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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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

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.

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/

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/

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

The Insider Threat Test Dataset is a collection of synthetic insider threat test datasets that provide both background and malicious actor synthetic data.The CERT Division, in partnership with ExactData, LLC, and under sponsorship from DARPA I2O, generated a collection of synthetic insider threat test datasets. These datasets provide both synthetic background data and data from synthetic malicious actors.For more background on this data, please see the paper, Bridging the Gap: A Pragmatic Approach to Generating Insider Threat Data.Datasets are organized according to the data generator release that created them. Most releases include multiple datasets (e.g., r3.1 and r3.2). Generally, later releases include a superset of the data generation functionality of earlier releases. Each dataset file contains a readme file that provides detailed notes about the features of that release.The answer key file answers.tar.bz2 contains the details of the malicious activity included in each dataset, including descriptions of the scenarios enacted and the identifiers of the synthetic users involved.

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 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/

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...

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

This archive contains the results of 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

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/2020/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 coverage-validation runs
results-verified/ results of test-generation runs and aggregated results

The folder results-validated/ contains the results from coverage-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

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 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/

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/

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/

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?

Request Free Sample

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

Snapshot img

Textile Testing Equipment Market Size 2025-2029

The textile testing equipment market size is forecast to increase by USD 390.6 million, at a CAGR of 4.6% between 2024 and 2029.

The market is witnessing significant growth, driven by the increasing demand for technical textiles and the shift towards the use of sustainable fibers. The adoption of technical textiles is on the rise due to their superior properties, including durability, strength, and resistance to various environmental conditions. Additionally, with increasing urbanization and rising middle-class income, consumer demand for apparel and fashion is also on rise. Operational issues faced by Original Equipment Manufacturers (OEMs) pose a significant hurdle. These challenges include complex testing procedures, high capital investment for advanced testing equipment, and the need for specialized expertise.
OEMs must navigate these challenges to ensure the production of high-quality textile products that meet regulatory requirements and customer expectations. To remain competitive, companies must invest in innovative testing solutions that streamline processes, reduce costs, and improve efficiency. Sustainable fibers, such as organic cotton, linen, and hemp, offer environmental benefits and appeal to consumers who prioritize eco-friendly products. Companies can capitalize on the growing demand for technical textiles and sustainable fibers, positioning themselves for long-term success in the market.

What will be the Size of the Textile Testing Equipment 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.
Request Free Sample

The market encompasses a range of technologies and tools designed to ensure product quality, optimize processes, and comply with regulatory standards in the textile industry. Key areas of focus include textile chemistry, material science, and fiber technology, which are integral to textile design and engineering. Report generation software and quality control systems facilitate efficient data management and analysis. Abrasion and bursting strength testers assess fabric durability, while tensile testers measure fiber strength and elasticity. Fiber identification systems enable accurate material analysis, and image analysis software aids in textile research and development. Bio-based fibers and smart textiles are driving innovation in the sector, necessitating advanced testing solutions.
Material testing labs employ flammability chambers and data logging systems to ensure safety and compliance. Waste reduction and circular economy principles are influencing market trends, with digital transformation playing a crucial role in streamlining processes and enhancing efficiency. Textile certification and the development of wearable technology further expand the market's scope, as industry players strive to meet evolving consumer demands and regulatory requirements. Textile physics and research continue to advance, fueling ongoing innovation and growth in the sector.

How is this Textile Testing Equipment Industry segmented?

The textile testing 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.

End-user

  Textile companies
  Professional testing agencies


Application

  Apparel industry
  Footwear industry
  Others


Technology

  Manual
  Semi-automated equipment
  Fully automated and smart


Geography

  North America

    US


  Europe

    France
    Germany
    Italy
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By End-user Insights

The textile companies segment is estimated to witness significant growth during the forecast period. Textile testing equipment plays a crucial role in ensuring the quality and performance of various textile products. In the dynamic textile industry, textile companies are driving the growth of the market. The increasing demand for textiles in numerous sectors, such as automotive, medical, home furnishings, and apparel, necessitates rigorous testing to meet the required standards. Textile testing laboratories employ advanced technologies, including digital imaging, software analysis, and automated testing, to evaluate fabric properties like pilling resistance, bursting strength, moisture management, and color fastness. Eco-friendly materials and recycled textiles are gaining popularity, necessitating testing for recovery rate, fabric weight, and other properties. Moreover, the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies is enabling the development of better testing equipments.

