Tag: #test

Maynooth University scientists pioneer breakthrough fingerprint forensic test

A pioneering new test that can recover fingerprints from ammunition casing, once thought nearly impossible, has been developed by scientists at Maynooth University.

Dr Eithne Dempsey, and her recent PhD student Dr Colm McKeever, of Maynooth University’s Department of Chemistry, have developed a unique electrochemical method which can visualise fingerprints on brass casings, even after they have been exposed to the high temperature conditions experienced during gunfire.

For decades, investigators have struggled to recover fingerprints from weapons because any biological trace is usually destroyed by the high temperatures, friction and gas released after a gun is fired. As a result, criminals often abandon their weapons or casings at crime scenes, confident that they leave no fingerprint evidence behind.

“The Holy Grail in forensic investigation has always been retrieving prints from fired ammunition casings,” said Dr Dempsey. “Traditionally, the intense heat of firing destroys any biological residue. However, our technique has been able to reveal fingerprint ridges that would otherwise remain imperceptible.”

The team found they could coat brass casings with a thin layer of specialised materials to make hidden fingerprint ridges visible. Unlike existing methods that need dangerous chemicals or high-powered equipment, the Department of Chemistry, Maynooth University process uses readily available non-toxic polymers and minimal amounts of energy to quicky reveal prints from seemingly blank surfaces.

It works by placing the brass casing of interest in an electrochemical cell containing specific chemical substances. When a small voltage is applied, chemicals in the solution are attracted to the surface, coating the spaces between fingerprint ridges and creating a clear, high contrast image of the print. The fingerprint appears within seconds as if by magic!

“Using the burnt material that remains on the surface of the casing as a stencil, we can deposit specific materials in between the gaps, allowing for the visualisation,” said Dr McKeever.

Tests showed that this technique also worked on samples aged up to 16 months, demonstrating remarkable durability.

The research has significant implications for criminal investigations, where the current assumption is that firing a gun eliminates fingerprint residues on casings.

“Currently, the best case of forensic analysis of ammunition casings is to match it to the gun that fired it,” said Dr McKeever. “But we hope a method like this could match it back to the actual person who loaded the gun.”

The team focused specifically on brass ammunition casings, a substance that has been traditionally resistant to fingerprint detection and is the most common type of material used globally.

The researchers believe that the test for fingerprints on brass they have developed could be adapted for other metallic surfaces, expanding its range of potential forensic applications, from firearm-related crimes to arson.

This technique uses a device called a potentiostat, which controls voltage and can be as portable as a mobile phone, making it possible to create a compact forensic testing kit.

“With this method, we have turned the ammunition casing into an electrode, allowing us to drive chemical reactions at the surface of the casing,” said Dr McKeever.

While promising, the new technology faces rigorous testing and validation before it could potentially be adopted by law enforcement agencies worldwide. The research, funded by Research Ireland and Maynooth University is published in a leading forensic science journal, has significant implications for global policing.

The published findings are available in the science journal, Forensic Chemistry: https://www.sciencedirect.com/science/article/pii/S2468170925000256?via%3Dihub

Next-Gen AI Test Tools: The Role of NLP in Automated Testing

AI test tools are changing the way organizations ensure quality for applications since software is getting too complex. Natural Language Processing (NLP) advancements have made automation testing much easier and friendly, as testers can interact with the automation tools in natural language. 

This innovation simplifies the creation of tests and fosters collaboration among technical and nontechnical stakeholders who discuss issues with the traditional approach of testing, which is often time-consuming and dependent on specific expertise. 

Cloud testing platforms such as LambdaTest amplify these benefits through robust infrastructure to execute automated tests across a variety of environments. By using AI and NLP, organizations can simplify their testing processes, enhance team collaboration, and deliver high-quality software products faster to respond to changing user requirements.

Overview of Next-Gen AI Test Tools

Next-generation AI test tools are ready to fight the challenges set by modern development teams. They have offered advanced features of using generative AI, self-healing scripts, and predictive analytics. These innovations together work to improve testing processes most efficiently and effectively, allowing organizations to deliver high-quality software faster.

Generative AI is playing a critical role in this evolution by automating such tasks as the generation of test cases, bug prediction, and the creation of test data. Automation increases the efficiency of testing and accuracy but reduces the manual effort of doing those mundane, repetitive tasks. For instance, the creation of scripts on its own by the system means that generative AI can accelerate tests. Thus, development cycles for the tests become shorter. 

