Category Archives: Software Outsourcing

test automation

Challenges and Considerations in AI-Driven Test Automation

As the field of software testing continues to evolve, organizations are increasingly exploring the potential of artificial intelligence (AI) in test automation. AI-driven test automation promises enhanced efficiency, accuracy, and coverage in software testing processes. However, implementing AI in test automation comes with its own set of challenges and considerations. In this blog post, we will delve into the key challenges and considerations when incorporating AI into test automation, including data quality, model interpretability, ethical considerations, and the balance between human expertise and automated approaches.

Data Quality

One of the fundamental requirements for successful AI-driven test automation is high-quality data. AI models heavily rely on training data to learn patterns and make predictions. Therefore, organizations need to ensure that the data used to train AI models is accurate, diverse, and representative of the system being tested. Poor data quality, such as incomplete or biased data, can lead to unreliable or skewed results.

To address data quality challenges, organizations should invest in data collection and preprocessing techniques that maintain data integrity and diversity. Data validation processes should be implemented to identify and rectify any anomalies or biases. Moreover, organizations should continuously monitor and update their data sets to reflect the evolving nature of the software systems under test.

 

Model Interpretability

In the realm of AI-driven test automation, one of the key challenges that organizations face is model interpretability. While AI models can offer remarkable accuracy and efficiency in software testing, their inner workings often remain obscure and difficult to comprehend. This lack of transparency poses a significant hurdle in building trust and understanding the decisions made by AI models.

To address the challenge of model interpretability, organizations must prioritize the use of AI models that can provide human-understandable explanations for their decisions. Techniques such as explainable AI (XAI) are emerging to bridge this gap by shedding light on the reasoning behind AI model outputs. By utilizing XAI methods, testers and stakeholders can gain insights into how the model arrived at its conclusions.

There are several approaches to achieving model interpretability. One approach is to use simpler and more transparent models, such as decision trees or rule-based systems, which are inherently interpretable. While these models may not offer the same level of accuracy as complex neural networks, they provide a clear understanding of how input data influences the output.

Another approach involves post-hoc interpretation techniques that aim to explain the behavior of complex AI models. These techniques include generating feature importance scores, visualizing activation patterns, or creating saliency maps to highlight the significant factors that contribute to the model’s decision-making process.

By ensuring model interpretability, organizations can build trust in the results produced by AI-driven test automation. Testers and stakeholders can gain confidence in understanding why certain defects were identified or missed, enabling them to make informed decisions based on the AI model’s outputs. Ultimately, model interpretability contributes to more effective and reliable software testing processes.

 

Ethical Considerations

AI-driven test automation raises ethical considerations that organizations must carefully address. Testing AI systems themselves requires a thorough understanding of the ethical implications surrounding AI technologies. Testers should be aware of potential biases, privacy concerns, and the ethical use of user data in the testing process.

Organizations should establish ethical guidelines and frameworks to ensure responsible and fair use of AI in test automation. This may involve adhering to relevant regulations, conducting ethical reviews of AI models, and implementing mechanisms for addressing potential biases and discrimination. It is crucial to prioritize transparency, accountability, and user consent when collecting and using data for testing AI systems.

 

Balancing Human Expertise with Automated Approaches

While AI-driven test automation offers significant benefits, it is important to strike a balance between automated approaches and human expertise. AI models excel at handling large-scale data analysis and repetitive tasks, but human testers possess critical domain knowledge, intuition, and creativity that AI cannot replicate.

Organizations should recognize that AI-driven test automation is a complementary tool to human expertise, rather than a complete replacement. Human testers play a vital role in designing test scenarios, validating results, and making critical decisions based on context and intuition. Collaboration between AI models and human testers ensures a holistic approach to software testing and maximizes the effectiveness of the testing process.

 

The Future Ahead

AI-driven test automation holds great promise for enhancing the efficiency and effectiveness of software testing. However, organizations must be aware of the challenges and considerations that come with its implementation. By addressing issues related to data quality, model interpretability, ethical considerations, and the balance between human expertise and automated approaches, organizations can navigate the complexities of AI-driven test automation and unlock its full potential. By doing so, they can improve the quality of their software products, streamline their testing processes, and gain a competitive edge in the rapidly evolving digital landscape.

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Beginner’s guide to learn golang: Your first steps to get started

Google created Go programming language, also known as Golang, to address some of the shortcomings and inefficiencies they were experiencing with existing languages and tools.

Why Google created Golang

Compile time: Google has historically struggled with long compile times for its projects having large codebases. This problem has been so significant that Google has developed build tools to separate the dependency analysis and compiling, just to gain a few percent in build time using conventional languages. In an effort to address this issue, Google created the programming language Go, which is designed for faster compilation without the need for dependency checking.

String processing: Google frequently processes and analyzes large amounts of text data in the form of web pages, requiring efficient manipulation of strings. To meet this need, the company incorporated a comprehensive set of string functions into the Go programming language, which also uses garbage collection to make working with strings more efficient compared to some other languages like C++.

Concurrency: The rise of multicore CPUs in the past argued that a language should provide first-class support for some sort of concurrency or parallelism. And to make resource management tractable in a large concurrent program, garbage collection, or at least some sort of safe automatic memory management was required. To address the complexity and difficulty of concurrent programming, where multiple parts of a program run simultaneously, is challenging to do well. Go was designed to make it easier to write concurrent programs by providing built-in support for concurrency and synchronization.

Learning curve: Go is a relatively simple language with a straightforward syntax and a small set of core features. This makes it easy for programmers to learn and use, even if they are new to programming.

Modern software development practices: Go was designed to support modern software development practices such as agile development, continuous integration, and test-driven development. It includes features such as built-in testing and support for dependency management.

Go = C + strings + garbage collection + concurrency.

Key features of Go

Some of the key features of Go include:

Concurrency: Go is built with concurrency in mind and provides a number of features to make it easy to write concurrent code.

