#AITesting

[edutech-brijesh]

Emerging trends in software testing include AI-driven testing, shift-left testing for early defect detection, and increased adoption of automation and DevOps practices.

[kani-2br]

Artificial intelligence in testing drives precision, speed, and adaptability, allowing teams to uncover issues faster while ensuring higher-quality software. AI can learn from past data, predict outcomes, and continuously improve testing strategies, making it a game-changer in modern software development.

[brewteams]

How to Start Building AI Software Without Getting Overwhelmed

Artificial Intelligence (AI) is transforming industries, driving automation, enhancing productivity, and making businesses more competitive. From personalized recommendations to predictive maintenance, AI technologies offer numerous benefits. However, many small and medium-sized enterprises (SMBs) often perceive building AI software as an expensive endeavor, only achievable by large corporations. The reality is that with the right approach and planning, you can make AI software on a budget and still harness the power of this revolutionary technology.

In this blog, we will discuss how to build AI software affordably, the role of custom software development services in UAE, and how to strategically create a solution that aligns with your business goals. We’ll also explore critical aspects like using AI in software testing, understanding AI software costs, and how AI differs from other software.

1. Understanding AI Software Development

Before diving into the step-by-step guide on how to build AI software on a budget, it’s essential to understand what AI software is and how it differs from traditional software.

How Is AI Software Different from Other Software?

Traditional software follows predefined rules, executing tasks based on algorithms written by developers. AI software, on the other hand, uses data to “learn” and adapt over time. Instead of following strict rules, AI systems use machine learning (ML) models and neural networks to process and analyze data, improving their performance based on patterns and trends.

For example, a traditional inventory management system will track stock based on programmed thresholds, while an AI-driven system will forecast demand, optimize inventory levels, and predict future trends based on historical data.

This difference in operation fundamentally changes the way AI software is developed. While traditional software development involves coding based on specific user inputs, AI software relies heavily on data collection, model training, and ongoing optimization.

Now that we have a basic understanding of AI, let’s explore how to build AI software, even with budget constraints.

2. How to Build AI Software on a Budget: Step-by-Step Guide

a. Identify a Business Problem AI Can Solve

The first step in building AI software is identifying a specific problem in your business that AI can address. This could be anything from automating customer service to improving inventory management, streamlining operations, or optimizing marketing efforts.

Start by asking yourself:

What manual processes can AI automate?

How can AI enhance decision-making within your business?

Are there areas where data can be leveraged to improve outcomes?

For example, AI can help automate repetitive tasks such as sorting customer emails, processing invoices, or providing product recommendations based on past purchase behavior.

Once you identify the problem, it’s easier to focus on building a solution with a clear goal in mind, helping you save costs on unnecessary development efforts.

b. Leverage Pre-Built AI Tools and Platforms

One of the most cost-effective ways to start building AI software is to leverage pre-built AI tools and platforms. Instead of building AI models from scratch, which can be costly and time-consuming, use readily available AI tools that allow you to customize them for your needs.

Popular AI platforms such as Google Cloud AI, IBM Watson, and Microsoft Azure offer a range of pre-trained models and APIs that can be integrated into your software. These platforms provide AI capabilities like natural language processing (NLP), image recognition, and sentiment analysis without requiring extensive knowledge of data science or machine learning.

By using these tools, you can quickly and affordably add AI features to your software while focusing your resources on customizing the software to suit your business needs.

c. Partner with a Custom Software Development Company

Partnering with a custom software development company can save you both time and money when building AI software. Instead of hiring an in-house development team, which can be costly and require ongoing salaries and benefits, outsourcing your project to a company that specializes in custom software development services allows you to tap into expertise on a project-by-project basis.

When working with a custom software development partner, make sure they have experience in AI and machine learning. This ensures that they can help you design and develop a solution that meets your goals while staying within your budget. The company will handle all aspects of the development process, from model selection to software integration, freeing you to focus on running your business.

d. Start Small with a Minimum Viable Product (MVP)

Building an MVP is a great strategy to stay on budget when developing AI software. Instead of trying to build a fully-featured AI solution from the outset, focus on creating a minimum viable product—a version of the software with only the essential AI features necessary to solve your identified business problem.

