results-verified/ contains the results from test-generation runs and aggregated results:

index.html: overview web page with rankings and score table

design.css: HTML style definitions

*.xml.bz2: XML results from BenchExec

*.merged.xml.bz2: XML results from BenchExec, status adjusted according to the validation results

*.logfiles.zip: output from tools

*.json.gz: mapping from files names to SHA 256 hashes for the file content

*.xml.bz2.table.html: HTML views on the detailed results data as generated by BenchExec’s table generator

*.All.table.html: HTML views of the full benchmark set (all categories) for each tool

META_*.table.html: HTML views of the benchmark set for each meta category for each tool, and over all tools

*.table.html: HTML views of the benchmark set for each category over all tools

iZeCa0gaey.html: HTML views per tool

quantilePlot-*: score-based quantile plots as visualization of the results

quantilePlotShow.gp: example Gnuplot script to generate a plot

score*: accumulated score results in various formats

The hashes of the file names (in the files *.json.gz) are useful for

validating the exact contents of a file and

accessing the files from the witness store.

Other Archives

Overview over archives from Test-Comp 2021 that are available at Zenodo:

https://doi.org/10.5281/zenodo.4459466 Witness store (containing the generated test suites)

https://doi.org/10.5281/zenodo.4459470 Results (XML result files, log files, file mappings, HTML tables)

https://doi.org/10.5281/zenodo.4459132 Test tasks, version testcomp21

https://doi.org/10.5281/zenodo.4317433 BenchExec, version 3.6

All benchmarks were executed for Test-Comp 2021 https://test-comp.sosy-lab.org/2021/ by Dirk Beyer, LMU Munich, based on the following components:

https://gitlab.com/sosy-lab/test-comp/archives-2021 testcomp21-0-gdacd4bf

https://gitlab.com/sosy-lab/software/sv-benchmarks testcomp21-0-gefea738258

https://gitlab.com/sosy-lab/software/benchexec 3.6-0-gb278ebbb

https://gitlab.com/sosy-lab/benchmarking/competition-scripts testcomp21-0-g8339740

https://gitlab.com/sosy-lab/test-comp/bench-defs testcomp21-0-g9d532c9

Contact

Feel free to contact me in case of questions: https://www.sosy-lab.org/people/beyer/

🛍️ Retail Star Schema (Normalized & Denormalized) – Synthetic Dataset

This dataset provides a simulated retail data warehouse designed using star schema modeling principles.

It includes both normalized and denormalized versions of a retail sales star schema, making it a valuable resource for data engineers, analysts, and data warehouse enthusiasts who want to explore real-world scenarios, performance tuning, and modeling strategies.

📁 Dataset Structure

This dataset set has two Fact tables: - fact_sales_normalized.csv – No columns from the dim_* tables have been normalised. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12492162%2F11f3c0350acd609e6b9d9336d0abb448%2FNormalized-Retail-Star-Schema.png?generation=1745327115564885&alt=media" alt="Normalized Star Schema">

• fact_sales_denormalized.csv – Specific columns from certain dim_* tables have been normalised. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12492162%2Fb567c752c7bc8bc55d9d6142d6ac40cf%2FDenormalized-Retial-Star-Schema.png?generation=1745327148166677&alt=media" alt="Denormalized Star Schema">

However, the dim_* table stay the same for both as follows: - Dim_Customers.csv - Dim_Products.csv - Dim_Stores.csv - Dim_Dates.csv - Dim_Salesperson - Dim_Campaign

🧠 Use Cases

• Practice star schema design and dimensional modeling
• Learn how to denormalize dimensions for BI and analytics performance
• Benchmark analytical queries (joins, aggregations, filtering)
• Test data pipelines, ETL/ELT transformations, and query optimization strategies

Explore how denormalization affects storage, redundancy, and performance

📌 Notes

All data is synthetic and randomly generated via python scripts that use polars library for data manipulation— no real customer or business data is included.

Ideal for use with tools like SQL engines, Redshift, BigQuery, Snowflake, or even DuckDB.

📎 Credits

Shrinivas Vishnupurikar, Data Engineer @Velotio Technologies.

