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Top 5 Features in Selenium 4 for Selenium Automation Testing

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Top 5 Features in Selenium 4 for Selenium Automation Testing

Automation testing has been making more strides with DevOps and agile methodologies undergoing more advancement.

Most businesses aspire for achieving quality software as quickly as possible. This is only achievable when the process of software testing is more effective. Leveraging the power of automation testing you can accelerate the testing process, thanks to sophisticated test automation frameworks and reusable test suites. 

Selenium is one of the most fancied tool suites for automatic testing of websites and web applications. The brainchild of Simon Stewart has undergone a major upgrade and the new makeover of its functionalities and features is highly commendable. Speaking of which, when Selenium WebDriver 1.0 was released in 2007, it left an indelible imprint on the software testing industry. It’s safe to say that Selenium’s contributions have successfully transformed the QA scenario. Simon Stewart has introduced Selenium 4 for grid, IDE, and web drivers. 

Coming into existence on 15 February 2021, Selenium 4 beta 1 introduced advanced features like new APIs, advanced IDE, W3C compliance, Selenium grid, and so on. This is just a trailer for some more interesting upgrades Selenium 4 has brought to the plate. In this post, we are going to take a look at the top five features in Selenium 4 for automation testing. This article will cover the most important revamped features in detail and offer a description of how they would benefit QA professionals. Let’s dig in.

1. W3C Standardization

Why Web Presence is More Critical Than Ever Before

Selenium 3 had the JSON protocol as the basis of browser interaction. As a result, it needed constant encoding as well as decoding of the APIs. Contrary to it, Selenium 4 aligns with the W3C standard protocol. This involves the browser and driver communication following a standard procedure that excludes API in coding and decoding. This way, direct communication occurs.

In other words, W3C standardization offers a flexible and friendly API for browser automation. It instigates compatibility between different software implementations of API and WebDriver. Since standardizing most web technologies is the premise behind W3C standards, it increases the stability of the framework and simultaneously reduces the level of complexities across various browsers.

If you are still using GeckoDriver and ChromeDriver browser drivers, the standardization won’t cause additional effects considering they already offer support for W3C protocols. Rest assured, it won’t negatively impact existing users.

2. Relative Locators

By including relative locators, Selenium 4 has simplified locating elements. Also known as friendly locators, relative locators help in locating web elements above, left of, near to, right of, and below a particular element. As long as you are clear about the visual location relative to the elements, it helps in locating web elements with ease. Here are the 5 relative locators in Selenium 4.

  • near() : Web element away from the specified element (approximately 50 pixels).

  • below(): Web element present below the specified element.

  • above(): Web element above the specified element.

  • toRightOf(): Web element to the right of a specified element.toLeftOf() : Web element located to the left of specified element.

Selenium 4 has relative locators sorted on the basis of proximity. This makes the outcomes highly deterministic. In other words, the sorting is on the basis of how far the bounding client rect of an element is from the midpoint. Also, instead of being able to use just tag names for finding relative locators, you can easily use any selector.

3. Selenium Grid 4 and An Upgraded IDE

The hub and node architecture made previous Selenium grid versions complicated. After all, during automation testing, testers had to start these separately. However, in Selenium 4, they come in one jar file. In other words, at the start of the server, it acts as both the node and hub. 

The infrastructure is more traceable and scalable and it supports nodes, distributors, session maps, and routers. Since Selenium grid 4 supports tools such as Azure, AWS and so on, it also facilitates the DevOps process. You can use the new grid in three modes known as fully distributed, hub and node, and standalone mode.

Kudos to Selenium grid for implementing the GraphQL model and the deployment of infrastructure is based on Kubernetes. Get docker images with the least maintenance, thanks to a standalone Firefox server. You don’t even need to set up virtual machines as Selenium grid 4 offers on-demand docker containers. The updated grid provides support for IPv6 addresses. This enables users to carry out seamless communication with the Selenium grid. They can use TLS connection-supported HTTPS protocol for this purpose.