For example, if you want to use AI to improve customer support, start by implementing a basic chatbot that can answer frequently asked questions. As you gather user feedback and data, you can gradually enhance the chatbot with more advanced AI features, such as sentiment analysis or personalized responses.

Starting with an MVP allows you to test your solution in the real world, gather valuable feedback, and make improvements—all while keeping development costs low.

e. Use Open-Source AI Frameworks

Another way to reduce the cost of building AI software is to use open-source AI frameworks and libraries. Many powerful machine learning libraries, such as TensorFlow, PyTorch, and Scikit-learn, are open-source and free to use. These libraries offer robust tools and resources for training AI models, processing data, and deploying AI solutions.

By leveraging open-source frameworks, you can avoid the licensing fees and costs associated with proprietary AI software, making it easier to build AI solutions on a budget. Additionally, the active open-source community provides a wealth of resources, tutorials, and support, making it easier for your development team to overcome challenges without incurring additional costs.

f. Consider Cloud AI Services

Cloud-based AI services are another budget-friendly option for building AI software. Instead of investing in expensive hardware to train and deploy AI models, cloud platforms like AWS, Microsoft Azure, and Google Cloud provide scalable, pay-as-you-go solutions.

With cloud AI services, you only pay for the computing power and storage you use, helping you control costs. These platforms also offer pre-built AI models and tools that can be customized to meet your business needs, reducing the complexity and cost of development.

3. How to Use AI in Software Testing

AI can significantly enhance the software testing process by automating repetitive tasks, identifying patterns in large datasets, and improving test accuracy. In traditional testing methods, developers manually write test cases and execute them to ensure software quality. However, AI-driven testing tools can automatically generate and execute test cases based on historical data, significantly reducing the time and effort required.

AI can be used in:

Regression Testing: AI can automatically retest software after updates, ensuring that new code doesn’t break existing functionality.

Bug Detection: AI algorithms can analyze code to detect bugs and vulnerabilities, often before they impact the user experience.

Test Optimization: AI can predict which test cases are most likely to fail, allowing teams to prioritize them and optimize the testing process.

By using AI in software testing, businesses can reduce the time to market, minimize human error, and lower testing costs—all while ensuring high-quality software.

4. How Much Does AI Software Cost?

The cost of developing AI software can vary widely depending on several factors, such as the complexity of the solution, the amount of data required, and the level of customization needed. A simple AI chatbot, for instance, may cost between $10,000 to $50,000 to develop, while more complex AI solutions like predictive analytics systems or image recognition platforms can range from $100,000 to $500,000 or more.

Factors that influence the cost of AI software development include:

Data Collection: AI software requires large datasets for training. If you need to collect or purchase data, this can add to the overall cost.

Model Complexity: More complex AI models, such as deep learning models, require significant computational resources, which can increase development costs.

Custom Features: Adding unique or custom AI features will also raise costs compared to using pre-built models and solutions.

To keep costs under control, focus on building an MVP with essential features and scaling as needed. Additionally, working with a custom software development company that specializes in AI can help you optimize costs while delivering a solution that meets your business needs.

5. The Role of Custom Software Development Services in AI Projects

Custom software development services play a critical role in AI projects, particularly when it comes to tailoring the software to meet specific business goals. Off-the-shelf AI solutions may not address your unique needs, and custom development allows you to create software that is optimized for your operations, workflows, and customer base.

A custom software development company can:

Help you define the problem and scope of the AI solution.

Identify the best machine learning models and AI algorithms for your project.

Customize pre-built AI tools or develop proprietary AI software.

Ensure seamless integration with existing systems, such as ERP, CRM, or other enterprise solutions.

Custom development ensures that the AI solution is designed specifically for your business, helping you achieve higher ROI and competitive advantage.

