Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high dimensional datasets, which commonly have a small number of samples because of the intrinsic high cost of data collection involving human participants. High dimensional data with a small number of samples is of critical importance for identifying biomarkers and conducting feasibility and pilot work, however it can lead to biased machine learning (ML) performance estimates. Our review of studies which have applied ML to predict autistic from non-autistic individuals showed that small sample size is associated with higher reported classification accuracy. Thus, we have investigated whether this bias could be caused by the use of validation methods which do not sufficiently control overfitting. Our simulations show that K-fold Cross-Validation (CV) produces strongly biased performance estimates with small sample sizes, and the bias is still evident with sample size of 1000. Nested CV and train/test split approaches produce robust and unbiased performance estimates regardless of sample size. We also show that feature selection if performed on pooled training and testing data is contributing to bias considerably more than parameter tuning. In addition, the contribution to bias by data dimensionality, hyper-parameter space and number of CV folds was explored, and validation methods were compared with discriminable data. The results suggest how to design robust testing methodologies when working with small datasets and how to interpret the results of other studies based on what validation method was used.

Context

Since i have started research in the field of data science, i have noticed there are lot of data sets available for NLP, medicine, images and other subjects but i could not find any single adequate data for the domain of software testing. The data sets which are hardly available are extracted from some piece of code or some historical data that too not available publicly to analyze. The domain of software testing and data science, especially machine learning has a lot of potential. While conducting research on testcase prioritization especially in initial stages of software test cycle the way companies set the priorities in software industry there is no black box data set available in that format. This was the reason that i wanted such data set to exist. So i collected the necessary attributes , arrange them against their values and make one.

Content

This data was gathered in [Aug, 2020], from a software company worked on a car financing lease company's whole software package from web to their management system. The dataset is in .csv format, there are 2000 rows and 6 columns in this data set. The detail of six attributes are as under: B_Req --> Business Requirement R_Prioirty --> Requirement Priority of particular business requirement FP --> Function point of each testing task, which in our case are test cases against each requirement under covers a particular FP Complexity --> Complexity of a particular function point or related modules(the description of assigning complexity is listed below in this section)* Time --> Estimated max time assigned to each Function Point of particular testing task by QA team lead or sr. SQA analyst Cost --> Calculated cost for each function point using complexity and time with function point estimation technique to calculates cost using the formula listed below: cost = “Cost = (Complexity * Time) * average amount set per task or per Function Point note: In this case it is set as 5$ per FP. The criteria for complexity is listed in .txt file attached with new version.

Acknowledgements

I would like to thank the persons from QA departments of different software companies. Especially team of the the company who provided me this estimation data and traceability matrix to extract data and compile these in to a dataset. I get a great help from the websites like www.softwaretestinghelp.com, www.coderus.com and many other sources which helps me to understand all the testing process and in which phases priorities are assigned usually.

Inspiration

My inspiration to collect this data is the shortage of dataset showing the priority of testcases with their requirements and estimated metrics to analyze the data while doing research in automation of testcase priority using machine learning. --> The dataset can be used to analyze and apply classification or any machine learning algorithm to prioritize testcases. --> Can be used reduce , select or automate testing based on priority, or cost and time or complexity and requirements. --> Can be used to build recommendation system problem related to software testing which helps software testing team to ease their task based estimation and recommendation.

Test Data Management Market Outlook

According to our latest research, the global Test Data Management market size in 2024 is valued at USD 1.52 billion, reflecting the rapid adoption of data-driven testing methodologies across industries. The market is expected to register a robust CAGR of 12.4% from 2025 to 2033, reaching a projected value of USD 4.33 billion by 2033. This strong growth trajectory is primarily driven by the increasing demand for high-quality software releases, stringent regulatory compliance requirements, and the growing complexity of enterprise IT environments.

The expansion of the Test Data Management market is propelled by the exponential growth in data volumes and the critical need for efficient, secure, and compliant testing environments. As organizations accelerate their digital transformation initiatives, the reliance on accurate and representative test data has become paramount. Enterprises are increasingly adopting test data management solutions to reduce the risk of data breaches, ensure data privacy, and enhance the reliability of software applications. The proliferation of agile and DevOps methodologies further underscores the need for automated and scalable test data management tools, enabling faster and more reliable software delivery cycles.