Garbage collection: Go includes a garbage collector that automatically manages memory, making it easier for developers to write code without having to worry about memory management.

Static typing: Go is a statically-typed language, which means that variables are explicitly declared with a specific type and the type of a variable cannot be changed during its lifetime. This can help catch errors at compile time and improve the overall reliability of the code.

Lightweight: Goroutines take only 8 kilobytes and you can have thousands of them.

Fast compilation: Go has a fast compiler that can quickly build large programs, making it suitable for building scalable applications.

Zero dependency: Since the language does not rely on any external libraries or frameworks, you do not need to worry about installing those dependencies on the target machine. This can simplify the deployment process and reduce the risk of issues caused by missing or incompatible dependencies. This can be particularly useful for building applications that need to be deployed in a variety of different environments.

Built-in support for testing: Go includes built-in support for writing and running tests, making it easy to test and verify code.

Strong community: Go has a strong and active community of developers who contribute to the language and its ecosystem, including libraries and tools.

Overall, Go is a versatile and powerful programming language that can be used in a wide range of projects. Whether you’re building a web application, a network server, or a command-line tool, Go is a good language to consider.

As more and more companies and people began to realize Go’s potential, it became a mainstream language to build following kinds of products.

  1. World-class system tools like Docker and Kubernetes
  2. Advanced databases like CockroachDB and InfluxDB
  3. Decentralized Blockchain platforms like Ethereum
  4. To separate configuration into infrastructure layer like Istio
  5. Faster continuous deployment like Drone.
  6. More performant messaging systems like NATS.
  7. Widely used CLI tools like Cobra

Some good resources to learn golang

  1. 𝐓𝐡𝐞 𝐨𝐟𝐟𝐢𝐜𝐢𝐚𝐥 𝐆𝐨𝐥𝐚𝐧𝐠 𝐰𝐞𝐛𝐬𝐢𝐭𝐞 provides comprehensive documentation, tutorials, and other resources for learning the language.
  2. “𝐆𝐨 𝐛𝐲 𝐄𝐱𝐚𝐦𝐩𝐥𝐞” website provides a collection of short, easy-to-understand examples of how to use various Golang features.
  3. “𝐀 𝐓𝐨𝐮𝐫 𝐨𝐟 𝐆𝐨” website provides an interactive, in-browser tutorial that teaches the basics of Golang.
  4. “Effective Go” is a free resource to learn the Go programming language and it is available on the official website of Golang. This course provides an explanation of all the key concepts in the Go programming language, how to use them, and their syntax.
  5. “GopherCon UK 2022 Conference” YouTube playlist includes a series of talks and presentations on various topics related to Golang.
  6. 𝐆𝐨𝐩𝐡𝐞𝐫𝐜𝐢𝐬𝐞𝐬 — a FREE course by John Calhoun of calhoun.io.
  7. Golang Crash Course by Traversy Media — This Go language crash course explains all the fundamentals of Golang.
  8. Learn Go Programming by Building 11 Projects — Course by FreeCodeCamp — This Golang course is completely free and published on freeCodeCamp’s YouTube channel.
  9. “𝐓𝐡𝐞 𝐆𝐨 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞” book by Alan A. A. Donovan and Brian W. Kernighan is a comprehensive guide to the language, with detailed explanations and examples.
  10. “𝐆𝐨𝐥𝐚𝐧𝐠 𝐍𝐞𝐰𝐬” website provides the latest news, articles, and resources related to Golang.
  11. “𝐆𝐨 𝐅𝐨𝐫𝐮𝐦” is an online community where you can ask questions and get help from other Golang developers.
  12. “𝐆𝐨𝐥𝐚𝐧𝐠 𝐒𝐥𝐚𝐜𝐤” is an online community where you can join channels and discuss Golang with other developers.

Although the design of most languages concentrates on innovations in syntax, semantics, or typing, Go is focused on the software development process itself. Go is efficient, easy to learn, and freely available, but we believe that what made it successful was the approach it took toward writing programs, particularly with multiple programmers working on a shared codebase.

If you’re ready to turn your software development vision into reality, look no further than Mindfire Solutions. As a trusted industry leader, we are dedicated to delivering excellence in software development, innovation, and reliability. Let’s bring your ideas to life. Contact us today to start your next project with confidence.  Explore Lakin’s original article and discover a treasure trove of trending tech articles on Medium.

 

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cognitive test automation featured img

Cognitive Test Automation: Harnessing Cognitive Computing for Complex Scenarios

In today’s fast-paced digital world, software testing has become an essential part of the software development life cycle. With the increasing complexity of software applications, traditional testing methods are no longer sufficient to ensure the quality of software products. This is where cognitive test automation comes into play. In this blog, we will explore the integration of cognitive computing capabilities, such as natural language processing (NLP) and image recognition, into test automation frameworks to handle complex testing scenarios.

What is Cognitive Test Automation?

Cognitive test automation is a subset of test automation that leverages cognitive computing capabilities to handle complex testing scenarios. Cognitive computing is a type of artificial intelligence that mimics human thought processes. It involves the use of natural language processing (NLP), machine learning, and other advanced technologies to enable computers to understand and interpret human language and behavior.

Cognitive test automation uses these cognitive computing capabilities to automate testing scenarios that were previously difficult or impossible to automate using traditional testing methods. For example, cognitive test automation can be used to automate testing scenarios that involve natural language processing, image recognition, and other complex tasks.

Benefits of Cognitive Test Automation

Cognitive test automation offers several benefits over traditional testing methods. Some of the key benefits include:

  1. Improved Test Coverage: Cognitive test automation can handle complex testing scenarios that were previously difficult or impossible to automate using traditional testing methods. This improves test coverage and ensures that all aspects of the software application are thoroughly tested.
  2. Increased Efficiency: Cognitive test automation can automate repetitive and time-consuming testing tasks, freeing up testers to focus on more complex testing scenarios. This increases efficiency and reduces the time and cost of software testing.
  3. Improved Accuracy: Cognitive test automation uses advanced technologies such as NLP and image recognition to ensure accurate and reliable testing results. This reduces the risk of human error and ensures that testing results are consistent and reliable.
  4. Faster Time-to-Market: Cognitive test automation can speed up the software development life cycle by automating testing tasks and reducing the time and cost of software testing. This enables organizations to bring software products to market faster and stay ahead of the competition.