Additionally, test coverage increases with generative AI since varied scenarios are produced, including those edge cases that would have gone undetected without this method. Generative AI reduces manual testing efforts, reducing hidden problems and human error, thereby enhancing cost efficiency in large-scale projects.

Another promising feature of the next-gen AI test tools is self-healing scripts that complement generative AI. Scripts can automatically adapt to changes made in the application under test with no manual interference. This helps minimize maintenance effort; tests remain updated with applications due to self-healing capabilities and dynamic adapting over time to keep the integrity of the testing process intact.

More importantly, predictive analytics revamps the whole testing approach by utilizing historical data to predict potential defects or performance failures in advance. Analytical tools for predictive analytics tools work out the trend in previous test results and point out the potential high-risk areas in the code base. This helps concentrate efforts where they are to be used effectively, making maximum usage of resources available and also improving the quality output.

Open-Source Next-Gen AI Test Tools and Frameworks

Here are some of the well-known open-source next-gen AI test tools to make your software testing processes much better:

 

  • Selenium

One of the widely used open-source testing frameworks is Selenium. This supports multiple programming languages and browsers. It’s very versatile, and one can run and execute tests with maximum flexibility, hence making it very popular among developers.

 

  • Robot Framework

This is an open-source automation framework that is keyword-driven and made easy for non-technical users. It supports web and desktop automation using different libraries and tools.

 

  • Appium

This is an open-source tool used in mobile applications to automate. It supports both native and hybrid apps across iOS and Android platforms and allows the writer to write tests in the preferred programming language.

 

  • Cypress

This open-source testing platform is known to have a very developer-friendly interface and runs the tests directly inside the browser. Debugging can be done relatively easily, especially in modern web applications.

 

  • iHarmony

An AI-powered, open-source tool meant to optimize automated testing for mobile and web applications. The test cases it generates are by the application of Machine Learning (ML) algorithms.

 

  • TestNG

Open-source testing framework derived from JUnit, with a set of functionalities that makes TestNG more powerful and easier to use, like annotations, flexible test configuration, and parallel test execution.

 

  • Watir

An open-source tool that uses Ruby for automation testing. Watir mimics the way a user works with a browser and has been pretty reliable in functional testing.

 

  • Cucumber

It is a tool that supports Behavior Driven Development (BDD) and lets users author tests in plain language using Gherkin syntax. This helps include non-technical stakeholders better in understanding test cases.

 

  • Jest

This is a testing framework mainly used for JavaScript applications. Jest is an open-source testing framework that lets users write unit tests with simple assertions and mocking capabilities.

Understanding NLP in Automated Testing

NLP refers to the ability of machines to understand and interpret human language. This has dramatically transformed software testing practices. The availability of NLP in automated testing allows for the creation and maintenance of tests without deep code so that nontechnical people, like analysts and product managers, can participate directly in the process. They will not need an extensive programming base to be able to contribute to the testing procedure. 

NLP can help automatically derive structured test cases from user stories and functional specifications to ensure that their application requirements indeed reflect. Other than that, NLP tools can update dynamically the test case as the change in requirements and ensure that their relevance and efficiency are maintained; this streamlines the testing, improves collaboration among the team members, enhances coverage of tests, and provides much more reliable results. Organizations can better meet user needs and deliver high-quality software products more efficiently.

Key Features of NLP in Test Automation

NLP is revolutionizing test automation by bringing features that make it more user-friendly and efficient. NLP allows testers to use natural language, which makes the testing process easier and promotes team collaboration. Here are some key features that describe the impact of NLP in test automation:

 

  • Simplified Test Creation

The feature of NLP allows the test team to devise test cases easily in simple language. Thus it means that whether technical or non-technical teams can understand, collaboration and the creation of tests can be handled. This again simplifies everything and gives diversification toward varied views on testing the application.

 

  • Maintaining Dynamic Test

NLP tools can automatically update tests with the changes in application requirements so that tests remain relevant and effective. This dynamic maintenance reduces the effort of keeping tests in line with the current state of the application, saving development teams time and resources.

 

  • Improved Test Coverage

NLP-driven tests cover a broad range of scenarios by emulating real interactions with users, which includes edge cases that might be able to be overlooked by traditional testing. It allows potentially problematic features to be found early and ensures robustness and reliability in the applications under a variety of conditions.

Benefits of Using NLP in Automated Testing

Using NLP in automated testing has several benefits that make the quality and efficiency of the testing process better:

 

  • Increased Accessibility

NLP makes it possible for nontechnical team members to be involved in test development using natural language. This is inclusive and creates a collaborative approach between departments, which leads to better testing strategies reflective of the real world.