Pulse Pattern Generator Market Outlook

The global Pulse Pattern Generator market size was valued at approximately USD 490 million in 2023 and is projected to reach USD 850 million by 2032, growing at a CAGR of 6.5% during the forecast period. This growth is primarily driven by the increasing demand for sophisticated testing equipment in various industries, rapid advancements in telecommunications technology, and the ongoing expansion of electronics manufacturing sectors worldwide.

One of the key growth factors contributing to the Pulse Pattern Generator market is the surging demand for advanced telecommunications infrastructure. With the rollout of 5G networks, there is a heightened need for precise and reliable testing equipment to ensure robust and efficient communication systems. Pulse pattern generators play a critical role in the development and testing of these systems, driving market growth. Additionally, the rise in data traffic and the need for high-speed data transfer capabilities further bolster the demand for these generators in the telecommunications sector.

Another significant growth driver is the increasing complexity of electronic devices and systems. The electronics manufacturing industry, including sectors such as consumer electronics, automotive electronics, and industrial electronics, requires advanced testing solutions to ensure product quality and reliability. Pulse pattern generators provide the necessary precision and functionality to test and verify the performance of various electronic components and systems, thereby fueling market growth. Furthermore, the miniaturization of electronic devices and the integration of sophisticated features necessitate more rigorous testing protocols, further stimulating demand.

The aerospace and defense sectors also contribute to the growth of the Pulse Pattern Generator market. These sectors demand highly reliable and precise testing equipment to ensure the safety and performance of critical systems and components. Pulse pattern generators are essential in testing communication systems, radar systems, and various other electronic systems used in aerospace and defense applications. The increasing investments in defense technologies and the development of advanced aerospace systems are expected to drive the demand for pulse pattern generators in these sectors.

Parity Generators and Checkers are crucial components in the realm of digital electronics, ensuring data integrity and error detection in communication systems. These devices are integral to maintaining the accuracy of data transmission, particularly in complex systems where data corruption can lead to significant issues. In the context of pulse pattern generators, parity generators and checkers play a vital role in validating the data sequences used for testing. By incorporating these components, engineers can ensure that the data patterns generated are free from errors, thereby enhancing the reliability of testing processes in telecommunications and other high-stakes industries.

From a regional perspective, North America holds a significant share of the global Pulse Pattern Generator market, driven by the presence of major technology companies, robust telecommunications infrastructure, and significant investments in research and development. Additionally, the Asia Pacific region is expected to witness substantial growth during the forecast period, attributed to the rapid expansion of the electronics manufacturing industry, increasing investments in telecommunications infrastructure, and growing adoption of advanced technologies in countries like China, Japan, and South Korea.

Type Analysis

The Pulse Pattern Generator market is segmented by type into portable and benchtop generators. Portable pulse pattern generators are witnessing increasing demand due to their flexibility, ease of use, and convenience in various applications. These portable devices are particularly useful in field testing, maintenance, and troubleshooting of telecommunication networks, where mobility and ease of transport are crucial. The growing trend towards miniaturization and portability in the electronics industry is further driving the demand for portable pulse pattern generators, making them an essential tool for engineers and technicians who require on-the-go testing solutions.

On the other hand, benchtop pulse pattern generators are characterized by their high precision, extensive functionality, a

The cell-free DNA (cfDNA) testing market is experiencing robust growth, driven by advancements in sequencing technologies, increasing adoption of non-invasive prenatal testing (NIPT), and the rising prevalence of cancer. The market, estimated at $5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $15 billion by 2033. This significant expansion is fueled by several factors. Firstly, the increasing demand for early cancer detection and personalized medicine is driving the development and adoption of cfDNA-based liquid biopsies. Secondly, the improved accuracy and reduced cost of next-generation sequencing (NGS) technologies are making cfDNA testing more accessible and affordable. Thirdly, the growing awareness among healthcare professionals and patients about the benefits of non-invasive diagnostic tools is contributing to market growth. Furthermore, the ongoing research and development efforts focused on improving the sensitivity and specificity of cfDNA tests are expected to further propel market expansion in the coming years. However, certain challenges remain. The high cost of cfDNA testing, particularly for advanced applications like early cancer detection, can limit its accessibility in certain regions. Also, the need for standardized testing protocols and regulatory approvals for various applications, along with the need for robust data analysis capabilities to interpret complex cfDNA data, pose hurdles to widespread adoption. Despite these challenges, the ongoing technological advancements, coupled with the increasing demand for early diagnosis and personalized treatment, are poised to overcome these barriers and fuel the continued growth of the cfDNA testing market throughout the forecast period. Key players such as Agilent Technologies, Illumina, and Roche are continuously investing in research and development, further solidifying the market's trajectory. The increasing number of strategic partnerships and collaborations among market players further indicates the promising future of this rapidly evolving field.