6. The Future of AI in Custom Software Development

AI is not just transforming the way software is used but also how it is developed. Increasingly, AI is being integrated into the software development process itself, making it faster and more efficient. From writing code snippets to testing, AI can assist developers in creating better software with fewer errors.

For example, AI-driven development tools can automatically generate code based on high-level specifications, reducing the amount of manual coding required. This makes the development process faster and more cost-effective, especially for SMBs looking to create customized software on a budget.

Conclusion

Building AI software on a budget is possible and achievable with the right approach. By leveraging pre-built AI platforms, using open-source frameworks, working with a custom software development company, and starting with an MVP, you can create a cost-effective solution tailored to your business needs. AI offers transformative capabilities, and with strategic planning, businesses of all sizes can benefit from this cutting-edge technology.

Whether you're using AI in software testing, enhancing customer experiences, or optimizing operations, the future of AI in business is bright. Take the first step today by identifying the problem you want AI to solve and finding a development partner who can help you build a solution without breaking the bank. To read more about how to build an app like dubainow step by step guide

[webomates]

Strengthening National Security: The Role of AI Testing in Defense Technology

The fusion of human ingenuity and AI

Gone are the days when you could only rely on traditional methods for safeguarding nations. Today’s defense forces carry out challenging and intricate tasks under erratic and dynamic conditions resulting in an urgent need for modern development and testing strategies.

To succeed, the defense needs to build human intelligence which is aided, enhanced, and augmented with AI and ML capabilities. AI can enhance the testing and quality assurance (QA) processes to ensure improved reliability, precision, and security of crucial defense operations.

Let’s explore the value of AI testing for defense and understand why a strong QA plan is necessary for more intelligent defense solutions.

A Quick Look at the Failures in Defense Due to Lack of Quality Testing

There are numerous examples of potential consequences of insufficient application testing in the U.S. military.

All these errors could have been avoided if the systems were properly tested and validated. According to the Artificial Intelligence in Military Market report, AI in the military market is estimated to be USD 9.2 billion in 2023 and is projected to reach USD 38.8 billion by 2028, at a CAGR of 33.3%.

Priority Outcomes through AI

How can testing solutions help defense? Our objectives and priority outcomes are to:

Unleash defense potential with the power of AI

Through the adoption of AI-enabled testing, our Armed Forces can modernize and rapidly transition into an agile and intelligent force.

Surveillance and threat monitoring

Defense forces capture massive amounts of surveillance data and confidential intelligence from a variety of sources and IoT-connected equipment, such as satellites, drones, radars, and cyberspace. By integrating IoT automated testing into such surveillance and threat monitoring systems, defense forces can validate the reliability of these systems, identify any patterns and monitor potential threats. This allows for effective and proactive defense tactics and increased threat response capabilities.

Enhancing Defense Communications

The defense sector relies heavily on effective communication for successful mission execution, coordination among forces, and ensuring real-time situational awareness.

Testing an ecosystem of intelligently connected devices poses significant challenges.

Functional Testing, which includes Performance testing, Cross-browser testing, and cross-device testing allows the defense systems to undergo extensive testing, minimizing the risk of catastrophic failures during mission-critical operations.

For functional and Usability testing, Webomates has an IoT lab setup for intensive testing of the functionality, usability, accessibility of heterogeneous devices, and networks of these IoT devices.

Accelerating Application Efficiency

Time is of the essence in the defense sector. AI-powered Intelligent Automation Testing solutions will empower the entire force since they will reduce redundant workloads. Defense forces can deploy new systems and updates faster, and also ensure timely response and adaptation to new threats and challenges.

Shift Left Testing speeds up software releases by testing frequently and early in the development process. This method finds issues faster and reduces unexpected outcomes at the end of development.

Mitigating Cybersecurity Risks

One of the critical defense applications for AI technology is cybersecurity, as these attacks can lead to the loss of highly sensitive and confidential data. By leveraging AI testing, defense forces can strengthen their cybersecurity and protect their assets, ensuring that sensitive data is secure and the organization is not compromised

You can take the help of Webomates’ penetration testing, Security testing, Exploratory Testing, and Performance Testing and prevent such cyber attacks.