ISO and ASTM standards guid

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/

The TidGen Power System generates emission-free electricity from tidal currents and connects directly into existing grids using smart grid technology. The power system consists of three major subsystems: shore-side power electronics, mooring system, and turbine generator unit (TGU) device. This submission includes the test report on the characterization program composite testing and the selected composite structure. ORPC arranged coupon testing of candidate material sets as part of a larger characterization program. The goal of this testing was to down select the candidate material sets and determine failure mechanisms. This was done by testing both dry and saturated material sets and examining the effects of moisture uptake of the coupons mechanical properties. Due to the limitations of this program we were limited to static tensile testing is longitudinal and transverse directions as well as limited tensile fatigue testing with a loading of R=0.1 (tension - tension). This program did however, allow for a larger spread of material sets including a novel hydrophobic resin that was promoted to resist water uptake, optimized for subsea applications. Also included is a technical report on the characterization program, including composite test data, design FMEA for composite structure, material selection, composite design, PFMEA for the composite production process, reliability models, production process control plan and development plan. Materials for Marine Hydrokinetic (MHK) devices need to be evaluated before being utilized on a device with a service life of 20 years. For this reason, and the fact that ORPCs turbines are a complex manufacturing challenge, a composite optimization program is conducted. This program looked at novel material sets, production processes and developed tools to evaluate manufacturing defects and characterize their effect on structural performance over an extended operating time. This report will cover the work done during Budget Period 1 for Task 2 of the Advanced TidGen Power System Project.

Snapshot img

Non-Destructive Testing (NDT) Equipment Market Size 2025-2029

The non-destructive testing (NDT) equipment market size is forecast to increase by USD 950.5 million at a CAGR of 6% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the rise in oil and gas and power generation projects. This sector's expansion creates a substantial demand for advanced NDT technologies, particularly ultrasonic testing equipment. Another key trend shaping the market is the increasing availability of pre-used NDT equipment. In the power generation sector, there is a growing emphasis on safety and reliability, leading to increased adoption of NDT technologies for asset management and maintenance. This trend offers cost-effective solutions for companies seeking to invest in NDT technology without the high upfront costs associated with new equipment. However, the market faces challenges as well. One significant obstacle is the stringent regulations governing the use of NDT equipment in various industries.
Additionally, the market's competitive landscape is becoming increasingly crowded, with numerous players vying for market share. Companies must differentiate themselves through innovation, quality, and customer service to remain competitive. To capitalize on the market's opportunities and navigate these challenges effectively, companies should focus on continuous improvement, regulatory compliance, and customer satisfaction. Ensuring compliance with these regulations can be time-consuming and costly, requiring substantial resources and expertise. This trend is further fueled by the rise in crude oil prices, which is expected to drive investments in conventional oil and gas basins.

What will be the Size of the Non-Destructive Testing (NDT) Equipment 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.
Request Free Sample

The market encompasses a range of solutions designed to ensure compliance assurance and risk mitigation in various industries. Consulting services play a crucial role in optimizing NDT processes, while software upgrades and automated NDT systems facilitate efficiency improvements and cost reduction. Global markets demand supply chain management solutions to streamline the procurement of NDT probes, engineering services, equipment rentals, and maintenance contracts. The market is experiencing significant growth, driven primarily by the increasing number of oil and gas and power generation projects worldwide.
Industry regulations mandate stringent safety standards, necessitating continuous process optimization and repair services. Overall, the NDT market is dynamic, with a focus on innovation and cost savings. Handheld and portable NDT equipment enable inspection personnel to conduct tests in remote locations, reducing downtime. Safety procedures and material science advancements drive the adoption of emerging technologies, such as phased array ultrasonic testing and eddy current testing.

How is this Non-Destructive Testing (NDT) Equipment Industry segmented?

The non-destructive testing (NDT) 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.