Another significant growth factor is the rising stringency of data protection regulations such as GDPR, CCPA, and HIPAA, which mandate robust data masking and subsetting practices during software testing. Organizations in highly regulated sectors such as BFSI and healthcare are prioritizing test data management solutions to safeguard sensitive information while maintaining compliance. Moreover, the increasing adoption of cloud-based applications and the integration of artificial intelligence and machine learning in test data management processes are enhancing efficiency, scalability, and accuracy, thereby fueling market growth.

The shift towards cloud-native architectures and the growing emphasis on cost optimization are also accelerating the adoption of test data management solutions. Cloud-based test data management offers organizations the flexibility to scale resources as needed, reduce infrastructure costs, and streamline data provisioning processes. Additionally, the need to support continuous integration and continuous delivery (CI/CD) pipelines is driving demand for advanced test data management capabilities, including automated data generation, profiling, and masking. As a result, vendors are innovating to deliver solutions that cater to the evolving needs of modern enterprises, further boosting market expansion.

Regionally, North America dominates the Test Data Management market, accounting for a significant share in 2024, driven by the presence of major technology companies, high regulatory awareness, and early adoption of advanced testing practices. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by rapid digitalization, increasing IT investments, and the emergence of new regulatory frameworks. Europe continues to be a strong market, supported by strict data privacy laws and a mature IT landscape. Latin America and the Middle East & Africa are also experiencing steady growth as enterprises in these regions increasingly recognize the importance of effective test data management.

Component Analysis

The Test Data Management market by component is segmented into software and services, each playing a pivotal role in shaping the overall market landscape. Software solutions form the backbone of test data management by providing functionalities such as data subsetting, masking, profiling, and generation. These tools are increasingly equipped with automation, artificial intelligence, and machine learning capabilities to enhance the accuracy and efficiency of test data provisioning. The growing complexity of enterprise applications and the need for rapid software releases have led to a surge in demand for comprehensive test d

This is a test collection for passage and document retrieval, produced in the TREC 2023 Deep Learning track. The Deep Learning Track studies information retrieval in a large training data regime. This is the case where the number of training queries with at least one positive label is at least in the tens of thousands, if not hundreds of thousands or more. This corresponds to real-world scenarios such as training based on click logs and training based on labels from shallow pools (such as the pooling in the TREC Million Query Track or the evaluation of search engines based on early precision).Certain machine learning based methods, such as methods based on deep learning are known to require very large datasets for training. Lack of such large scale datasets has been a limitation for developing such methods for common information retrieval tasks, such as document ranking. The Deep Learning Track organized in the previous years aimed at providing large scale datasets to TREC, and create a focused research effort with a rigorous blind evaluation of ranker for the passage ranking and document ranking tasks.Similar to the previous years, one of the main goals of the track in 2022 is to study what methods work best when a large amount of training data is available. For example, do the same methods that work on small data also work on large data? How much do methods improve when given more training data? What external data and models can be brought in to bear in this scenario, and how useful is it to combine full supervision with other forms of supervision?The collection contains 12 million web pages, 138 million passages from those web pages, search queries, and relevance judgments for the queries.

Dataset used in the article entitled 'Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools'. These datasets can be used to test several characteristics in machine learning and data processing algorithms.

Quality control and system suitability testing are vital protocols implemented to ensure the repeatability and reproducibility of data in mass spectrometry investigations. However, mass spectrometry imaging (MSI) analyses present added complexity since both chemical and spatial information are measured. Herein, we employ various machine learning algorithms and a novel quality control mixture to classify the working conditions of an MSI platform. Each algorithm was evaluated in terms of its performance on unseen data, validated with negative control data sets to rule out confounding variables or chance agreement, and utilized to determine the necessary sample size to achieve a high level of accurate classifications. In this work, a robust machine learning workflow was established where models could accurately classify the instrument condition as clean or compromised based on data metrics extracted from the analyzed quality control sample. This work highlights the power of machine learning to recognize complex patterns in MSI data and use those relationships to perform a system suitability test for MSI platforms.