 

Challenges of Cognitive Test Automation

While cognitive test automation offers several benefits, it also presents several challenges. Some of the key challenges include:

  1. Complexity: Cognitive test automation involves the integration of advanced technologies such as NLP and image recognition into test automation frameworks. This can be complex and requires specialized skills and expertise.
  2. Cost: Cognitive test automation requires specialized tools and technologies, which can be expensive to acquire and maintain. This can be a barrier to adoption for some organizations.
  3. Integration: Cognitive test automation must be integrated with existing test automation frameworks and processes. This can be challenging and requires careful planning and execution.
  4. Data Quality: Cognitive test automation relies on high-quality data to ensure accurate and reliable testing results. This can be a challenge if the data is incomplete, inaccurate, or inconsistent.

 

Methodologies for Cognitive Test Automation

There are several methodologies for implementing cognitive test automation. Some of the key methodologies include:

  1. Scriptless Test Automation: Scriptless test automation involves the use of visual models and drag-and-drop interfaces to create test cases. This approach simplifies the test automation process and reduces the need for specialized skills and expertise.
  2. Model-Based Testing: Model-based testing involves the creation of models that represent the behavior of the software application. These models are then used to generate test cases automatically. This approach reduces the time and cost of test case creation and ensures comprehensive test coverage.
  3. Behavior-Driven Development (BDD): BDD involves the creation of test cases that are based on the behavior of the software application. This approach ensures that testing is aligned with the business requirements and improves the accuracy and relevance of testing results.
  4. Artificial Intelligence (AI) and Machine Learning (ML): AI and ML can be used to automate testing tasks that involve natural language processing, image recognition, and other complex tasks. These technologies can also be used to analyze testing results and identify patterns and trends.

 

Natural Language Processing (NLP) in Test Automation

Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and humans using natural language. NLP can be used in test automation to automate testing tasks that involve natural language processing, such as testing chatbots and voice assistants.

NLP-based test automation involves the use of NLP algorithms to analyze and understand natural language inputs and outputs. This enables testers to create test cases that are based on natural language inputs and outputs, rather than code-based inputs and outputs.

 

Benefits of NLP-based Test Automation

NLP-based test automation offers several benefits over traditional testing methods. Some of the key benefits include:

  1. Improved Test Coverage: NLP-based test automation can handle complex testing scenarios that were previously difficult or impossible to automate using traditional testing methods. This improves test coverage and ensures that all aspects of the software application are thoroughly tested.
  2. Increased Efficiency: NLP-based test automation can automate repetitive and time-consuming testing tasks, freeing up testers to focus on more complex testing scenarios. This increases efficiency and reduces the time and cost of software testing.
  3. Improved Accuracy: NLP-based test automation uses advanced technologies such as NLP algorithms to ensure accurate and reliable testing results. This reduces the risk of human error and ensures that testing results are consistent and reliable.
  4. Faster Time-to-Market: NLP-based test automation can speed up the software development life cycle by automating testing tasks and reducing the time and cost of software testing. This enables organizations to bring software products to market faster and stay ahead of the competition.

 

Challenges of NLP-based Test Automation

While NLP-based test automation offers several benefits, it also presents several challenges. Some of the key challenges include:

  1. Complexity: NLP-based test automation involves the integration of NLP algorithms into test automation frameworks. This can be complex and requires specialized skills and expertise.
  2. Data Quality: NLP-based test automation relies on high-quality data to ensure accurate and reliable testing results. This can be a challenge if the data is incomplete, inaccurate, or inconsistent.
  3. Integration: NLP-based test automation must be integrated with existing test automation frameworks and processes. This can be challenging and requires careful planning and execution.

 

Image Recognition in Test Automation

Image recognition is a technology that enables computers to interpret and understand visual information, such as images and videos. Image recognition can be used in test automation to automate testing tasks that involve image recognition, such as testing user interfaces and web applications.

Image recognition-based test automation involves the use of image recognition algorithms to analyze and understand visual information. This enables testers to create test cases that are based on visual inputs and outputs, rather than code-based inputs and outputs.

 

Benefits of Image Recognition-based Test Automation

Image recognition-based test automation offers several benefits over traditional testing methods. Some of the key benefits include:

  1. Improved Test Coverage: Image recognition-based test automation can handle complex testing scenarios that were previously difficult or impossible to automate using traditional testing methods. This improves test coverage and ensures that all aspects of the software application are thoroughly tested.
  2. Increased Efficiency: Image recognition-based test automation can automate repetitive and time-consuming testing tasks, freeing up testers to focus on more complex testing scenarios. This increases efficiency and reduces the time and cost of software testing.
  3. Improved Accuracy: Image recognition-based test automation uses advanced technologies such as image recognition algorithms to ensure accurate and reliable testing results. This reduces the risk of human error and ensures that testing results are consistent and reliable.
  4. Faster Time-to-Market: Image recognition-based test automation can speed up the software development life cycle by automating testing tasks and reducing the time and cost of software testing. This enables organizations to bring software products to market faster and stay ahead of the competition.

 

Challenges of Image Recognition-based Test Automation

While image recognition-based test automation offers several benefits, it also presents several challenges. Some of the key challenges include:

  1. Complexity: Image recognition-based test automation involves the integration of image recognition algorithms into test automation frameworks. This can be complex and requires specialized skills and expertise.
  2. Data Quality: Image recognition-based test automation relies on high-quality visual data to ensure accurate and reliable testing results. This can be a challenge if the data is incomplete, inaccurate, or inconsistent.
  3. Integration: Image recognition-based test automation must be integrated with existing test automation frameworks and processes. This can be challenging and requires careful planning and execution.

