Data Subsetting Tools Market Outlook

According to our latest research, the global Data Subsetting Tools market size reached USD 1.42 billion in 2024, exhibiting robust growth driven by the increasing necessity for efficient data management and compliance across industries. The market is projected to grow at a CAGR of 13.6% during the forecast period, reaching an estimated USD 4.26 billion by 2033. This strong market momentum is primarily fueled by the rapid expansion of digital transformation initiatives, a surge in data privacy regulations, and the rising adoption of cloud-based solutions in both large enterprises and SMEs.

A significant growth factor for the Data Subsetting Tools market is the exponential increase in data volumes generated by organizations across various sectors. Enterprises are dealing with massive, complex datasets that require efficient management for analytics, testing, and development purposes. Data subsetting tools help organizations extract relevant subsets from large databases, significantly reducing storage costs and improving processing speeds. The adoption of these tools is further accelerated by the need to comply with stringent data privacy regulations such as GDPR, HIPAA, and CCPA. These regulations mandate that only necessary and non-sensitive data be used for non-production environments, making data subsetting tools indispensable for compliance-driven industries like BFSI and healthcare.

Another critical driver of growth in the Data Subsetting Tools market is the increasing reliance on software testing and development. As enterprises accelerate their digital transformation journeys, the demand for agile development and DevOps practices is surging. Data subsetting tools enable development teams to create smaller, more manageable test databases that mirror production environments without exposing sensitive information. This not only enhances testing efficiency but also mitigates the risk of data breaches during software development cycles. The ability to quickly generate relevant datasets for testing and analytics is becoming a strategic advantage, further propelling the adoption of data subsetting solutions.

The proliferation of cloud computing is also playing a pivotal role in the expansion of the Data Subsetting Tools market. Cloud-based deployment models offer scalability, flexibility, and cost-effectiveness, making them highly attractive to organizations of all sizes. With the increasing migration of enterprise workloads to the cloud, there is a growing need for data subsetting tools that can seamlessly integrate with cloud infrastructure. These tools enable secure and efficient data management across hybrid and multi-cloud environments, supporting organizations in their efforts to optimize data storage, enhance operational agility, and ensure regulatory compliance.

From a regional perspective, North America continues to dominate the Data Subsetting Tools market, accounting for the largest revenue share in 2024. The region’s leadership is attributed to the early adoption of advanced data management technologies, a mature regulatory environment, and the presence of major technology vendors. Europe follows closely, driven by strict data protection laws and a strong focus on digital innovation. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, expanding IT infrastructure, and increasing investments in cloud-based solutions. As organizations in emerging markets embrace digital transformation, the demand for data subsetting tools is expected to rise significantly across all regions.

Component Analysis

The component segment in the Data Subsetting Tools market is bifurcated into software and services, each playing a crucial role in the overall market landscape. Software solutions constitute the core of data subsetting, providing organizations with the technology required to extract, mask, and manage subsets of data efficiently. These solutions are continually evolving, integrating advanced features such as automation, AI-driven subsetting, and enhanced security protocols. The increasing complexity of enterprise data environments is driving demand for robust, scalable, and user-friendly software that can handle diverse data sources and formats. As organizations prioritize data privacy and operational agility, the software segment is expected to maintain a dominant market share throughout the forecast period.

Data Subsetting Tools Market Outlook

According to our latest research, the global Data Subsetting Tools market size reached USD 1.85 billion in 2024, demonstrating robust growth driven by increasing demand for efficient data management and compliance solutions. The market is expected to expand at a CAGR of 11.2% during the forecast period, reaching a projected value of USD 5.08 billion by 2033. This significant growth is attributed to the rising need for data privacy, regulatory compliance, and the adoption of advanced analytics across various sectors. As organizations continue to handle massive volumes of data, the role of data subsetting tools in optimizing storage, improving testing processes, and ensuring secure data access has become increasingly vital.