Sure! I'd be happy to provide you with an introduction to machine learning basics for beginners. Machine learning is a subfield of artificial intelligence (AI) that focuses on enabling computers to learn and make predictions or decisions without being explicitly programmed. Here are some key concepts and terms to help you get started:

1. Supervised Learning: In supervised learning, the machine learning algorithm learns from labeled training data. The training data consists of input examples and their corresponding correct output or target values. The algorithm learns to generalize from this data and make predictions or classify new, unseen examples.

2. Unsupervised Learning: Unsupervised learning involves learning patterns and relationships from unlabeled data. Unlike supervised learning, there are no target values provided. Instead, the algorithm aims to discover inherent structures or clusters in the data.

3. Training Data and Test Data: Machine learning models require a dataset to learn from. The dataset is typically split into two parts: the training data and the test data. The model learns from the training data, and the test data is used to evaluate its performance and generalization ability.

4. Features and Labels: In supervised learning, the input examples are often represented by features or attributes. For example, in a spam email classification task, features might include the presence of certain keywords or the length of the email. The corresponding output or target values are called labels, indicating the class or category to which the example belongs (e.g., spam or not spam).

5. Model Evaluation Metrics: To assess the performance of a machine learning model, various evaluation metrics are used. Common metrics include accuracy (the proportion of correctly predicted examples), precision (the proportion of true positives among all positive predictions), recall (the proportion of true positives predicted correctly), and F1 score (a combination of precision and recall).

6. Overfitting and Underfitting: Overfitting occurs when a model becomes too complex and learns to memorize the training data instead of generalizing well to unseen examples. On the other hand, underfitting happens when a model is too simple and fails to capture the underlying patterns in the data. Balancing the complexity of the model is crucial to achieve good generalization.

7. Feature Engineering: Feature engineering involves selecting or creating relevant features that can help improve the performance of a machine learning model. It often requires domain knowledge and creativity to transform raw data into a suitable representation that captures the important information.

8. Bias and Variance Trade-off: The bias-variance trade-off is a fundamental concept in machine learning. Bias refers to the errors introduced by the model's assumptions and simplifications, while variance refers to the model's sensitivity to small fluctuations in the training data. Reducing bias may increase variance and vice versa. Finding the right balance is important for building a well-performing model.

9. Supervised Learning Algorithms: There are various supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks. Each algorithm has its own strengths, weaknesses, and specific use cases.

10. Unsupervised Learning Algorithms: Unsupervised learning algorithms include clustering algorithms like k-means clustering and hierarchical clustering, dimensionality reduction techniques like principal component analysis (PCA) and t-SNE, and anomaly detection algorithms, among others.

These concepts provide a starting point for understanding the basics of machine learning. As you delve deeper, you can explore more advanced topics such as deep learning, reinforcement learning, and natural language processing. Remember to practice hands-on with real-world datasets to gain practical experience and further refine your skills.

Market Overview: The global Test Data Management market is projected to reach USD 1632.3 million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The rising demand for efficient and reliable data management practices, increasing adoption of cloud-based solutions, and the need to ensure data quality for testing purposes are the key growth drivers. Key Trends and Restraints: The shift towards cloud computing is a significant trend, as it enables organizations to streamline test data management processes and reduce infrastructure costs. Additionally, the adoption of artificial intelligence (AI) and machine learning (ML) technologies is enhancing automation capabilities, further boosting market growth. However, concerns over data privacy and security, as well as the high cost of implementation and maintenance, are potential restraints that could hinder the market's progress.

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Test Data Management Market Size 2025-2029

The test data management market size is forecast to increase by USD 727.3 million, at a CAGR of 10.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of automation by enterprises to streamline their testing processes. The automation trend is fueled by the growing consumer spending on technological solutions, as businesses seek to improve efficiency and reduce costs. However, the market faces challenges, including the lack of awareness and standardization in test data management practices. This obstacle hinders the effective implementation of test data management solutions, requiring companies to invest in education and training to ensure successful integration. To capitalize on market opportunities and navigate challenges effectively, businesses must stay informed about emerging trends and best practices in test data management. By doing so, they can optimize their testing processes, reduce risks, and enhance overall quality.