 

Conclusion

Cognitive test automation, NLP-based test automation, and image recognition-based test automation are powerful tools for handling complex testing scenarios in today’s fast-paced digital world. By leveraging cognitive computing capabilities such as NLP and image recognition, organizations can improve test coverage, increase efficiency, improve accuracy, and reduce the time and cost of software testing. While these approaches present several challenges, there are several methodologies and technologies available to help organizations overcome these challenges and reap the benefits of cognitive test automation.

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webtransport-featured-img

WebTransport: Bridging the Gap Beyond WebRTC & WebSockets

Imagine a world where real-time communication is faster, more secure, and more efficient than ever before. A world where online gaming feels as responsive as playing in person, video streaming is seamless and buffer-free, and IoT devices transfer data with lightning speed. Welcome to the era of WebTransport, the cutting-edge technology poised to transform the way we connect and communicate over the web. It’s time to say goodbye to the limitations of traditional protocols and get ready to be amazed by the endless possibilities of WebTransport.

In this blog article, we will explore the advantages of WebTransport over the widely adopted WebSockets and WebRTC technologies. We will uncover how WebTransport harnesses the power of datagrams, streams, and the cutting-edge QUIC protocol to redefine real-time communication, offering unparalleled benefits for a wide range of applications.

We will also discuss the current state of WebTransport adoption and the future of this promising technology.

So, without further ado, let’s dive into the world of WebTransport!

WebTransport is a web API that uses the HTTP/3 protocol as a bidirectional transport. It’s intended for two-way communications between a web client and an HTTP/3 server. It supports sending data both unreliably via its datagram APIs, and reliably via its streams APIs.

How WebTransport works under the hood

WebTransport can be used to send and receive data in two different ways: datagrams and streams.

  • Datagrams are individual packets of data that are limited in size by the maximum transmission unit (MTU) of the underlying connection. They may or may not be transmitted successfully, and if they are transferred, they may arrive in an arbitrary order. Datagrams are provided through the QUIC datagram extension and are prefixed with a session ID that the recipient can use to multiplex different transports
  • WebTransport also uses streams to send and receive data that need reliable and ordered delivery. Streams are provided by creating an individual unidirectional or bidirectional QUIC stream and are also prefixed with a session ID. Streams provide better transport efficiency than the older packet mechanism.

Process:

  1. The client and server first establish a QUIC connection. This is done by using the QUIC handshake protocol.
  2. Once the connection is established, the client and server can begin sending data to each other.
  3. Data is sent in packets. Each packet is encrypted and includes a sequence number.
  4. The receiver uses the sequence number to reorder the packets and decrypt them.
  5. If a packet is lost, the receiver will request a retransmission.
  6. The sender will retransmit the lost packet.
  7. This process continues until all of the data has been sent and received.

 

WebTransport offers versatile capabilities for sending multiple types of data over the same connection. It allows for the reliable transmission of text or file data while concurrently enabling the unreliable transmission of video information. This flexibility enhances the efficiency and richness of communication among numerous simultaneous users. WebTransport facilitates the segregation of different content types on separate channels, preventing one type from blocking the transmission of others. Additionally, the ability to establish bidirectional streams enables quick data exchange between the server and client, making it ideal for implementing messaging systems and facilitating rapid communication.

What is QUIC Protocol?

QUIC, short for “Quick UDP Internet Connections,” is a modern transport protocol designed to enhance internet communication. Developed by Google, QUIC aims to replace TCP by utilizing UDP for faster and more efficient data transfer. It incorporates features like multiplexing to enable simultaneous transmission of multiple data streams, built-in encryption for enhanced security, and advanced congestion control algorithms to optimize network performance. With its standardization efforts by the IETF and increasing adoption by major internet players, QUIC holds the potential to revolutionize internet communication, offering improved speed, security, and reliability for a better web browsing experience.

QUIC is still under development.

Here are some of the benefits of using QUIC:

Performance: QUIC can improve the performance of web applications by reducing latency and increasing throughput. This is because QUIC is a connectionless protocol, which means that it does not need to establish a connection before sending data. This can reduce the amount of time it takes to send data, which can improve the performance of web applications.

Security: QUIC can improve the security of web applications by providing encryption and authentication. This is because QUIC uses TLS 1.3 to encrypt all data that is sent over the connection. This ensures that the data is secure from eavesdropping and tampering.

Efficiency: QUIC is more efficient than TCP in terms of bandwidth and CPU usage. This is because QUIC does not need to resend data that is lost, which can save bandwidth. QUIC also uses less CPU than TCP, which can save power.

Advantages of WebTransport over WebRTC/Websockets

WebTransport offers several advantages over WebSockets and WebRTC in certain use cases. Here are some key advantages of using WebTransport:

Enhanced Performance: WebTransport introduces the QUIC protocol, which offers significant performance improvements over WebSocket and WebRTC. It leverages UDP-based transport, reducing latency and providing faster data transmission. This means quicker response times, smoother real-time interactions, and improved user experiences.

Efficient Resource Utilization: Unlike WebSocket and WebRTC, WebTransport utilizes a more efficient resource management approach. It requires fewer server resources to establish and maintain connections, allowing servers to handle a larger number of simultaneous connections without compromising performance or scalability.

Bidirectional: WebTransport is a bidirectional protocol, which means that data can flow in both directions between the client and server. This is useful for applications that need to send and receive data simultaneously, such as chat applications and multiplayer games.

Streamlined Data Transfer: With WebTransport’s data grams and streams, data can be sent and received in a more organized and efficient manner. It allows for the creation of bidirectional streams, enabling simultaneous data transfer between the client and server. This enables the development of advanced features such as reliable file transfer, simultaneous video streaming, and real-time collaboration.

