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

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Australia Software Testing Services Market Size 2025-2029

The Australia software testing services market size is forecast to increase by USD 1.7 billion, at a CAGR of 12.3% between 2024 and 2029.

The Software Testing Services Market in Australia is driven by the increasing need for cost reduction and faster time-to-market in the software development industry. This demand is fueled by the competitive business landscape, where companies strive to release high-quality software quickly to gain a competitive edge. Another significant trend in the market is the evolution of software testing labs, which offer specialized testing services and advanced testing tools to ensure software functionality, reliability, and security. However, the market also faces challenges, such as the availability of open-source and free testing tools that can potentially reduce the demand for paid testing services. Predictive analytics and test results analysis are driving test strategy development, enabling proactive identification and resolution of issues.
Additionally, the increasing complexity of software applications and the need for continuous testing pose significant challenges for testing service providers. Companies must adapt to these trends and challenges by offering value-added services, leveraging advanced testing tools, and focusing on providing expert testing capabilities to differentiate themselves in the market. By doing so, they can capitalize on the growing demand for software testing services and effectively navigate the competitive landscape.

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

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The software testing services market in Australia encompasses various offerings, including software testing consulting, test automation expertise, and quality assurance audits. Certifications in software testing methodologies and test automation frameworks are increasingly valued, as businesses prioritize quality assurance metrics and adherence to software quality standards. Mobile app testing and security vulnerability scanning are crucial components of modern testing practices, with test execution management and test reporting tools streamlining processes. Quality assurance professionals employ test planning, test design techniques, and test case management to ensure comprehensive coverage.
Performance monitoring, cloud-based testing, and test data generation are essential for maintaining optimal software functionality. AI-powered testing and test automation platforms are transforming the industry, offering advanced capabilities in test automation frameworks and test environment provisioning. Defect tracking systems facilitate efficient issue resolution, while test results analysis and quality assurance audits ensure continuous improvement.

How is this market segmented?

The market 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.

Product

  Application testing
  Product testing


End-user

  BFSI
  Telecom and media
  Manufacturing
  Retail
  Others


Deployment

  Cloud-based
  On-premises


Service Type

  Manual testing
  Automated testing
  Performance testing
  Security testing


Geography

  APAC

    Australia

By Product Insights

The application testing segment is estimated to witness significant growth during the forecast period. Application testing is an essential process in ensuring the functionality, consistency, and usability of software applications. Three primary types of applications – desktop, mobile, and web – require testing for various reasons. Web applications undergo testing for business logic, application integrity, functionality, data flow, and hardware and software compatibility. Performance, security, and load testing are crucial for web applications, along with cross-browser testing, beta testing, compatibility testing, exploratory testing, regression testing, multilanguage support testing, and stress testing. Mobile application software testing includes UI testing, security testing, functionality and compatibility testing, and regression testing. Three testing methodologies – black box, white box, and grey box – are used to test applications.

Black box testing focuses on the application's external behavior, while white box testing examines the internal structure and workings. Grey box testing combines elements of both, providing a more comprehensive testing approach. Moreover, the software development lifecycle integrates various testing types, including load testing, integration testing, test analysis, test management tools, bug tracking, test data management, test reporting, usability testing, automation testing, test automation frameworks, u

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/

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-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/

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/

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/

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/

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.

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/

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

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/

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 2023

Test Suites

This file describes the contents of an archive of the 5th Competition on Software Testing (Test-Comp 2023). https://test-comp.sosy-lab.org/2023/

The competition was organized by Dirk Beyer, LMU Munich, Germany. More information is available in the following article: Dirk Beyer. Software Testing: 5th Comparative Evaluation: Test-Comp 2023. In Proceedings of the 26th International Conference on Fundamental Approaches to Software Engineering (FASE 2023, Paris, April 22 - 27), 2023. 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 of archives from Test-Comp 2023 that are available at Zenodo:

• https://doi.org/10.5281/zenodo.7701126 Test Suites from Test-Comp 2023 Test-Generation Tools. Store of coverage witnesses (containing the generated test suites)
• https://doi.org/10.5281/zenodo.7701118 Test-Suite Generators from Test-Comp 2023. Executable tools that were evaluated in the competition
• https://doi.org/10.5281/zenodo.7701122 Results of the 5th Intl. Competition on Software Testing (Test-Comp 2023). Results (XML result files, log files, file mappings, HTML tables)
• https://doi.org/10.5281/zenodo.7627783 SV-Benchmarks: Benchmark Set of SV-COMP 2023 and Test-Comp 2023. Verification tasks, version testcomp23
• https://doi.org/10.5281/zenodo.7612021 BenchExec, version 3.16. Benchmarking framework
• https://doi.org/10.5281/zenodo.7635975 CoVeriTeam, version 1.0. Remote execution and continuous integration of testers