One of the primary growth factors for the Data Subsetting Tools market is the intensifying regulatory landscape surrounding data privacy and protection. Legislation such as GDPR in Europe, CCPA in California, and similar frameworks globally are compelling organizations to enforce strict data governance standards. Data subsetting tools enable enterprises to create anonymized or masked subsets of production data, facilitating safer data sharing and compliance with stringent privacy regulations. Furthermore, as data breaches and cyber threats continue to rise, companies are prioritizing solutions that minimize exposure of sensitive information during development, testing, or analytics activities. This focus on compliance and security is driving substantial investments in data subsetting solutions across industries like BFSI, healthcare, and government.

Another significant driver propelling the market forward is the exponential growth in data volumes generated by digital transformation initiatives, IoT deployments, and cloud migration. Organizations are increasingly leveraging data-driven decision-making, which necessitates robust data management and testing environments. However, working with full-scale production data is often impractical due to storage costs, performance bottlenecks, and security risks. Data subsetting tools address these challenges by enabling the creation of smaller, relevant datasets that maintain referential integrity and are representative of the entire data landscape. This capability not only accelerates application development and testing cycles but also reduces infrastructure costs, making data subsetting an indispensable component of modern IT strategies.

The growing adoption of cloud-based solutions and DevOps practices is also fueling demand for advanced data subsetting tools. As enterprises transition to hybrid and multi-cloud environments, the need to securely and efficiently move data across platforms becomes paramount. Data subsetting tools facilitate seamless data migration, environment provisioning, and continuous integration and delivery (CI/CD) pipelines by providing secure, high-quality test data on demand. Moreover, the integration of artificial intelligence and machine learning within these tools is enhancing their ability to automate complex data selection, masking, and provisioning tasks, further boosting operational efficiency and scalability.

Regionally, North America continues to dominate the Data Subsetting Tools market due to the presence of major technology providers, early adoption of innovative data management solutions, and a mature regulatory environment. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, expanding IT infrastructure, and increasing awareness of data privacy regulations. Europe remains a significant market, supported by stringent data protection laws and a strong focus on data-driven business transformation. Other regions such as Latin America and the Middle East & Africa are gradually catching up, with growing investments in digital infrastructure and regulatory reforms expected to drive future demand.

Component Analysis

The Component segment of the Data S

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

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

Discover the booming market for SAP Selective Test Data Management Tools. This in-depth analysis reveals key market trends, growth drivers, leading companies (IntelliCorp, SAP, Qlik, Informatica), and projected market size through 2033. Learn how organizations are optimizing their testing processes and mitigating data risks.

This app produces basic maps and timeseries using data from the GRACE mission. NASA’s GRACE mission provides the first opportunity to directly measure groundwater changes from space. By observing changes in the Earth’s gravity field, scientists can estimate changes in the amount of water stored in a region, which cause changes in gravity. GRACE provides a more than 10 year-long data record for scientific analysis. This makes a huge difference for scientists and water managers who want to understand trends in how our resources are being consumed over the long term. GRACE has returned data on some of the world’s biggest aquifers and how their water storage is changing [e.g. Rodell and Famiglietti, 2001; Yeh et al., 2006; Rodell et al., 2007]. Using estimates of changes in snow and surface soil moisture, scientists can calculate an exact change in groundwater in volume over a given time period. A study by Rodell et al. [2009] in northwest India used terrestrial water storage-change observations from GRACE and simulated soil-water variations from a data-integrating hydrological modeling system to show that groundwater is being depleted at a mean rate of 4.0 +/- 1.0 cm yr-1 equivalent height of water (17.7 +/- 4.5 km3 yr-1) over the Indian states of Rajasthan, Punjab and Haryana (including Delhi). During the study period of August 2002 to October 2008, groundwater depletion was equivalent to a net loss of 109 km3 of water, which is double the capacity of India's largest surface-water reservoir.

Files in this dataset have been produced during Flexibility experiments of Wikidata subsetting practical tools: Subsetting based on references using WDSub.