 

  • Rapid Testing Cycles

NLP tools facilitate automated updates and provide real-time feedback on test results, which enables faster iterations. This efficiency reduces time-to-market, thus enabling organizations to respond quickly to user feedback and changing market demands.

 

  • Accuracy

By minimizing human errors through automated testing, NLP provides test results that are reliable and repeatable. Due to high accuracy, the quality of developed software is improved so that stakeholders and end-users have confidence in the produced software.

The Role of Cloud in Automated Testing with NLP Integration

Cloud-based testing can support modern-day software development, wherein it is very possible to let the organization carry out tests using numerous devices from many browsers using little infrastructure; hence, flexibility allows applications to execute well in changed environments and settings. Cloud provides the teams using cloud resources to effectively simulate different kinds of testing cases. 

Furthermore, NLP embedded into cloud-based testing enables testers to work with test case creation and even test case maintenance in natural languages. This is much easier to develop than with the traditional black box approach. 

Collaboration among teammates, regardless of technical background, promotes development and quickly adapts to all changes in application while still carrying full test coverage, ultimately leading to improved quality and faster times-to-market.

Linking NLP with Cloud Testing

The integration of NLP with cloud testing enhances the process by allowing rapid execution of NLP-driven tests across multiple configurations, saving time, and ensuring proper coverage of various user scenarios.

A powerful platform for automated testing is LambdaTest; it streamlines the testing process for developers and testers alike by providing a reliable infrastructure for running automation tests using frameworks such as Selenium, Cypress, and Appium. 

Such features that LambdaTest offers include blazing-fast automation for faster release cycles across 3000+ desktop and mobile environments, auto-healing in finding flaky tests without any human intervention, and robust security compliance through the SOC2 Type2 certification and GDPR adherence. 

Hence, it makes this organization reliable for delivering results with quality standards while improving testing efficiency significantly by leveraging NLP-driven automation tools in combination with LambdaTest. 

This synergy makes it possible for teams to design powerful, low-maintenance, and highly adaptive automated tests. The users can put forward the test scenario in plain language and will immediately receive real-time feedback regarding their test results.

Moreover, these platforms assure cross-browser compatibility. So, having a blend of AI QA tools like LambdaTest with NLP power makes the organization versatile and robust in its testing procedures to provide better software products quickly. 

The Future of AI Testing Tools

As we look ahead, the landscape of AI testing tools will continue evolving. Here are some trends that are shaping the future:

 

  • Increased Adoption of AI-Powered Tools

As organizations increasingly rely upon AI tools enhancing automation, hence use AI-powered ML algorithms in designing test cases, which can upgrade old test cases plus optimize their suit.

 

  • Enhanced Collaboration through Codeless Testing

A codeless solution is preferred with codeless testing, as this saves the hassle that a nono-coder must follow to prepare tests. Additionally, it improves efficiency as well as nontechnical collaborations for team members working on these two streams of professions.

 

  • Integration with Continuous Integration and Continuous Delivery or Deployment (CI/CD) Pipelines

AI solutions will be able to identify trends of test failure and, hence, optimize future test runs by focusing on the most failed areas. These tools based on data trend analysis in NLP models further suggest efficient paths without redundant cases during testing.

 

  • Smart Error Detection and Test Optimization

AI solutions will be able to identify trends of test failure and optimize future test runs by focusing on the most failed areas. These tools based on data trend analysis in NLP models further suggest efficient paths without redundant cases during testing.

 

  • Focus on User Experience (UX) Testing

Next-generation AI test tools will keep enhancing UX, a factor essential for software success. These test tools will simulate real user interactions for better testing phases. This trend will identify usability issues early in the development process of organizations.

 

  • Continuous Learning Capabilities

The future AI testing tools will include continuous learning, which will imply that it will change its tactics after knowing the previous results. These systems will learn from past mistakes and successes to improve future testing strategies automatically.

Conclusion

To conclude, NLP is changing the face of automation testing; it reduces the complex method of test creation and maintenance and allows teams to stay ahead of the growing complexity of software applications. 

With this, AI testing tools and methods such as NLP play a vital role in enhancing collaboration and efficiency for competing organizations. LambdaTest helps teams improve the quality of the product, the satisfaction of the users, and overall organizational success through rapid adaptation to change. Quicker release cycles and better-quality products enable the effective meeting of the changing expectations of the users.