More Reliable: WebTransport is a reliable protocol, which means that data is guaranteed to be delivered in the correct order. This is important for applications that require high reliability, such as gaming and video streaming.

Robust Security Measures: WebTransport’s streams API, based on the secure QUIC protocol, offers robust security for encrypted streaming. With built-in encryption, it ensures data confidentiality. Additional measures such as Origin header usage and specific opt-in requirements enhance security. WebTransport provides a secure environment for streaming, protecting the integrity and privacy of transmitted data.

Seamless Integration: WebTransport is designed to seamlessly integrate with existing web technologies. It can be easily adopted and incorporated into web applications without requiring major architectural changes. This makes it a viable option for migrating from WebSocket or WebRTC while preserving the functionality and user experience of the existing application.

Support for server push: WebTransport enables servers to proactively push data to clients, eliminating the need for clients to continually poll for updates.

Specific Problems Addressed by WebTransport

Higher latency in WebSockets: WebSockets introduce additional latency due to the handshake and framing overhead, which WebTransport minimizes.

Lack of stream multiplexing in WebRTC: WebRTC does not natively support stream multiplexing, making it less efficient for scenarios requiring multiple concurrent data streams.

Limited transport options in WebSockets: WebSockets primarily provide reliable, ordered transport, whereas WebTransport offers flexibility by supporting both reliable and unreliable transport modes.

Inefficiency of polling for updates: With WebTransport’s server push capability, the need for clients to continually poll the server for updates is eliminated, reducing network congestion and improving efficiency.

Difficulty in extending protocols in WebSockets and WebRTC: WebTransport addresses this problem by providing a more extensible framework, allowing the introduction of new protocols and features without disrupting existing implementations.

Connect to a server using WebTransport QUIC using JavaScript

  1. Client creates a new WebTransport object.
  2. Client calls the connect() method on the WebTransport object, passing in the URL of the server.
  3. WebTransport object establishes a QUIC connection to the server.
  4. Client and server negotiate the parameters of the QUIC connection, such as the encryption and congestion control algorithms.
  5. The client and server can now send data to each other over the QUIC connection.

Here is an example using javaScript:

const webTransport = new WebTransport();

webTransport.connect(‘https://example.com’);

webTransport.on(‘message’, (data) => {

  // Handle the data received from the server.

});

webTransport.on(‘error’, (error) => {

  // Handle the error that occurred while connecting to the server.

});

Possible use cases 

𝐆𝐚𝐦𝐢𝐧𝐠: WebTransport’s bidirectional streams are super helpful for making games. They make sure that the data sent by the server arrives quickly with very little delay. This is awesome for action-packed games like shooting, driving, or fighting because it reduces the time between when you do something on your device and when you see the game respond. It’s also great for cloud gaming services, where the game is run on a powerful server and streamed to your device. With WebTransport, the back-and-forth communication between you and the game server happens fast, making the whole gaming experience smoother and more responsive.

For example, it could be used to send real-time game state updates and player input to other players.

𝐕𝐢𝐝𝐞𝐨 𝐬𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠: WebTransport is a good choice for video streaming, as it can provide a secure, reliable, and efficient way to send and receive video data. In addition, WebTransport can be used to multiplex multiple streams of data over a single connection, which can be useful for streaming video alongside other data, such as audio data or chat messages.

For example, it could be used to send video and audio data to the client without buffering.

𝐂𝐡𝐚𝐭 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: WebTransport is a great fit for chat apps because it allows for fast and efficient communication between users. When we chat with someone, we want our messages to reach them quickly, and we want their responses to come back to us right away. WebTransport makes this happen by providing a smooth and low-latency connection. It ensures that our messages are sent and received almost instantly, creating a more seamless chatting experience. Additionally, WebTransport is designed to handle a large number of users at once, so even in busy chat rooms with lots of people, communication remains smooth and responsive. In simple terms, WebTransport helps chat apps work really well by making messages travel fast and keeping the conversation flowing smoothly.

IoT( Internet of Things) applications: WebTransport can be really useful for transferring data from Internet of Things (IoT) devices to servers. These devices often collect and send small bits of data regularly. With WebTransport, this data can be sent quickly with very little delay. This is important because IoT devices usually run on batteries, so it’s better if they use less energy. Plus, if there are a lot of devices connected to the internet, it can sometimes make the network slow. But with WebTransport, the devices use fewer resources and create less congestion, which means everything works better. So, WebTransport helps make IoT devices work well and saves energy at the same time.

Browser Support:

Chrome: 97+

Firefox: 114+

Edge: 97+

Safari: 16+ (partial support)

Samsung Internet: 19+

Opera: 97+

Current State of WebTransport Adoption and Future of This Promising Technology

The current state of WebTransport adoption shows promising signs for the future of this technology. Although still in its early stages, WebTransport has received significant interest and attention from developers and industry experts. The potential benefits it offers, such as improved performance, lower latency, and increased reliability, make it an attractive choice for building modern web applications. Major browser vendors have started implementing WebTransport protocols and APIs, signaling their belief in its value. As more developers become familiar with WebTransport and explore its capabilities, we can expect to see wider adoption and integration of this technology into various web applications and services. With ongoing advancements and the active involvement of the web development community, the future of WebTransport looks promising, and we can anticipate exciting developments and innovations in this space in the years to come.

Building upon Lakin’s original article, our latest piece delves deeper into the subject, providing fresh insights and a broader perspective. Unlock the true potential of real-time communication with Mindfire. Our experts specialize in enhancing applications through cutting-edge technologies like WebTransport, including QUIC and HTTP/3. Elevate user experiences and stay ahead in the dynamic digital landscape. Let us transform your apps into seamless and efficient communication hubs. Reach out for a consultation today!

 

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The Integration of mHealth into Clinical Practice: Opportunities and Barriers

Mobile health or mHealth solutions are transforming the healthcare industry. From mobile applications that allow for remote monitoring and diagnosis to wearable devices that provide real-time data about a patient’s vital signs, mHealth is changing how we think about healthcare delivery.