What will be the Size of the Test Data Management 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 market continues to evolve, driven by the ever-increasing volume and complexity of data. Data exploration and analysis are at the forefront of this dynamic landscape, with data ethics and governance frameworks ensuring data transparency and integrity. Data masking, cleansing, and validation are crucial components of data management, enabling data warehousing, orchestration, and pipeline development. Data security and privacy remain paramount, with encryption, access control, and anonymization key strategies. Data governance, lineage, and cataloging facilitate data management software automation and reporting. Hybrid data management solutions, including artificial intelligence and machine learning, are transforming data insights and analytics. Data regulations and compliance are shaping the market, driving the need for data accountability and stewardship. Data visualization, mining, and reporting provide valuable insights, while data quality management, archiving, and backup ensure data availability and recovery. Data modeling, data integrity, and data transformation are essential for data warehousing and data lake implementations. Data management platforms are seamlessly integrated into these evolving patterns, enabling organizations to effectively manage their data assets and gain valuable insights. Data management services, cloud and on-premise, are essential for organizations to adapt to the continuous changes in the market and effectively leverage their data resources.

How is this Test Data Management Industry segmented?

The test data management 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. ApplicationOn-premisesCloud-basedComponentSolutionsServicesEnd-userInformation technologyTelecomBFSIHealthcare and life sciencesOthersSectorLarge enterpriseSMEsGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACAustraliaChinaIndiaJapanRest of World (ROW).

By Application Insights

The on-premises segment is estimated to witness significant growth during the forecast period.In the realm of data management, on-premises testing represents a popular approach for businesses seeking control over their infrastructure and testing process. This approach involves establishing testing facilities within an office or data center, necessitating a dedicated team with the necessary skills. The benefits of on-premises testing extend beyond control, as it enables organizations to upgrade and configure hardware and software at their discretion, providing opportunities for exploration testing. Furthermore, data security is a significant concern for many businesses, and on-premises testing alleviates the risk of compromising sensitive information to third-party companies. Data exploration, a crucial aspect of data analysis, can be carried out more effectively with on-premises testing, ensuring data integrity and security. Data masking, cleansing, and validation are essential data preparation techniques that can be executed efficiently in an on-premises environment. Data warehousing, data pipelines, and data orchestration are integral components of data management, and on-premises testing allows for seamless integration and management of these elements. Data governance frameworks, lineage, catalogs, and metadata are essential for maintaining data transparency and compliance. Data security, encryption, and access control are paramount, and on-premises testing offers greater control over these aspects. Data reporting, visualization, and insigh

This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for energy conservation as a loss term. These models were pre-trained with uncalibrated Process-Based model outputs (PB0) before training on actual temperature observations. Performance was measured as root-mean squared errors relative to temperature observations during the test period. Test data include compiled water temperature data from a variety of sources, including the Water Quality Portal (Read et al. 2017), the North Temperate Lakes Long-TERM Ecological Research Program (https://lter.limnology.wisc.edu/), the Minnesota department of Natural Resources, and the Global Lake Ecological Observatory Network (gleon.org). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).

Context

In modern software engineering, Continuous Integration (CI) has become an indispensable step towards systematically managing the life cycles of software development. Large companies struggle with keeping the pipeline updated and operational, in useful time, due to the large amount of changes and addition of features, that build on top of each other and have several developers, working on different platforms. Associated with such software changes, there is always a strong component of Testing.

In software versioning systems, e.g. GitHub or SVN, changes to a repository are made by commiting a new version to the system. To ensure the new version proper functioning, tests need to be applied.

As teams and projects grow, exhaustive testing quickly becomes inhibitive, as more and more tests are needed to cover every piece of code, becoming adamant to select the most relevant tests earlier, without compromising software quality.

We believe that this selection can be made through establishing a relationship between modified files and tests. Hence, when a new commit arrives with a certain amount of modified files, we apply relevant tests early-on, maximising early detection of issues.

Content

The dataset is composed by 3 columns: the commit ID, the list of modified files and the list of tests that were affected in that commit. The data was collected over a period of 4 years from a company of the financial sector, that was composed of ~90 developers.