The Selenium IDE has a revived user interface and logins that completely support Firefox, Chrome, or other web extension plugins. With the help of if’ and ‘while’ conditions, users can improvise on the scripts. you can also export recorded tests in C#, Java, .net, JavaScript, and python. The new Selenium IDE also offers upgraded locator strategies and a backup element selector for increasing the stability of tests.

Selenium 4 has the IDE as an addon for Microsoft Edge, Chrome, Firefox, and other major browsers. It also enables QA professionals to use node.js platform for running projects. The recent upgrade promises an intuitive UX, thanks to the new and updated user interface.

4. Better Documentation

Since Selenium 2.0, there has been no update in its documentation. But with the latest release, all official documents including IDE, grid, and web driver clearly explained the necessary changes. Earlier versions of Selenium had bulky documentation with huge chunks of text. This created hindrances in understanding how to make the most of this platform. But the new user interface Selenium 4 offers is a game-changer!

Users can easily navigate to the section of their choice. Selenium 4 targets to assist developers and testers in finding the necessary information with great ease. This includes information about language binding, tools, and so on. As we already know, the Selenium umbrella encompasses a wide variety of APIs and tools. 

The descriptive documentation covers them all which is especially beneficial for r beginners regardless of whether they are individual testers or QA teams. This way, you can straight away kickstart automation testing after getting acquainted with the functionalities and features of Selenium 4.

5. Multiple Tabs and Windows Management

Testers need to constantly switch tabs and windows while working on a single test flow. All previous Selenium versions called for creating a whole new WebDriver object for managing multiple tabs and windows. But with the new release, testers no longer need to indulge in this utter waste of time. 

The WindowHandle feature allows testers to switch to a new tab or window and take subsequent actions in the respective tab or window. Thanks to the new API (newWindow), testers no longer need to create a whole new WebDriver object with every session.

Some Additional Features in Selenium 4 for Selenium Automation Testing

A crucial additional feature for Selenium includes the Chrome debugging protocol. The Chrome DevTools protocol enables QA professionals to use various Chrome development properties including Profiler, Network, Performance, Fetch, Application cache, etc. Users can also carry out geolocation testing and simulate network conditions by leveraging the API. This API also helps testers and developers resolve bugs faster.

Selenium 4 replaces capabilities objects with Options. Testers can set test requirements after creating an Options object. Browser-respective options include FirefoxOptions, ChromeOptions, InternetExplorerOptions, EdgeOptions, and SafariOptions. Actions Class comes with new methods including click(WebElement), clickAndHold(WebElement), contextClick(WebElement), doubleClick(WebElement), and release().

Selenium 4 allows users to monitor console logs and JS exceptions and facilitates network interception. Meaning, users can intersect network responses and requests along with validating and modifying them. Basic Auth, Mocking Geolocation, Device Mode simulation, backward compatibility, and Network Simulation are some other crucial offerings of Selenium 4. Yes, that’s right! Migration from a previous Selenium version to the new one is a peach. It won’t affect the stability of existing test cases.

As a matter of fact, plenty of test automation tools have been using Selenium as a sturdy base that helps in developing a full-fledged automation testing framework. To use Selenium 4 to its fullest potential, you need an All-in-One solution that offers low code automation testing for desktop, mobile API, and web.

Summing It Up

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It doesn’t seem like there’s any progressive business left unaware of how crucial automation testing is. But the best products require the best frameworks and test automation tools. Without a doubt, Selenium is one of the most widely used ones by testers at a global level.

The recent update has been gaining plenty of traction from its target audience. After all, Selenium 4 offers several additional benefits such as parallel testing, better documentation, enhanced cross browser testing, and seamless integration. The long-awaited release is ready to transform the testing scenario ever since it came out. With its improved features, all you need is the right automation testing platform that easily allows the running of Selenium tests on the cloud with lightning fast speed.

Speaking of which, most global enterprises like Microsoft, Cisco, Xerox, Scholastic, Capgemini, and many more trust LambdaTest for test execution. The secure and safe cloud-based infrastructure offers access to more than 3000 mobile and desktop browser environments. With unmatched test execution speed, you can unblock the precious time of your developers and test more often. One of the main aims is to reduce costs and delivery timelines.