According to recent research, the revenue of the global mHealth solutions market is estimated to be $119.5 billion and is expected to grow at a CAGR of 27% to reach a value of $395 billion by 2028.

While there are many potential benefits associated with integrating mHealth into clinical practice, there are also numerous barriers that must be overcome in order for these technologies to reach their full potential.

In this blog post, we will explore both the opportunities and challenges associated with incorporating mHealth into clinical practice.

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Opportunities 

Improved Patient Engagement: mHealth solutions can help patients become more engaged in their own healthcare by providing them with real-time access to their health data. Patients can use these tools to monitor their vital signs, track their medication adherence, and access educational resources. This can lead to improved health outcomes and reduced healthcare costs.

Remote Monitoring: With mHealth solutions, healthcare providers can make medical care more accessible for patients. The technology can be used to remotely monitor patients who have chronic conditions or are recovering from surgery, reducing the need for in-person visits and enabling healthcare professionals to intervene early if there are any concerns.

Enhanced Data Collection: mHealth solutions can enable healthcare professionals to collect more comprehensive data about patients’ health status. Clinical decision support systems (CDSS) are one example of how mHealth can provide data-driven care. CDSSs use predictive analytics and machine learning algorithms to interpret data from multiple sources and offer meaningful insights and advice, allowing healthcare providers to be more efficient in their patient care. This data can be used to identify trends and patterns and to develop more personalized treatment plans.

Increased Efficiency: Healthcare professionals can streamline clinical workflows and reduce administrative burdens by incorporating mHealth solutions, which can enable them to focus more on patient care. For example, mHealth solutions can automate appointment scheduling, provide electronic prescription management, and reduce paperwork.

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Barriers

Privacy and Security Concerns:  The collection and use of patient health data by mHealth solutions raise significant privacy and security concerns. Healthcare providers must ensure that patient data is stored securely and in compliance with privacy regulations.

Lack of Standardization: The lack of industry standards for mHealth systems can make it difficult for healthcare providers to integrate various different technologies into their existing workflows. Without standardized data formats and protocols, it can be challenging to ensure interoperability and data accuracy.

Technical Challenges: The integration of mHealth solutions into clinical practice requires technical expertise and infrastructure. Healthcare providers need to have the necessary hardware, software, and network capabilities in order to effectively use mHealth solutions.

Resistance to Change: The integration of mHealth solutions into clinical practice requires a cultural shift. Healthcare professionals will have to embrace new ways of working. This can be challenging for healthcare organizations, particularly those already burdened with the challenges of less-than-adequate manpower and lengthy decision-making procedures.

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Overcoming Barriers

Invest in Infrastructure: Healthcare organizations should invest in the necessary infrastructure to support the integration of mHealth solutions. This may include upgrading hardware and software systems, investing in secure network capabilities, and training staff on how to use new technologies.

Develop Standards: Establishing and adhering to standards for mHealth systems can help to ensure data accuracy and compliance with privacy regulations. Healthcare organizations should look to develop standard protocols and data formats that are compatible with their existing systems. This process can involve working with government agencies and other stakeholders to establish guidelines.

Educate Healthcare ProfessionalsEncouraging healthcare professionals to embrace new technologies can be the key to the successful integration of mHealth solutions. Educating them on the benefits of leveraging mHealth solutions in clinical practices, and training them on using it effectively is essential.

Collaborate with PatientsEstablishing open communication with patients and involving them in the process of integrating mHealth solutions into clinical practice can help to build trust and increase patient engagement. Encourage healthcare professionals to engage patients in discussions about their care.
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Conclusion

The integration of mHealth into clinical practice offers many opportunities for improving healthcare delivery and outcomes. However, there are also significant barriers that must be overcome in order to make the most of these benefits. As technology continues to advance, it is important that healthcare organizations remain agile and adaptive, ready to embrace new tools and services that can improve patient outcomes and reduce costs.

At Mindfire Solutions, we understand the challenges associated with integrating mHealth solutions into clinical practices. Our team of experienced developers is committed to helping healthcare organizations navigate the complexity and uncertainty of introducing new technologies in their workflows. We are dedicated to developing robust, secure, and intuitive mHealth solutions that meet the needs of your healthcare organization.

Take a peek at our expertise to know how we can assist you to take your services to the next level.

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How has RPA Helped Fintech Companies Become More Resilient?

Legacy financial services companies as well as budding start-ups are leveraging the power of cutting-edge technologies to stay ahead of the curve and transform themselves into core Fintech companies. At the center of driving this change and helping Fintech companies become more efficient and productive is robotic process automation or RPA. According to a study done by Mckinsey, RPA in Fintech can play a very definitive role. Research indicates that the implementation of RPA technology can easily cut down costs by 10-25%, and fully automate approximately 42% of finance activities.

In this blog, we have touched upon ways in which RPA is acting as a catalyst to make Fintech companies become more resilient and efficient.

RPA is a form of automation that uses software bots to perform repetitive and time-consuming tasks. These bots mimic human actions, such as copying and pasting data, filling out forms, and running applications. RPA software can work 24/7, with minimal supervision, and can handle a large volume of tasks with high accuracy and speed.

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Benefits of RPA in Fintech

Improved Productivity: RPA can automate tedious tasks such as data entry, reconciliation, and report generation. These tasks are time-consuming and require a high degree of accuracy. However, by automating these tasks, Fintech companies can reduce costs and improve operational efficiency.

Increased Scalability: Fintech companies with aggressive growth goals need to scale their operations to meet the demand and minimize their system downtime. RPA offers a better scalability solution. Bots can be quickly deployed and configured to handle the increased workloads, making RPA a flexible and adaptable technology.

Improved Compliance: Companies providing financial services operate in a highly regulated environment and are subject to strict compliance requirements. Integrating RPA can ensure that processes are consistent and auditable, thus improving compliance.