Inspiration

Some interesting questions (tasks) can be performed on this dataset -

• How can we establish meaningful file-test links ?
• What Data Cleaning steps lead to the best result ?
• How to deal with increasing data dimensionality ?
• How to deal with deprecated/unused files and tests?

This data set is composed of one Sentinel-2 image of Darwin City, Australia. The objective of this work is to use EO data to compare the performance of different machine learning algorithms.

Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label “match” or “no match”) for four product categories, computers, cameras, watches and shoes. In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test sets. For each product category, we provide training sets in four different sizes (2.000-70.000 pairs). Furthermore there are sets of ids for each training set for a possible validation split (stratified random draw) available. The test set for each product category consists of 1.100 product pairs. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web weak supervision. The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites. For more information and download links for the corpus itself, please follow the links below.

Test Data Management as a Service Market Outlook

According to our latest research, the global Test Data Management as a Service market size reached USD 1.23 billion in 2024, with a robust year-on-year growth driven by the increasing complexity of enterprise applications and the demand for efficient data management solutions. The market is forecasted to expand at a CAGR of 13.7% from 2025 to 2033, reaching a projected value of USD 4.11 billion by 2033. This significant growth trajectory is primarily attributed to the rising adoption of DevOps and Agile methodologies, stringent data privacy regulations, and the accelerating digital transformation across various industries.

The growth of the Test Data Management as a Service market is propelled by the escalating need for high-quality test data to support continuous software development and deployment cycles. As organizations increasingly shift towards Agile and DevOps frameworks, the demand for reliable, secure, and scalable test data management solutions is surging. Enterprises are recognizing that effective test data management is critical for minimizing defects, reducing time-to-market, and ensuring compliance with regulatory standards. The proliferation of data-intensive applications and the growing emphasis on data security further amplify the need for advanced test data management services, especially in highly regulated sectors such as BFSI and healthcare.

Another key growth driver is the growing complexity of IT environments and the diversification of data sources. Modern enterprises operate in hybrid and multi-cloud ecosystems, where managing consistent and compliant test data across disparate platforms is a formidable challenge. Test Data Management as a Service offerings provide centralized, automated, and policy-driven solutions that address these challenges by enabling seamless data provisioning, masking, and subsetting. The rise of artificial intelligence and machine learning applications also necessitates sophisticated test data management to ensure the accuracy and reliability of model training and validation processes. As a result, organizations are increasingly turning to managed service providers to streamline their test data management processes, reduce operational overheads, and enhance business agility.

The market is also benefiting from the tightening of data privacy regulations such as GDPR, CCPA, and HIPAA, which mandate stringent controls over the use and protection of sensitive data. These regulations are compelling organizations to adopt robust test data management practices, including data masking, encryption, and anonymization, to safeguard personally identifiable information (PII) during software testing. Test Data Management as a Service platforms are uniquely positioned to help enterprises navigate these regulatory complexities by offering automated compliance features, audit trails, and real-time monitoring capabilities. The increasing frequency of data breaches and cyber threats further underscores the importance of secure test data management, driving sustained investment in this market.

From a regional perspective, North America currently dominates the Test Data Management as a Service market, accounting for the largest share in 2024 due to the presence of numerous technology giants, early adoption of cloud-based solutions, and stringent regulatory frameworks. Europe follows closely, with significant growth observed in countries such as the UK, Germany, and France, where data privacy concerns and digital transformation initiatives are fueling demand. The Asia Pacific region is expected to witness the highest CAGR during the forecast period, driven by rapid digitization, expanding IT infrastructure, and the increasing adoption of cloud services in emerging economies like India and China. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as organizations in these regions increasingly recognize the value of efficient test data management in supporting their digital agendas.

Component Analysis

The Component segment of the Test Data Management as a Service market is bifurcated into software and services, each playing a pivotal role in shaping the industry landscape. The software sub-segment encompasses a range of test data management tools designed to automate data provisioning, masking, and subsetting processes. These solutions are increasingly integrated with advan

Overview

The data has been split into two groups:

• training set (train.csv)
• test set (test.csv)

The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features.