Strategic Decision Making

Decision making especially in high-stress situations is difficult. And defense forces rely on systems that use AI and ML algorithms to analyze historical and real-time information and interpret data.

These systems need to undergo extensive testing to be able to evaluate risks and help the forces make informed decisions.

Optimizing Resource Allocation

The defense sector works with the motto — ‘Do more with less’ as it operates under strict timelines along with budgetary constraints and must make optimal use of limited resources.

Depending on the requirements of the application, Regression testing along with Exploratory testing can be done on various scales. By pinpointing bottlenecks and highlighting potential improvement areas, they offer valuable test insights into system performance. As a result, defense organizations can optimize their operations, reduce costs, and ensure the most efficient use of resources.

Preventative maintenance of warfare systems

With AI-powered testing techniques, defense forces can switch from reactive to proactive maintenance strategies.

AI testing techniques like defect prediction and self-healing testing can be used by warfare systems including weapons, sensors, navigation, aviation support, and surveillance to identify deviations from expected behavior and take immediate remedial actions. This proactive approach enables teams to handle problems in advance, which reduces downtime and helps them avoid costly consequences.

Secure Software Development and Testing

By automating code validation, deployment validation, and test execution, AI testing can decrease manual effort and improve system resilience as a whole. Continuous testing is an integral part of the CI/CD pipeline, that can be integrated into the defense application’s development lifecycle. Combined with Shift Left Testing, it ensures that the functionality, performance, and security of warfare systems are continuously validated.

Success Story

With our exemplary work with the esteemed US Air Force, we have demonstrated our ability to help organizations achieve scalability and agility while overcoming the typical traditional testing bottlenecks. Webomates has successfully completed SBIR Phase 1 and Phase 2 with the US Airforce.

Webomates’ Testing as a Service (TaaS) — also known as On-Demand testing service — helps you get clear visibility into your testing data, outcomes, and valuable insights by combining applications and data into a single platform.

We work with unwavering dedication to understand your unique needs and provide customized solutions to ensure the success of your application. Take a look at this animation and know the three easy steps you can take to AI automate your application.

To find out more about what Webomates Intelligent Testing services can do for your business, get In touch with us today.

[avisys]

Emerging Technologies in App Testing for Telcos: What to Expect in 2024

The future of App Testing in Telecom is here! AI, ML, RPA, IoT, Blockchain, and the rise of DevOps and Agile testing are transforming the testing game for telcos worldwide.

Read our latest blog post to learn more about what to expect in 2024 and beyond: https://www.avisysservices.com/blog/emerging-technologies-in-app-testing-for-telcos-what-to-expect-in-2024/

[odiware]

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[aitest-tools]

How a Unified QA Platform Can Streamline Your Software Testing Process

A unified QA platform is a software solution that integrates various testing tools

In today's fast-paced software development landscape, delivering high-quality releases quickly is crucial for success.  However, traditional testing methods can be time-consuming and expensive. This is where a unified QA platform can be a game-changer.

A unified QA platform is a software solution that integrates various testing tools and functionalities into a single, centralized platform. This eliminates the need to manage multiple disparate tools and streamlines the entire testing process.

Here are some of the key benefits of using a unified QA platform for your software testing needs:

Improved Efficiency:  A unified platform automates many manual testing tasks, freeing up your team's time to focus on more strategic initiatives.

Enhanced Quality: By providing a comprehensive overview of your testing process, a unified platform helps you identify and fix bugs early in the development cycle.

Reduced Costs: By eliminating the need for multiple tools and subscriptions, a unified platform can help you save money on your software testing budget.

Faster Releases: With streamlined testing processes and automated tasks, a unified platform can help you get your software to market faster.