You can easily run Selenium automation scripts on LambdaTest’s reliable, secure, and scalable Selenium grid cloud. Scale your testing process by accelerating release cycles with parallel testing in Selenium. Its latest feature Hypertest promises a speed as good as local test execution and it is cost-effective. With LambdaTest, you can also perform a detailed analysis of the Selenium test log for on-the-go debugging. 

Not to mention, you will get hawkeye insights such as video logs, raw Selenium logs, network logs, exception logs, and so on. The best part is, before deciding on LambdaTest as your go-to automation testing tool, you can try it out for free for a hundred minutes for automation testing!

So, are you ready to level up your automation testing game with Selenium 4? Whether it’s node.js, Java, JavaScript, or C#, rest assured. LambdaTest’s effortless integration has got you covered. So, what are you waiting for? Combine the upgraded features of Selenium 4 with the best cross browser testing tool and become an unbeatable epitome of high product quality!


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NLP & Computer Vision in Cybersecurity

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NLP & Computer Vision in Cybersecurity

Natural language processing (NLP) and computer vision are two branches of artificial intelligence (AI) that are disrupting cybersecurity.

NLP is the ability of computers to understand and process human language, including speech and text. In cybersecurity, NLP can be used for fraud detection by analyzing large amounts of text data, such as emails and chat logs, to identify patterns of malicious activity. NLP can also be used for threat intelligence by analyzing data from various sources, such as news articles and social media, to identify potential security threats.

Computer vision, on the other hand, refers to the ability of computers to interpret and understand images and videos. In cybersecurity, computer vision can be used for password cracking by analyzing images and videos that contain passwords or other sensitive information. It can also be used for facial recognition, which verifies the identity of individuals who access sensitive information or systems.

Cybersecurity is a critical issue in our increasingly connected world, and artificial intelligence (AI) is playing an increasingly important role in helping to keep sensitive information and systems secure. In particular, natural language processing (NLP) and computer vision are two areas of AI that are having a major impact on cybersecurity.

NLP_in_Cybersecurity.png

Source: Masernet

NLP and computer vision have the potential to revolutionize the way organizations approach cybersecurity by allowing them to analyze large amounts of data, identify patterns of malicious activity, and respond to security threats more quickly and effectively. However, it’s important to be aware that AI itself presents new security risks, such as the potential for AI systems to be hacked or misused. As a result, organizations must adopt a comprehensive and well-informed approach to cybersecurity that takes into account the full range of risks and benefits associated with AI technologies. Here are 4 ways NLP & computer vision are useful in cybersecurity.

1. Detecting Fraud

NLP can be used to analyze large amounts of text data, such as emails and chat logs, to identify patterns of fraud and other types of malicious activity. This can help organizations to detect and prevent fraud before it causes significant harm.

2. Analyzing Threats

NLP can also be used to analyze large amounts of text data from a variety of sources, such as news articles and social media, to identify potential security threats. This type of “big data” analysis can help organizations to respond to security threats more quickly and effectively.

3. Preventing Password Cracking

Computer vision can be used to crack passwords by analyzing images and videos that contain passwords or other sensitive information. This type of technology can help organizations to better protect their sensitive information by making it more difficult for attackers to obtain passwords through visual means.

4. Improving Facial Recognition

Computer vision can also be used for facial recognition, which can help organizations to improve their security by verifying the identity of individuals who access sensitive information or systems.

Conclusion

Visual-AI-Workflow-For-Phishing-Detection-1200x883.jpg

Source: Visua

AI technologies like NLP and computer vision are playing an increasingly important role in helping to keep sensitive information and systems secure. These technologies have the potential to revolutionize the way that organizations approach cybersecurity by allowing them to analyze large amounts of data, identify patterns of malicious activity, and respond to security threats more quickly and effectively. However, it’s also important to recognize that AI itself presents new security risks, such as the potential for AI systems to be hacked or misused. As a result, organizations must take a holistic and well-informed approach to cybersecurity that takes into account the full range of risks and benefits associated with these powerful new technologies.