End-user

  Oil and gas
  Power generation
  Aerospace and defense
  Automotive and transportation
  Others


Product Type

  Ultrasonic testing equipment
  Radiography testing equipment
  Visual testing equipment
  Others


Product

  Portable equipment
  Stationary equipment
  Software solutions


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The oil and gas segment is estimated to witness significant growth during the forecast period. Non-Destructive Testing (NDT) equipment plays a crucial role in ensuring the integrity and safety of various industries, particularly in energy applications such as oil and gas. In this sector, pipelines are a vital means of transporting oil and gas, making NDT essential for detecting defects, including corrosion and cracks. These issues can lead to severe consequences if left unchecked, such as pipeline leaks or ruptures. NDT techniques, including digital radiography, magnetic particle testing, and ultrasonic testing, are employed to identify and classify defects. Data analysis software and machine learning algorithms enable real-time radiography and predictive modeling, enhancing the efficiency and accuracy of inspections.

NDT software, including report generation software and data visualization tools, streamline the

Snapshot img

OSAT Market Size 2025-2029

The osat market size is forecast to increase by USD 28.47 billion, at a CAGR of 9.4% between 2024 and 2029.

The market is experiencing significant growth, driven by advancements in chip packaging technology. This innovation enables the production of smaller, more efficient semiconductors, meeting the increasing demand for compact and powerful electronic devices. Furthermore, the market is witnessing an escalating trend of strategic partnerships and acquisitions among key players. These collaborations aim to expand market reach, enhance technological capabilities, and strengthen competitive positions. However, the OSAT industry faces a formidable challenge in the form of a semiconductor shortage. This scarcity poses a significant threat to the industry's growth, as it hampers the production capacity of OSATs and, consequently, the manufacturing of electronic devices that rely on these components. Companies must navigate this challenge by exploring alternative semiconductor sources, investing in research and development, and fostering strong relationships with semiconductor manufacturers. By capitalizing on the opportunities presented by technological advancements and strategic collaborations while addressing the semiconductor shortage, OSATs can effectively drive growth and maintain a competitive edge in the market.

What will be the Size of the OSAT 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.
Request Free SampleThe OSAT (Outsourced Semiconductor Assembly and Testing) market is characterized by continuous evolution and dynamic market activities. Seamlessly integrated processes such as test program development, semiconductor testing, system-level test, humidity testing, test escape rate reduction, time to market optimization, reliability testing, quality assurance, test coverage, report generation, parametric testing, data processing, and mixed-signal testing are integral to the OSAT landscape. The supply chain's intricacies necessitate ongoing process optimization, with entities focusing on yield improvement through defect density reduction, failure analysis, test engineering, and test equipment innovation. Environmental testing, including thermal, vibration, shock, and RF testing, plays a crucial role in ensuring product reliability. Test time reduction and cost reduction are essential priorities, with test data management and equipment qualification crucial for efficient test program execution. The integration of advanced technologies like boundary scan, data acquisition, and root cause analysis further enhances the market's capabilities. The OSAT industry's continuous advancements cater to various sectors, including automotive, telecommunications, consumer electronics, and industrial applications, among others. The focus on cost reduction, product quality, and time to market ensures that OSAT providers remain at the forefront of technological innovation.

How is this OSAT Industry segmented?

The osat 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. ServiceAssembly and packagingTestingEnd-userTelecommunicationConsumer electronicsIndustrial electronicsAutomotiveOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKAPACChinaJapanSouth KoreaTaiwanSouth AmericaBrazilRest of World (ROW)

By Service Insights

The assembly and packaging segment is estimated to witness significant growth during the forecast period.The semiconductor industry relies heavily on OSAT (Outsourced Semiconductor Assembly and Testing) providers for critical production tasks, such as functional safety testing, logic testing, vibration testing, quality control, and supply chain management. These services are essential for the final stages of semiconductor production, including thermal testing, OSAT testing, IC testing, yield improvement, failure analysis, test engineering, test equipment maintenance, and defect density reduction. OSAT companies invest significantly in research and development to stay competitive in the market. The industry's dynamic nature demands continuous process optimization, leading to advancements in test time reduction, environmental testing, memory testing, yield enhancement, package testing, in-circuit testing, functional testing, product reliability, RF testing, test data management, optical testing, shock testing, test program development, semiconductor testing, system-level testing, humidity testing, test escape rate minimization, time to market acceleration, reliability testing, quality assurance, test coverage, report generation, parametric testing, data processing, mixed-signal testing, equipment qualific