How aiTest can be your Unified QA Platform?

aiTest is a powerful AI-powered testing platform that can serve as your one-stop shop for all your software testing needs. aiTest offers a variety of features that can help you improve the quality of your software releases, including:

Cross-device testing: aiTest can automate tests across a wide range of devices, ensuring that your software works flawlessly on all platforms.

Synthetic data generation: aiTest can generate synthetic data for your machine learning models, helping you train them more effectively.

Integration with existing tools: aiTest integrates seamlessly with your existing development and testing tools, so you don't have to disrupt your current workflow.

Reporting and analytics: aiTest provides comprehensive reporting and analytics that give you valuable insights into your testing process.

https://app.aitest.qualityx.io/auth/signup

By using aiTest as your unified QA platform, you can experience the many benefits outlined above and streamline your software testing process for improved efficiency, quality, and speed.

[v2softindia]

Artificial Intelligence & Machine Learning tests are provided by a team of certified professionals who are highly trained in the field of AI and machine learning as well as other related areas. Our AI & ML test is designed to measure how well humans can interact with an artificial intelligence or machine learning system by performing tasks such as playing games and answering questions in natural language. The AI & ML Test provides a way to determine the suitability of a person's abilities when working with these technologies, and also functions as an effective recruitment tool for potential employees.

[saketh]

AI Testing

The 5th IEEE International Conference on Artificial Intelligence Testing (AITEST 2023) covers topics such as:

Methodologies for testing, verification and validation of AI applications

Testing AI applications

Tools and environment for automated and semi-automated software testing AI applications for various testing activities and management of testing resources

Techniques for testing AI applications

Applications of AI techniques to software testing

Specific concerns of software testing with various specific types of AI technologies and AI applications

Constraint Programming for test case generation and test suite reduction

Machine learning applications to software testing, such as test case generation, test effectiveness prediction and optimization, test adequacy improvement, test cost reduction, etc

Crowdsourcing and swarm intelligence in software testing

Constraint Scheduling and Optimization for test case prioritization and test execution scheduling

Data quality evaluation for AI applications

Genetic algorithms, search-based techniques and heuristics to optimization of testing

Quality assurance for unstructured training data

Automatic data validation tools

Techniques for testing deep neural network learning, reinforcement learning and graph learning

Large-scale unstructured data quality certification

The 5th IEEE International Conference on Artificial Intelligence Testing (AITEST 2023) will be held in Kallithea on 17-20 July 2023.

[vanivineelaaremanda]

AITEST - 2024

Join industry leaders, academics, and practitioners at the 6th IEEE International Conference on Artificial Intelligence Testing (AITEST 2024), scheduled for July 15-18, 2024, at the Shanghai Institute for Advanced Study of Zhejiang University, China - https://ieeeaitest.com/

This conference is a pivotal platform for disseminating advanced research and developments in AI testing, including methodologies, tools, applications, and practical techniques. AITEST 2024 will feature in-depth discussions on verification, validation of AI applications, optimization of testing resources, quality assurance, and innovative machine learning applications for software testing. Engage with cutting-edge content and network with experts in the field to advance your knowledge and contribute to the evolution of AI testing. Secure your participation for a unique opportunity to influence and drive forward the future of artificial intelligence testing.

#china#internationalconference#artificialintelligence#testing

[webomates]

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[sivakrishh0807]

Posting Call for Paper for IEEE AI Testing Conference

Exciting announcement! 🚀 Join experts from the industry, academia, and the field of artificial intelligence at the 6th IEEE International Conference on Artificial Intelligence Testing (AITEST 2024) from July 15-18, 2024, at the Shanghai Institute for Advanced Study of Zhejiang University, China.

Learn about cutting-edge research and developments in AI testing, covering methodologies, tools, applications, and practical techniques. AITEST 2024 will feature insightful discussions on AI application verification, validation, testing resource optimization, quality assurance, and innovative machine learning applications for software testing.

Secure your spot for this unique opportunity to connect with industry leaders, academics, and practitioners. Let's shape the future of artificial intelligence testing together! 🌐 Visit https://ieeeaitest.com/ for more details.

[odiware]

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