ABSTRACT: The MODIS global subsetting and visualization tool provides customized subsets and visualizations of 13 MODIS Collection 5 land products on demand for any land area on Earth, from 1 pixel up to 201 x 201 km. Subset products are made available to the user through an interactive web page. On this page are time series plots, QC information, land cover and phenology, citation, and data download. Users specify a location for the center point of their area of interest by either entering the geographic coordinates or selecting the location on an interactive map. The land area is then defined by specifying the desired number of kilometers “Above and Below†and “Left and Right†of the center point (recall that MODIS land products are in sinusoidal projection). Users then choose the desired MODIS land product and date range for this subset. After providing a valid e-mail address, the request is submitted and the subset and associated products created. The user receives an e-mail with a link to a customized “Data Visualization and Download†page. The tool provides time series plots of the measurement, quality information, land cover grid (land cover classification) of the area and phenology, along with an estimate of heterogeneity (Shannon richness and evenness). The land product subset data are provided in comma-separated value (.csv) and GeoTIFF (.tif) formats.

The National Water Availability Assessment Data Companion (NWDC) provides regularly updated, model-based estimates of water availability and use, derived from U.S. Geological Survey (USGS) scientific models. Use this subset and download tool to access NWDC datasets by model and to generate syntactically correct URLs to use with the web service. Datasets can be subset by specific region and/or time period.

This app produces basic maps and timeseries using data from the GRACE mission.

Files in this dataset have been produced during Performance and Accuracy experiments of Wikidata subsetting practical tools: WDumper, KGTK, WDSub, WDF.

Source file: GeneTaxon_wikidata-20201102-all.ttl.gz

ShEx: https://github.com/kg-subsetting/paper-wikidata-subsetting-2023/blob/master/flexibility-experiments/genes%2Btaxons/GeneTaxon.shex

More information: https://www.semantic-web-journal.net/content/wikidata-subsetting-approaches-tools-and-evaluation

Synthetic Test Data Generation Market Outlook

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

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

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

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

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

Component Analysis

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

Source file: GeneTaxon_wikidata-20190121-all.ttl.gz

ShEx: https://github.com/kg-subsetting/paper-wikidata-subsetting-2023/blob/master/flexibility-experiments/genes%2Btaxons/GeneTaxon.shex

More information: https://www.semantic-web-journal.net/content/wikidata-subsetting-approaches-tools-and-evaluation

This resource contains a Jupyter notebook example demonstrating how to interact with the CUAHSI Subsetter Service via APIs using the Subsetter Python Client.

October 2025 - Dataset View and OData/API Connection Change Notice - Please Read: https://data.transportation.gov/stories/s/6kdh-gsgx Rail equipment accidents/incidents, collisions, derailments, fires, explosions, acts of God, or other events involving the operation of railroad on-track equipment (standing or moving) and causing reportable damages greater than the reporting threshold for the year in which the accident/incident occurred, must be reported by railroads to the FRA on Form FRA 6180.54 - Rail Equipment Accident/Incident. Please note that this dataset displays unique train accidents. When an accident involves multiple railroads, each railroad must report its data. As a result, there can be multiple records for one accident. This dataset has been modified to pull and display one record for each accident. Highway-rail crossing incidents have also been removed from this dataset because they are not considered train accidents. To see the full dataset with all reports with all data for all accidents, please visit https://data.transportation.gov/Railroads/Rail-Equipment-Accident-Incident-Data/85tf-25kj. The data dictionary can be found here: https://datahub.transportation.gov/api/views/byy5-w977/files/ac84a1c8-56d4-4a21-85c2-3a33080ab720?download=true&filename=Form54_Data_Dictionary.xlsx. For information on how to filter and export data, please visit: https://data.transportation.gov/stories/s/Download-Export-and-Print-User-Guide/s8hj-vns8/. To view the data release schedule, please visit: https://data.transportation.gov/stories/s/Data-Release-Schedule/qfc9-tapk/.

This resource contains the supporting data and code files for the analyses presented in "Toward automating post processing of aquatic sensor data," an article published in the journal Environmental Modelling and Software. This paper describes pyhydroqc, a Python package developed to identify and correct anomalous values in time series data collected by in situ aquatic sensors. For more information on pyhydroqc, see the code repository (https://github.com/AmberSJones/pyhydroqc) and the documentation (https://ambersjones.github.io/pyhydroqc/). The package may be installed from the Python Package Index (more info: https://packaging.python.org/tutorials/installing-packages/).