The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic.

We also include gender_submission.csv, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like.

Data Dictionary:

| Variable | Definition | Key | | --- | --- | | survival | Survival | 0 = No, 1 = Yes | | pclass | Ticket class | 1 = 1st, 2 = 2nd, 3 = 3rd | | sex | Sex | | | Age | Age in years | | | sibsp | # of siblings / spouses aboard the Titanic | | | parch | # of parents / children aboard the Titanic | | | ticket | Ticket number | | | fare | Passenger fare | | | cabin | Cabin number | | | embarked | Port of Embarkation | C = Cherbourg, Q = Queenstown, S = Southampton |

Variable Notes

pclass: A proxy for socio-economic status (SES) 1st = Upper 2nd = Middle 3rd = Lower

age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5

sibsp: The dataset defines family relations in this way... Sibling = brother, sister, stepbrother, stepsister Spouse = husband, wife (mistresses and fiancés were ignored)

parch: The dataset defines family relations in this way... Parent = mother, father Child = daughter, son, stepdaughter, stepson Some children travelled only with a nanny, therefore parch=0 for them.

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Dataset Description

Context and Methodology

• Research Domain/Project:
  This dataset was created for a machine learning experiment aimed at developing a classification model to predict outcomes based on a set of features. The primary research domain is disease prediction in patients. The dataset was used in the context of training, validating, and testing.

• Purpose of the Dataset:
  The purpose of this dataset is to provide training, validation, and testing data for the development of machine learning models. It includes labeled examples that help train classifiers to recognize patterns in the data and make predictions.

• Dataset Creation:
  Data preprocessing steps involved cleaning, normalization, and splitting the data into training, validation, and test sets. The data was carefully curated to ensure its quality and relevance to the problem at hand. For any missing values or outliers, appropriate handling techniques were applied (e.g., imputation, removal, etc.).

Technical Details

• Structure of the Dataset:
  The dataset consists of several files organized into folders by data type:

  • Training Data: Contains the training dataset used to train the machine learning model.

  • Validation Data: Used for hyperparameter tuning and model selection.

  • Test Data: Reserved for final model evaluation.

  Each folder contains files with consistent naming conventions for easy navigation, such as train_data.csv, validation_data.csv, and test_data.csv. Each file follows a tabular format with columns representing features and rows representing individual data points.

• Software Requirements:
  To open and work with this dataset, you need VS Code or Jupyter, which could include tools like:

  • Python (with libraries such as pandas, numpy, scikit-learn, matplotlib, etc.)

Further Details

• Reusability:
  Users of this dataset should be aware that it is designed for machine learning experiments involving classification tasks. The dataset is already split into training, validation, and test subsets. Any model trained with this dataset should be evaluated using the test set to ensure proper validation.

• Limitations:
  The dataset may not cover all edge cases, and it might have biases depending on the selection of data sources. It's important to consider these limitations when generalizing model results to real-world applications.

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to assess status and trends of bat populations while developing innovative and community-driven conservation solutions using its unique data and technology infrastructure. To support scalability and transparency in the NABat acoustic data pipeline, we developed a fully-automated machine-learning algorithm. This dataset includes audio files of bat echolocation calls that were considered to develop V1.0 of the NABat machine-learning algorithm, however the test set (i.e., holdout dataset) has been excluded from this release. These recordings were collected by various bat monitoring partners across North America using ultrasonic acoustic recorders for stationary acoustic and mobile acoustic surveys. For more information on how these surveys may be conducted, see Chapters 4 and 5 of “A Plan for the North American Bat Monitoring Program” (https://doi.org/10.2737/SRS-GTR-208). These data were then post-processed by bat monitoring partners to remove noise files (or those that do not contain recognizable bat calls) and apply a species label to each file. There is undoubtedly variation in the steps that monitoring partners take to apply a species label, but the steps documented in “A Guide to Processing Bat Acoustic Data for the North American Bat Monitoring Program” (https://doi.org/10.3133/ofr20181068) include first processing with an automated classifier and then manually reviewing to confirm or downgrade the suggested species label. Once a manual ID label was applied, audio files of bat acoustic recordings were submitted to the NABat database in Waveform Audio File format. From these available files in the NABat database, we considered files from 35 classes (34 species and a noise class). Files for 4 species were excluded due to low sample size (Corynorhinus rafinesquii, N=3; Eumops floridanus, N =3; Lasiurus xanthinus, N = 4; Nyctinomops femorosaccus, N =11). From this pool, files were randomly selected until files for each species/grid cell combination were exhausted or the number of recordings reach 1250. The dataset was then randomly split into training, validation, and test sets (i.e., holdout dataset). This data release includes all files considered for training and validation, including files that had been excluded from model development and testing due to low sample size for a given species or because the threshold for species/grid cell combinations had been met. The test set (i.e., holdout dataset) is not included. Audio files are grouped by species, as indicated by the four-letter species code in the name of each folder. Definitions for each four-letter code, including Family, Genus, Species, and Common name, are also included as a dataset in this release.