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What’s Wrong with the Algorithms?

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What's Wrong with the Algorithms?

Social media algorithms have become a source of concern due to the spread of misinformation, echo chambers, and political polarization.

The main purpose of social media algorithms is to personalize and optimize user experience on platforms such as Facebook, Twitter, and YouTube.

Most social media algorithms sort, filter, and prioritize content based on a user’s individual preferences and behaviors. Social media algorithms have come under scrutiny in recent years for contributing to the spread of misinformation, echo chambers, and political polarization.

Facebook’s news feed algorithm has been criticized for spreading misinformation, creating echo chambers, and reinforcing political polarization. In 2016, the algorithm was found to have played a role in the spread of false information related to the U.S. Presidential election, including the promotion of fake news stories and propaganda. Facebook has since made changes to its algorithm to reduce the spread of misinformation, but concerns about bias and polarization persist.

Twitter’s trending topics algorithm has also been criticized for perpetuating bias and spreading misinformation. In 2016, it was revealed that the algorithm was prioritizing trending topics based on popularity, rather than accuracy or relevance. This led to the promotion of false and misleading information, including conspiracy theories and propaganda. Twitter has since made changes to its algorithm to reduce the spread of misinformation and improve the quality of public discourse.

YouTube’s recommendation algorithm has been criticized for spreading conspiracy theories and promoting extremist content. In 2019, it was revealed that the algorithm was recommending conspiracy theory videos related to the moon landing, 9/11, and other historical events. Additionally, the algorithm was found to be promoting extremist content, including white nationalist propaganda and hate speech. YouTube has since made changes to its algorithm to reduce the spread of misinformation and extremist content, but concerns about bias and polarization persist.

In this article, we’ll examine the problem with social media algorithms including the impact they’re having on society as well as some possible solutions.

1. Spread of Misinformation

Spread_of_Information.jpg

Source: Scientific American

One of the biggest problems with social media algorithms is their tendency to spread misinformation. This can occur when algorithms prioritize sensational or controversial content, regardless of its accuracy, in order to keep users engaged and on the platform longer. This can lead to the spread of false or misleading information, which can have serious consequences for public health, national security, and democracy.

2. Echo Chambers and Political Polarization

Political_Polarization.jpg

Source: PEW Research Center

Another issue with social media algorithms is that they can create echo chambers and reinforce political polarization. This happens when algorithms only show users content that aligns with their existing beliefs and values, and filter out information that challenges those beliefs. As a result, users can become trapped in a self-reinforcing bubble of misinformation and propaganda, leading to a further division of society and a decline in the quality of public discourse.

3. Bias in Algorithm Design and Data Collection

Bias_in_Algorithm_Design.png

Source: Springer Link

There are also concerns about bias in the design and implementation of social media algorithms. The data used to train these algorithms is often collected from users in a biased manner, which can perpetuate existing inequalities and reinforce existing power structures. Additionally, the designers and developers of these algorithms may hold their own biases, which can be reflected in the algorithms they create. This can result in discriminatory outcomes and perpetuate social injustices.

4. Democracy in Retreat

Derosion_of_Democracy.jpeg

Source: Freedom House

Social media algorithms are vulnerable to manipulation and can spread false or misleading information, which can be used to manipulate public opinion and undermine democratic institutions. The dominance of a few large social media companies has led to a concentration of power in the hands of a small number of organizations, which can undermine the diversity and competitiveness of the marketplace of ideas, a key principle of democratic societies.

How to Improve Social Media Algorithms?

Boost_Social_Media_Posts.jpeg

Source: Tech Xplore

Governments and regulatory bodies have a role to play in holding technology companies accountable for the algorithms they create and their impact on society. This could involve enforcing laws and regulations to prevent the spread of misinformation and extremist content, and holding companies responsible for their algorithms’ biases.