Included in this resource are input data, Python scripts to run the package on the input data (anomaly detection and correction), results from running the algorithm, and Python scripts for generating the figures in the manuscript. The organization and structure of the files are described in detail in the readme file. The input data were collected as part of the Logan River Observatory (LRO). The data in this resource represent a subset of data available for the LRO and were compiled by querying the LRO’s operational database. All available data for the LRO can be sourced at http://lrodata.usu.edu/tsa/ or on HydroShare: https://www.hydroshare.org/search/?q=logan%20river%20observatory.

There are two sets of scripts in this resource: 1.) Scripts that reproduce plots for the paper using saved results, and 2.) Code used to generate the complete results for the series in the case study. While all figures can be reproduced, there are challenges to running the code for the complete results (it is computationally intensive, different results will be generated due to the stochastic nature of the models, and the code was developed with an early version of the package), which is why the saved results are included in this resource. For a simple example of running pyhydroqc functions for anomaly detection and correction on a subset of data, see this resource: https://www.hydroshare.org/resource/92f393cbd06b47c398bdd2bbb86887ac/.

These are the customized for the Tethys applications for Togo.

Software Test Data Management Market Outlook

According to our latest research, the global Software Test Data Management market size reached USD 1.45 billion in 2024, demonstrating robust expansion across multiple verticals. The market is expected to grow at a CAGR of 13.2% from 2025 to 2033, with the forecasted market size projected to reach USD 4.13 billion by 2033. This remarkable growth trajectory is primarily driven by the increasing complexity of enterprise software environments, the surging adoption of DevOps and agile methodologies, and stringent regulatory requirements for data privacy and security in software testing. As organizations worldwide strive for faster, more reliable software releases, the demand for advanced test data management solutions is accelerating, shaping a dynamic and competitive market landscape.

One of the foremost growth factors fueling the software test data management market is the ever-increasing pace of digital transformation initiatives across industries. Enterprises are rapidly modernizing their IT infrastructure, adopting cloud-native applications, and integrating advanced analytics and artificial intelligence into their workflows. These changes have significantly increased the volume, variety, and velocity of data that must be managed and tested before deployment. As a result, organizations are seeking sophisticated test data management tools that can automate data provisioning, masking, and subsetting, ensuring high-quality, compliant, and production-like test environments. The need to maintain data integrity and security throughout the software development lifecycle has never been more critical, further propelling the demand for comprehensive test data management solutions.

Another major driver for the software test data management market is the growing prevalence of DevOps and agile methodologies in software development. Modern development cycles require rapid, continuous testing and deployment, which in turn necessitates the availability of realistic, up-to-date test data. Traditional manual approaches to test data management are no longer sufficient, as they are time-consuming, error-prone, and unable to keep pace with the speed of agile sprints. Automated test data management solutions enable organizations to quickly generate, refresh, and mask test data, reducing bottlenecks and accelerating time-to-market. This capability is particularly valuable for industries such as banking, financial services, healthcare, and telecommunications, where data privacy, compliance, and reliability are paramount.

A further catalyst for market expansion is the tightening regulatory landscape surrounding data privacy and protection. Regulations such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA) impose strict requirements on how organizations handle, store, and process sensitive data, including in non-production environments. Test data management solutions equipped with advanced data masking, encryption, and anonymization features are increasingly in demand to help organizations comply with these regulations while still enabling effective software testing. As regulatory scrutiny intensifies globally, the adoption of robust test data management platforms is becoming a strategic imperative for businesses seeking to mitigate compliance risks and safeguard customer trust.

From a regional perspective, North America currently leads the global software test data management market, accounting for the largest revenue share in 2024. The region’s dominance is underpinned by the presence of major technology vendors, a mature IT infrastructure, and early adoption of advanced software development practices. Meanwhile, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, expanding IT investments, and a burgeoning startup ecosystem. Europe also demonstrates significant growth potential, fueled by stringent data protection regulations and increasing demand for secure, scalable test data management solutions. As organizations across all regions prioritize software quality, compliance, and innovation, the global market is poised for sustained growth through 2033.

Component Analysis

The software test data management market by component is primarily segmented into Solutions and Services. Solutions encompass a wide array of tools and platfor

Question Paper Solutions of chapter Debugging Tools of Data Analysis with R, 2nd Semester , Bachelor of Computer Application 2023-2024