Dyson launch search for creators to produce content and test out their Zone headphones with rewards given

Having disrupted the audio category with the launch of the Dyson Zone™ noise-cancelling headphones in the UK and Ireland in May 2023, Dyson has announced a casting call for aspiring content creators across the UK and Ireland to join its first ever Dyson Field Testers program – a collaborative global community for music and technology fans.

With applications open between 30 November – 31 December 2023, people across Ireland can  Apply to become a Dyson Field Tester today to get hands on the high-fidelity headphones and create social content bringing to life their experience.

Successful applicants will form part of an exclusive network of creators across Great Britain and Ireland, Australia, New Zealand, and the US, that forms part of Dyson’s new global Instagram channel @dysonwear launching on 30 November 2023. After receiving a pair of Dyson Zone™ noise-cancelling headphones to road test the technology, Dyson Field Testers will curate social content based on a series of themed challenges. The most unique and inspiring social posts will be rewarded with music experiences, subscriptions, sporting tickets,  Dyson technologies and more.

Applicants who are selected to become a Dyson Field Tester will form part of an exclusive community and receive:

  • Seeding pack including Dyson Zone™ noise-cancelling headphones
  • Fortnightly themed challenges
  • Rewards for curating unique content.
  • Updates on upcoming Dyson audio technologies and experiences
  • The opportunity to be featured on Dyson’s global social media channels.

 Apply here  before 31 December 2023 to become a Dyson Field Tester .

Test Data Management: Boosting Quality and Efficiency in Software Testing

Test data management guarantees the accuracy, dependability, and alignment of test data with end-user needs. TDM encompasses three primary categories of data, namely control data, reference data, and the test data itself, ensuring comprehensive management of all relevant data types. Control data serves as the main dataset against which other data is compared, while reference data is used to test specific software functions. Test data refers to the actual data used in the testing process, which can be derived from control or reference data or created specifically for testing purposes. Implementing effective test data management tools brings several benefits that enhance the efficiency and reliability of software testing. Let’s explore some of these advantages:

Enhanced Compliance and Security Measures

Test data often contain sensitive and confidential information. TDM ensures compliance with data protection regulations and security best practices. By encrypting and securing sensitive data, TDM safeguards it from unauthorized access, reducing the risk of data breaches and ensuring regulatory compliance.

Data Provisioning for Various Types of Testing 

TDM ensures the provisioning of appropriate data for different types of tests. It prevents the usage of production data for unit tests, which could yield inaccurate results. Similarly, it avoids relying on sample data for performance tests, ensuring a realistic assessment of system performance under real-world conditions. By provisioning the right data for each testing type, TDM enhances the accuracy of test results.

Enhanced Data Reusability

Efficient TDM promotes the reusability of test data. After generating accurate and dependable test data, it can be securely stored in a repository for subsequent utilization. The ability to reuse test data not only saves valuable time and effort but also enables testing teams to dedicate their focus to other essential tasks, thereby expediting the overall testing process.

Eliminating Data Redundancy

In projects involving multiple teams, redundant copies of production data can be created, resulting in wasted storage space. TDM tools provide a centralized repository that eliminates data redundancy. All teams can access and utilize the same data, optimizing storage capacity and reducing data duplication.

Enhanced Performance of Application

The ultimate benefit of TDM lies in its ability to deliver high-quality data. By ensuring accurate and reliable test data, TDM enables the early detection of bugs and flaws during the testing phase. As a result, the development of robust and superior applications is facilitated, with a reduced number of issues during the production phase. This ultimately leads to an improved user experience and heightened customer satisfaction.

Conclusion

Test data management plays a vital role in testing automation software by guaranteeing the quality, precision, and dependability of test data. By implementing effective TDM practices, businesses can achieve optimal test coverage, reduce costs through early bug detection, and maintain data compliance and security. Opkey offers a comprehensive TDM solution that incorporates test mining technology to autonomously extract test data from clients’ environments in the required format. Opkey’s solution includes mining master data details such as Employees, Orders to Cash, Items, and more, reducing the data collection efforts of QA teams by up to 40%. This TDM solution proves highly effective during enterprise resource planning (ERP) migrations or regression testing for periodic updates. By leveraging Opkey’s TDM solution, companies can save time and resources, ensuring their test data is readily available for Oracle testing.

Check Your Drink releases results of a nationwide survey on drink spiking

Ireland’s first drink spiking detection test, CYD Check Your Drink, has released the results of a nationwide survey on drink spiking, shedding light on the issue’s prevalence and impact on the Irish population.

According to the survey, more than half of respondents, at 57%, know someone who has been spiked in the past, while nearly 19% of respondents self-reported being spiked themselves. When asked about their feelings towards drink spiking, 54.15% of people said they were more afraid of being spiked now than in the last five years, while 30.77% reported having the same level of concern as before.