All benchmarks were executed for Test-Comp 2023 https://test-comp.sosy-lab.org/2023/ by Dirk Beyer, LMU Munich, based on the following components:

• https://gitlab.com/sosy-lab/test-comp/archives-2023 testcomp23 bd9d84151c52ba7ad549212a590e2b3a18c071bc
• https://gitlab.com/sosy-lab/benchmarking/sv-benchmarks testcomp23 f87854c275a177e8dc3673fe6959cd89038652af
• https://gitlab.com/sosy-lab/test-comp/bench-defs testcomp23 21a5ba27dc84c8e59fefe18369b5723550c96786
• https://gitlab.com/sosy-lab/software/benchexec 3.16 b43e7384112770a41b571399135065daa7f387ab
• https://gitlab.com/sosy-lab/benchmarking/competition-scripts testcomp23 e6f84b5a79626b4295108a0f96ea370edde1f023
• https://gitlab.com/sosy-lab/test-comp/test-format testcomp23 e6b0eff82a5132a6e4e4b52f8fbfaa3553af3ebb
• https://gitlab.com/sosy-lab/software/coveriteam 1.0 78d9f9f9d5fefc4fdd934d1e0f43ddba4ec51711

Contact

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

AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites Overview

Unlock the next generation of agentic commerce and automated shopping experiences with this comprehensive dataset of meticulously annotated checkout flows, sourced directly from leading retail, restaurant, and marketplace websites. Designed for developers, researchers, and AI labs building large language models (LLMs) and agentic systems capable of online purchasing, this dataset captures the real-world complexity of digital transactions—from cart initiation to final payment.

Key Features

Breadth of Coverage: Over 10,000 unique checkout journeys across hundreds of top e-commerce, food delivery, and service platforms, including but not limited to Walmart, Target, Kroger, Whole Foods, Uber Eats, Instacart, Shopify-powered sites, and more.

Actionable Annotation: Every flow is broken down into granular, step-by-step actions, complete with timestamped events, UI context, form field details, validation logic, and response feedback. Each step includes:

Page state (URL, DOM snapshot, and metadata)

User actions (clicks, taps, text input, dropdown selection, checkbox/radio interactions)

System responses (AJAX calls, error/success messages, cart/price updates)

Authentication and account linking steps where applicable

Payment entry (card, wallet, alternative methods)

Order review and confirmation

Multi-Vertical, Real-World Data: Flows sourced from a wide variety of verticals and real consumer environments, not just demo stores or test accounts. Includes complex cases such as multi-item carts, promo codes, loyalty integration, and split payments.

Structured for Machine Learning: Delivered in standard formats (JSONL, CSV, or your preferred schema), with every event mapped to action types, page features, and expected outcomes. Optional HAR files and raw network request logs provide an extra layer of technical fidelity for action modeling and RLHF pipelines.

Rich Context for LLMs and Agents: Every annotation includes both human-readable and model-consumable descriptions:

“What the user did” (natural language)

“What the system did in response”

“What a successful action should look like”

Error/edge case coverage (invalid forms, OOS, address/payment errors)

Privacy-Safe & Compliant: All flows are depersonalized and scrubbed of PII. Sensitive fields (like credit card numbers, user addresses, and login credentials) are replaced with realistic but synthetic data, ensuring compliance with privacy regulations.

Each flow tracks the user journey from cart to payment to confirmation, including:

Adding/removing items

Applying coupons or promo codes

Selecting shipping/delivery options

Account creation, login, or guest checkout

Inputting payment details (card, wallet, Buy Now Pay Later)

Handling validation errors or OOS scenarios

Order review and final placement

Confirmation page capture (including order summary details)

Why This Dataset?

Building LLMs, agentic shopping bots, or e-commerce automation tools demands more than just page screenshots or API logs. You need deeply contextualized, action-oriented data that reflects how real users interact with the complex, ever-changing UIs of digital commerce. Our dataset uniquely captures:

The full intent-action-outcome loop

Dynamic UI changes, modals, validation, and error handling

Nuances of cart modification, bundle pricing, delivery constraints, and multi-vendor checkouts

Mobile vs. desktop variations

Diverse merchant tech stacks (custom, Shopify, Magento, BigCommerce, native apps, etc.)

Use Cases

LLM Fine-Tuning: Teach models to reason through step-by-step transaction flows, infer next-best-actions, and generate robust, context-sensitive prompts for real-world ordering.

Agentic Shopping Bots: Train agents to navigate web/mobile checkouts autonomously, handle edge cases, and complete real purchases on behalf of users.

Action Model & RLHF Training: Provide reinforcement learning pipelines with ground truth “what happens if I do X?” data across hundreds of real merchants.

UI/UX Research & Synthetic User Studies: Identify friction points, bottlenecks, and drop-offs in modern checkout design by replaying flows and testing interventions.

Automated QA & Regression Testing: Use realistic flows as test cases for new features or third-party integrations.