Improved Risk Management: Organizations are able to gain better visibility into their operations by using RPA to monitor and evaluate processes. This can help companies identify potential threats, allowing them to take proactive measures to mitigate risks.

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Use Cases of RPA in Fintech

Customer Onboarding: Customer onboarding is a long and tedious process that involves a number of steps such as collecting, verifying, and processing a large volume of data from customers. This is one of the areas where RPA is particularly effective. Automating this process with RPA can reduce the amount of manual work required and provide an improved customer experience as well as enhanced security.

Customer Service: RPA can automate customer service processes, such as responding to customer queries,  balance inquiries, providing information about products and services, etc. By automating these tasks, Fintech companies can reduce costs associated with these processes, freeing up customer service agents to focus on more complex issues.

Mortgage Lending: The process of mortgage lending is very time-consuming; thus, implementing RPA can help speed up the process. It can be used to automatically collect and analyze data, such as credit scores, income statements, employment information, etc., in order to accurately make loan approval decisions. This can help reduce the time taken for loan approvals, eliminate tedious manual processes associated with it, and also improve customer satisfaction.

Loan Underwriting: Loan underwriting is an exhaustive process. Here, RPA bots can be used to bring down the turnaround time for this process to 15 mins. They can also be used to automatically assess a borrower’s creditworthiness, evaluate loan documents and applications, identify potential risks associated with a loan, and assist in making decisions.

Automatically Track Transactions: RPA bots can reconcile thousands of transactions in seconds, whereas it may take several hours for a human to do the same task. This reduces the risk of errors and also improves the organization’s efficiency. It can also track the investment of customers and provide them with comprehensive transaction reports on their portfolio to help them maintain full traceability of their investment transactions.

Generate Reports: With the help of RPA technology, Fintech companies can automatically generate reports on customer data with actionable insights and distribute them to relevant stakeholders, such as regulators and investors, without any human intervention.

Anti-Money Laundering: An RPA bot can automatically flag transactions that require further investigation based on predefined rules and thresholds. This ensures that all transactions are reviewed in a timely and consistent manner, which provides greater security to the Fintech platforms.

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Conclusion

RPA is a game-changer for the Fintech industry. It offers numerous benefits, including cost savings, improved customer experience, increased scalability, and improved compliance and risk management. By adopting RPA, Fintech companies can become more resilient, efficient, and competitive in a rapidly changing industry.

Mindfire Solutions can assist in leveraging the power of robotic process automation to drive growth for Fintech companies. Our industry experts can develop tailor-made solutions to create a frictionless digital experience for your customers.

Contact Mindfire Solutions to learn more about how we can help you get the most out of automation.

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Adoption of Cloud Computing in Healthcare to Improves Patient Care Coordination

The cloud has revolutionized the way we live and work. It has brought about a new era of flexibility and convenience, allowing us to access information and collaborate with others from anywhere in the world.

According to a Gartner survey, global spending on cloud services is projected to reach over $482 billion this year (2022). The numbers are much higher than those recorded last year, i.e., $313 billion.

Now, healthcare providers are taking advantage of this technology to improve patient care coordination. Adoption of Cloud Computing in Healthcare with Cloud-based applications can help healthcare organizations manage patients more effectively, share important data in a secured manner, and reduce costs.

Let’s first get on with the basics of cloud computing and move on to the benefits of cloud computing in healthcare.

What is Cloud Computing?

Cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale. 

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). This technology allows organizations to quickly scale up or down as needed and pay only for the resources they use.

It has many advantages over traditional on-premise IT infrastructure. With cloud computing, businesses can be more agile and responsive to change because they can provision new resources in minutes or hours instead of weeks or months.

Cloud computing offers greater scalability and enables businesses to scale up or down as they please, without having to make huge capital investments as they are required to pay only for the resources they use.

Cloud Computing in Healthcare

The healthcare industry is in a state of flux. With the ever-changing landscape of regulations, the increasing costs of care and the shift to value-based reimbursement models, healthcare organizations are looking for ways to cut costs and improve efficiency. One way they’re doing this is by adopting cloud-based solutions.

Cloud computing has already transformed many industries, and healthcare is next on the list. By moving to the cloud, healthcare organizations can reduce IT costs, improve patient care, and drive innovation.

Let’s look at how cloud computing is changing healthcare for the better.

  • Reducing IT Costs

One of the biggest benefits of adoption of cloud computing in healthcare is that it can help healthcare organizations reduce their IT costs. With on-premises solutions, organizations have to pay for hardware, software, maintenance, and support. But with cloud-based solutions, they only have to pay for what they use.

Additionally, cloud providers often offer discounts for long-term contracts or for paying upfront. As cloud providers handle maintenance and upgrades, healthcare organizations can further reduce costs.

  • Improving Patient Care 

Another benefit of cloud computing in healthcare is that it can help improve patient care. By moving to the cloud, healthcare organizations can improve collaboration between care team members, ultimately leading to better patient outcomes. 

In addition, patients are increasingly expecting more personalized and convenient care. And with the help of cloud-based solutions like telemedicine and patient portals, they’re getting it.

These solutions give patients 24/7 access to their health information and allow them to book appointments, refill prescriptions, and more from the comfort of their homes.

  • Improving Patient Experience 

Doctors and hospitals now have the ability to increase patient engagement and provide them with anywhere, anytime access to their medical data, test results, and even doctor’s notes thanks to adoption of cloud computing in healthcare. This gives patients more power and control, as well as increasing their knowledge of their medical conditions. Furthermore, because doctors can access the history of medical records, it provides a new level of safety for patients, preventing them from being overprescribed or avoiding unnecessary testing. 