There are several possible solutions that can be implemented to improve social media algorithms and reduce their impact on democracy. Some of these solutions include:

  • Increased transparency and accountability: Social media companies should be more transparent about their algorithms and data practices, and they should be held accountable for the impact of their algorithms on society. This can include regular audits and public reporting on algorithmic biases and their impact on society.

  • Regulation and standards: Governments can play a role in ensuring that social media algorithms are designed and operated in a way that is consistent with democratic values and principles. This can include setting standards for algorithmic transparency, accountability, and fairness, and enforcing penalties for violations of these standards.

  • Diversification of ownership: Encouraging a more diverse and competitive landscape of social media companies can reduce the concentration of power in the hands of a few dominant players and promote innovation and diversity in the marketplace of ideas.

  • User education and awareness: Social media users can be educated and empowered to make informed decisions about their usage of social media, including recognizing and avoiding disinformation and biased content.

  • Encouragement of responsible content creation: Social media companies can work to encourage the creation of high-quality and responsible content by prioritizing accurate information and rewarding creators who produce this content.

  • Collaboration between industry, government, and civil society: Addressing the challenges posed by social media algorithms will require collaboration between social media companies, governments, and civil society organizations. This collaboration can involve the sharing of data and best practices, the development of common standards and regulations, and the implementation of public education and awareness programs.

Conclusion

Social media companies have the power to censor and suppress speech, which can undermine the right to free expression and the democratic principle of an open and inclusive public discourse. It is crucial for technology companies and policymakers to address these issues and work to reduce the potential for harm from these algorithms. Social media platforms need to actively encourage and facilitate community participation in the development and improvement of their algorithms. This would involve setting up forums for discussion and collaboration, providing documentation and support for developers, and engaging with the community to address their concerns and ideas. In order to ensure that the algorithms are fair and unbiased, tech companies need to be transparent about the data they collect and use to train their algorithms. This would involve releasing the data sets used to train the algorithms, along with information about how the data was collected, what it represents, and any limitations or biases it may contain.

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Daasity builds ELT+ for Commerce on the Snowflake Data Cloud

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Cloud Computing News

Modular data platform Daasity has launched ELT+ for Commerce, Powered by Snowflake.

It is thought ELT+ for Commerce will benefit customers by enabling consumer brands selling via eCommerce, Amazon, retail, and/or wholesale to implement a full or partial data and analytics stack. 

Dan LeBlanc, Daasity co-founder and CEO, said: “Brands using Daasity and Snowflake can rapidly implement a customisable data stack that benefits from Snowflake’s dynamic workload scaling and Secure Data Sharing features.

“Additionally, customers can leverage Daasity features such as the Test Warehouse, which enables merchants to create a duplicate warehouse in one click and test code in a non-production environment. Our goal is to make brands, particularly those at the enterprise level, truly data-driven organisations.”

Building its solution on Snowflake has allowed Daasity to leverage Snowflake’s single, integrated platform to help joint customers extract, load, transform, analyse, and operationalise their data. With Daasity, brands only need one platform that includes Snowflake to manage their entire data environment.

Scott Schilling, senior director of global partner development at Snowflake, said: “Daasity’s ELT+ for Commerce, Powered by Snowflake, will offer our joint customers a way to build a single source of truth around their data, which is transformative for businesses pursuing innovation.

“As Snowflake continues to make strides in mobilising the world’s data, partners like Daasity give our customers flexibility around how they build data solutions and leverage data across the organisation.” 

Daasity enables omnichannel consumer brands to be data-driven. Built by analysts and engineers, the Daasity platform supports the varied data architecture, analytics, and reporting needs of consumer brands selling via eCommerce, Amazon, retail, and wholesale. Using Daasity, teams across the organisation get a centralised and normalised view of all their data, regardless of the tools in their tech stack and how their future data needs may change. 

ELT stands for Extract, Load, Transform, meaning customers can extract data from various sources, load the data into Snowflake, and transform the data into actions that marketers can pursue. For more information about Daasity, our 60+ integrations, and how the platform drives more profitable growth for 1600+ brands, visit us at Daasity.com.

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