Oral bioavailability is a pharmacokinetic property that plays an important role in drug discovery. Recently developed computational models involve the use of molecular descriptors, fingerprints, and conventional machine-learning models. However, determining the type of molecular descriptors requires domain expert knowledge and time for feature selection. With the emergence of the graph neural network (GNN), models can be trained to automatically extract features that they deem important. In this article, we exploited the automatic feature selection of GNN to predict oral bioavailability. To enhance the prediction performance of GNN, we utilized transfer learning by pre-training a model to predict solubility and obtained a final average accuracy of 0.797, an F1 score of 0.840, and an AUC-ROC of 0.867, which outperformed previous studies on predicting oral bioavailability with the same test data set.

Test Data Generation Tools Market Outlook

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

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

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

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

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

Context

Since i have started research in the field of data science, i have noticed there are lot of data sets available for NLP, medicine, images and other subjects but i could not find any single adequate data for the domain of software testing. The data sets which are hardly available are extracted from some piece of code or some historical data that too not available publicly to analyze. The domain of software testing and data science, especially machine learning has a lot of potential. While conducting research on testcase prioritization especially in initial stages of software test cycle the way companies set the priorities in software industry there is no black box data set available in that format. This was the reason that i wanted such data set to exist. So i collected the necessary attributes , arrange them against their values and make one.

Content

This data was gathered in [Jan, 2021], from a local industry's MIS, developed by a software team worked on company's whole software package including their management system. The dataset is in .csv format, there are 1314 rows and 8 columns in this data set. The detail of these eight attributes are as under: B_Req --> Business Requirement R_Prioirty --> Requirement Priority of particular business requirement and explained in .txt file. Weight --> I have assigned a weightage against "R_Priority(Requirement Priority)" its criteria is explained in Testing_MIS.txt file. FP --> Function point of each testing task, which in our case are test cases against each requirement under covers a particular FP Complexity --> Complexity of a particular function point or related modules(the description of assigning complexity is listed below in this section)* Time --> Estimated max time assigned to each Function Point of particular testing task by QA team lead. Cost --> Calculated cost for each function point using complexity and time with function point estimation technique to calculates cost using the formula listed below: cost = “Cost = (Complexity * Time) * average amount set per task or per Function Point note: In this case it is set as 7$ per man hour. The criteria for complexity is listed in .txt file attached with this version. Prioirty --> Is the assigned testcases priority against each Function Point by the testing team.

Acknowledgements

I would like to thank the persons from QA departments of different software companies. Especially team of the the company who provided me this estimation data and traceability matrix to extract data and compile these in to a dataset. I get a great help from the websites like www.softwaretestinghelp.com, www.coderus.com and many other sources which helps me to understand all the testing process and in which phases priorities are assigned usually.

Inspiration

My inspiration to collect this data is the shortage of dataset showing the priority of testcases with their requirements and estimated metrics to analyze the data while doing research in automation of testcase priority using machine learning. --> The dataset can be used to analyze and apply classification or any machine learning algorithm to prioritize testcases. --> Can be used reduce , select or automate testing based on priority, or cost and time or complexity and requirements. --> Can be used to build recommendation system problem related to software testing which helps software testing team to ease their task based on estimation and recommendation.