What’s Included

10,000+ annotated checkout flows (retail, restaurant, marketplace)

Step-by-step event logs with metadata, DOM, and network context

Natural language explanations for each step and transition

All flows are depersonalized and privacy-compliant

Example scripts for ingesting, parsing, and analyzing the dataset

Flexible licensing for research or commercial use

Sample Categories Covered

Grocery delivery (Instacart, Walmart, Kroger, Target, etc.)

Restaurant takeout/delivery (Ub...

Test-Comp 2022

Competition Results

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

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/2022/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

• : 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 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/

This meta data file contains original data of the dicom_id, subject_id, study_id, and original view position, and adds data on image quality (TRUE/FALSE), corrected view (FRONTAL/LATERAL), identified sex differences (TRUE/FALSE), view agreement of original and new labels (TRUE/FALSE), the test set IDs for the view correction, quality assessment and cardiomegaly classification, and borderline cardiomegaly instances (TRUE/FALSE). (CSV)

Data quality assessment of the MIMIC-CXR dataset (65,379 patients, 227,827 individual reports, 377,100 images). Indication of mismatched sex mentions in reports attributed to the same individual, number (%) of poor quality images indicated by our poor quality image classification model, and number (%) of wrongly labelled views (in the metadata) indicated by our view classification model. All reports and images indicated above were manually checked, and we provide a spreadsheet in S1 Data with the corrected view labels and reports likely from different individuals due to sex differences with other reports attributed to the same person identifier.

Snapshot img

APAC Genetic Testing Market Size 2024-2028

The APAC genetic testing market size is forecast to increase by USD 2.49 billion, at a CAGR of 17.8% between 2023 and 2028.

In the APAC genetic testing market, the approval of advanced genetic testing products is gaining momentum, driven by the increasing awareness and acceptance of personalized healthcare. This trend is further fueled by the rapid advancements in next-generation sequencing technology, enabling faster and more accurate genetic testing. Bioinformatics tools and digital health platforms are essential components of the market, facilitating data analysis and interpretation and enabling easy access to electronic health records. However, the market landscape is complex, with varying regulations on genetic testing and research across different countries posing significant challenges. Navigating these regulatory hurdles requires a deep understanding of local laws and regulations, making it essential for companies to establish strong partnerships with local regulatory bodies and experts.
Effective collaboration and compliance with these regulations will be crucial for market success, as companies seek to capitalize on the significant growth opportunities in the APAC genetic testing market.

What will be the size of the APAC Genetic Testing Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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In the Asian Pacific (APAC) market, genetic testing has emerged as a critical tool for disease diagnosis, with molecular diagnostics and gene expression profiling gaining traction. Ethical guidelines are strictly adhered to ensure patient privacy and informed consent. Single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) serve as essential genetic markers for diagnosing various diseases. Regulatory compliance, data security, and data integrity are top priorities, with laboratories implementing robust data management systems and automation technology to enhance throughput capacity and improve diagnostic accuracy. Result reporting is streamlined, ensuring test specificity and sensitivity meet clinical utility standards. Negative predictive value is equally important, with chromosome analysis and protein biomarkers playing significant roles.
Quality control measures are implemented rigorously to maintain diagnostic accuracy, while sample tracking and test result reporting ensure efficient laboratory workflow. Patient consent is obtained through standardized procedures, and data management systems ensure data security and privacy. Regulatory bodies continue to emphasize regulatory compliance, and ethical guidelines are being updated to address emerging trends in genetic testing.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Cancer diagnosis
  Genetic disease diagnosis
  Cardiovascular disease diagnosis
  Others


Product

  Equipment
  Consumables


Geography

  APAC

    China
    India
    Japan
    South Korea

By Application Insights

The cancer diagnosis segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth due to the increasing demand for early disease diagnosis and personalized medicine. Non-invasive prenatal testing and prenatal diagnosis are key areas of focus, with advancements in sample preparation techniques, such as DNA extraction methods and mutation screening, driving innovation. Quality assurance and laboratory accreditation are crucial for ensuring accuracy and reliability, while ethical considerations and data privacy remain essential concerns. Advancements in genome sequencing technologies, including whole genome sequencing and next-generation sequencing, have led to the development of predictive testing and carrier screening. Digital PCR and quantitative PCR are also gaining popularity for their accuracy and efficiency.

Cytogenetic analysis and genetic variant detection are other important techniques used in genetic testing. Personalized medicine and disease risk prediction are major trends, with companies investing in bioinformatics pipelines and data interpretation to provide accurate and actionable results. Regulatory frameworks and genetic testing regulation are evolving to ensure standardization and quality. The market is witnessing a surge in the development of advanced genetic testing devices, including polymerase chain reaction, SNP genotyping, and DNA microarray. Newborn screening and STR profiling are also important applications. In conclusion, the market is dynamic and evolving, driven by advancemen

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