The survey indicates that the Irish population is more aware of drink spiking issues than before and recognises the need to address the problem.

Commenting on the research, Owen McKeon, CEO of Phrassa, CYD Check Your Drink states: “Our research clearly shows that drink spiking is a pervasive problem in Ireland, and it’s time we took action. We are extremely proud to offer a simple and effective solution that can help people protect themselves and prevent becoming victims. It’s our hope that Check Your Drink’s self-test kits will become a standard part of any night out, ensuring that everyone can enjoy a safe and positive social experience.”

How to Make Sure Your Test Automation Strategy is Scalable Testing with Automated Testing Services

Test automation is one of the most important aspects of software development. It helps developers to identify and fix bugs in their code before it goes into production. That way, they can make sure that the software is stable and provides a good user experience.

In order to understand how to make sure your test automation strategy is scalable, you need to first understand what scalability means. Scalability is the ability of a system or process to handle an increasing amount of work or activity as more resources are added. In other words, it’s the ability for a company’s testing team to keep up with its growing demand for testing services without hiring more people or spending too much time writing tests.

There are many ways you can ensure that your test automation strategy will be scalable in the future. One way would be by automating regression testing which ensures that features don’t break over time when new updates are released by developers. Another way would be by using automated testing services which allow companies to quickly and efficiently get new tests added to their automated suites.

Why Invest in Test Automation?

Test automation is a way to develop software with the help of computers, without the need for human intervention. Automated testing is a way to find and fix bugs in an application before it is released. It also helps developers improve their code quality and maintainability.

The benefits of test automation are:

  • Reduced costs due to faster release cycles
  • Improved quality by catching bugs early on in the development process
  • Reduced risk of regression bugs that can occur when new features are added

Benefits of Automated Testing

Automated testing is a process that can detect software bugs before they are shipped to customers. It is a way of reducing the cost, time and risk of software development.

Automated testing can:

  1. Identify bugs quickly and reliably
  2. Reduce the number of defects in software
  3. Improve the quality of the code

One of the benefits of automated testing is that it can be used for regression testing, which means it will test for any bugs or errors that might have been introduced during the development. Automated testing services are a great way to ensure your product is up to date with all its bugs fixed and ready for release.

The Value of an Automated Testing Service for Your Organization

An automated testing service https://u-tor.com/services/managed-testing is a great way to save time and money. These services can help you do more with less by covering more test cases with less resources. Automated testing services can be an excellent solution for organizations that are struggling to keep up with the ever-increasing demand for quality assurance and continuous integration in their development process.

Testing is a critical component of any project and it is the responsibility of the team to ensure that quality standards are met. However, it can be difficult to find skilled professionals who have the time and resources to test every aspect of your product. This is where an automated testing service can help you – they will take care of all your testing needs so you don’t have to worry about hiring a team or managing the process yourself.

We check out the Lidl Ireland Rapid SARS – CoV-2 Antigen test.

OK so yesterday the Lidl Ireland antigen test hit the shelves and it was pretty much a welcome move until NPHET and Dr Tony Holohan jumped in and said do not rely on the test which is fine however if people are using them is it not good to be a bit more proactive here but also noting these do not give you a free pass. I can see his point and it is valid to some degree but these tests are out there in other shape or form and already in use and I went a bought the one in question you might see some other talk on these and they look different but after posting on Twitter my DMs went off asking about the test, this is not a review or a clickbait dummies guide or “how to” do the test but we go through the package and show you what is involved all is clearly written in the contents and many want to see what the test actually looks like and what is in the package this is all new to us remember..

There is two types of testing out there today and they are Antigen and PCR and PCR being the more accurate of the two and the Antigen test can be done at home without the need to send it off and wait for your results to come back if anything it reminds you of a pregnancy test. The kit can be bought in Lidl at the checkout area for €25 which is not bad and you get 5 tests in the package.

I done the test and it took around 20 mins in total to complete and is not invasive although it might be uncomfortable for some. Given this is a rapid test I do see the point of NPHET telling us not to purchase such as some might think it is a ticket to roam about the place as if nothing is going on but being proactive does no harm either and gives you an extra layer of comfort knowing if all is ok, Is Dr Holohan being the Lidl version of Dr Phil here? Many will argue yes some will not. Anyhow keep following the guidelines and stay safe and also check out the KYMIRA MASK we recently checked out. Finally Whatever results your get in the test you should follow the government guidelines.