Test-Comp 2022

Test Suites

This file describes the contents of an archive of the 4th Competition on Software Testing (Test-Comp 2022).
https://test-comp.sosy-lab.org/2022/

The competition was run by Dirk Beyer, LMU Munich, Germany.
More information is available in the following article:
Dirk Beyer. Advances in Automatic Software Testing: Test-Comp 2022. In Proceedings of the 25th International Conference on Fundamental Approaches to Software Engineering (FASE 2022, Munich, April 2 - 7), 2021. Springer.

Copyright (C) Dirk Beyer
https://www.sosy-lab.org/people/beyer/

SPDX-License-Identifier: CC-BY-4.0
https://spdx.org/licenses/CC-BY-4.0.html

Contents

• LICENSE.txt: specifies the license
• README.txt: this file
• witnessFileByHash/: This directory contains test suites (witnesses for coverage). Each test witness in this directory is stored in a file whose name is the SHA2 256-bit hash of its contents followed by the filename extension .zip. The format of each test suite is described on the format web page: https://gitlab.com/sosy-lab/software/test-format A test suite contains also metadata in order to relate it to the test task for which it was produced.
• witnessInfoByHash/: This directory contains for each test suite (witness) in directory witnessFileByHash/ a record in JSON format (also using the SHA2 256-bit hash of the witness as filename, with .json as filename extension) that contains the meta data.
• witnessListByProgramHashJSON/: For convenient access to all test suites for a certain program, this directory represents a function that maps each program (via its SHA2256-bit hash) to a set of test suites (JSON records for test suites as described above) that the test-generation tools have produced for that program. For each program for which test suites exist, the directory contains a JSON file (using the SHA2 256-bit hash of the program as filename, with .json as filename extension) that contains all JSON records for test suites for that program.

A similar data structure was used by SV-COMP and is described in the following article:
Dirk Beyer. A Data Set of Program Invariants and Error Paths. In Proceedings of the 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR 2019, Montreal, Canada, May 26-27), pages 111-115, 2019. IEEE.
https://doi.org/10.1109/MSR.2019.00026

Other Archives

Overview over archives from Test-Comp 2022 that are available at Zenodo:

• https://doi.org/10.5281/zenodo.5831010 Test Suites from Test-Comp 2022 Test-Generation Tools. Witness store (containing the generated test suites)
• https://doi.org/10.5281/zenodo.5831012 Results of the 4th Intl. Competition on Software Testing (Test-Comp 2022). Results (XML result files, log files, file mappings, HTML tables)
• https://doi.org/10.5281/zenodo.5831003 SV-Benchmarks: Benchmark Set of SV-COMP 2022 and Test-Comp 2022. Test tasks, version testcomp22
• https://doi.org/10.5281/zenodo.5720267 BenchExec, version 3.10. Benchmarking framework

All benchmarks were executed for Test-Comp 2022 https://test-comp.sosy-lab.org/2022/
by Dirk Beyer, LMU Munich, based on the following components:

• https://gitlab.com/sosy-lab/test-comp/archives-2022 testcomp22 c211efbc
• https://gitlab.com/sosy-lab/benchmarking/sv-benchmarks testcomp22 ad265d07
• https://gitlab.com/sosy-lab/test-comp/bench-defs testcomp22 93f581b9
• https://gitlab.com/sosy-lab/software/benchexec 3.10 4e8716bd
• https://gitlab.com/sosy-lab/benchmarking/competition-scripts testcomp22 2022c698
• https://gitlab.com/sosy-lab/test-comp/test-format testcomp22 2895be41

Contact

Feel free to contact me in case of questions: https://www.sosy-lab.org/people/beyer/