  • Faster Deliver of Time- Critical Medical Services & Impact of Covid19 

Covid19 proved to be a powerful driver of rapid digital transformation across industries. Cloud computing in healthcare is cost-effective and quick to deploy, among other benefits that can be extremely useful, particularly during a pandemic. In many ways, the year 2020 has been unprecedented. Time was critical in combating the pandemic and constructing new hospitals, releasing the vaccine onto the market, and arranging a safe method of mass testing. Cloud computing should be used to help bring important technological solutions to market faster for Time – Critical Medical Services.

  • Data Security and Privacy 

Security and Privacy of patient data are the two most important factors that matter to healthcare providers and payers or for that matter any other healthcare stakeholder looking at leveraging software systems. All reputed cloud services providers undertake the required measures to ascertain that vulnerability of patient data to potential breach is either negative or as negligible as possible. It is, however, a moving target and it is advisable to hire the services of a reliable and experienced tech solution provider to address this concern while adopting cloud solutions.

  • Implementation of AI/ML & Access to Analytics for Data Driven Decisions 

Large datasets of patient information from ePHI, IoT devices, and consumer health applications are processed by cloud platforms. Technology can help to promote healthier patient behavior, improve disease detection rates, and aid in advanced diagnosis and decision making. Through data insights and analytics, AI/ML enables healthcare professionals to make data-driven decisions. It has the potential to personalize medicine, improve care, and deliver real-time information to patients and staff. Data from AI/ML is being used to drive innovation. Healthcare providers make better decisions with adopting cloud computing in healthcare, which improves service operations and increases hospital efficiency. Automated analytics provide significant benefits for patient scheduling, background checks, and managing all associated medical records. Data interoperability benefits research programmes by allowing researchers to quickly collect statistics from a diverse range of patients.

Cloud Computing Market & Opportunities in Health Care

Cloud Computing Makes Patient Care Coordination Easier

There is no denying that technology has revolutionized the healthcare industry. One of the most significant changes has been the move from paper-based systems to electronic health records (EHRs). This shift has resulted in more efficient and coordinated patient care.

However, as anyone who has ever dealt with a complex health issue knows, coordinating care can still be a major challenge. Many players are involved in the care of a single patient, including doctors, nurses, specialists, pharmacists, and others. It is difficult to track all the treatments and medications a patient takes. 

That’s where cloud computing comes in.

  • Cloud computing helps in storing and accessing data online. EHRs are often stored in the cloud, allowing different patient care team members to access them from anywhere at any time. This is a major advantage in coordinating care, as it allows everyone to see a patient’s complete medical history real time and in one place.
  • In addition to making information more readily available, cloud computing also enables sharing with ease large files such as x-rays or MRI scans. This is especially important when patients consult multiple specialists or receive care at different facilities.
  • In the past, patients would have to provide their records separately to each consulting physician, which was both time-consuming and inconvenient. With cloud computing, patients can give their doctors access to their records with just a few clicks.
  • Cloud computing in healthcare also makes it easier for doctors to communicate with each other. Earlier, if two doctors needed to discuss a patient’s diagnosis or treatment plan, they would have to do so by phone or fax (if they were lucky enough to have access to each other’s contact information). With secure cloud messaging platforms, doctors can easily send referrals, consult on cases, and request test results without ever having to pick up the phone.

Conclusion

Cloud computing in healthcare enables the doctors and other members of a patient’s care team to communicate and collaborate easily, resulting in more coordinated and efficient patient care. Adoption of cloud computing in healthcare also helps in implementing latest technologies like AI/ML.

If you are stuck with on premise healthcare software systems, now is the time to switch. Not only will you be able to improve patient care coordination, but you’ll also reap all the other benefits that a cloud infrastructure can provide you, leading to increased productivity and decreased costs.

If you are looking for a software technical partner who can assist you in developing custom solutions that are cloud-based, or in cloud migration, please reach out to us. With decades of experience, we have the necessary resources to help you achieve your goals. Connect with our experts today.

 

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The Impact of AR on Retail

The Impact of Augmented Reality on Retail

Augmented Reality (AR) is a technology that allows overlaying digital content like images, videos and 3D objects onto the real world, thereby give the illusion of being a part of it. One of the most famous examples of AR is Pokémon Go, which overlays a virtual Pokémon (a 3D cartoon character) onto the real world. AR also offers tremendous possibilities outside of the Gaming Industry, especially in Retail.

The adage “Customer Is God” is a golden rule. It isn’t surprising, therefore, that any business that solves its customer’s problems effectively, gets rewarded with the customer’s loyalty, money, and trust. AR is fast becoming an invaluable tool in the hands of Retail businesses that aim to constantly impress their customer base and stay ahead of the competition.

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Let’s look at some of the issues that concern the customers of this industry.

Customer Problems

With Online Retail:

  • High Time Consumption – Let’s say you order cloth from an e-commerce website. Typically it will take a few days for the product to reach you. You then gauge it on all the parameters that matter – size, color, texture, etc. If the product does not meet your expectations, you are likely to exchange the item, triggering the cycle to repeat.
  • Return Costs – If the business doesn’t bear the shipment cost of returns, customers are likely to pay for it.
  • Problems with large items – It requires a very vivid imagination to see how a new couch would look in a room. Will it look good with the rest of the furniture? Or will it even fit in the first place?

With Offline Retail:

  • Too much work – It takes a lot of time and energy going around dozens of stores and looking for the right items and then trying various permutations and combinations to check if they look well together.

With both Online and Offline Retail:

  • Un-try-able Products – Some products can’t be tried on. For example, It’s hard to imagine how a particular hair color would look on you, or if that dragon tattoo would be too much for you to carry.
  • Un-personalized Shopping Experience – Currently the preferences of a customer are unknown to the business. Consequently, the suggestions given to the customers are un-personalized and work on a hit-and-trial basis.
  • Hygiene Issues – Whether you have germophobia or otherwise, there is always a risk of contracting a disease owing to the dress being tried on earlier by an infected person.
  • It ain’t fun – Going from store to store, from one website and mobile app to another and trying on or imagining how every product would look like on you is an exhausting experience and is not fun for most. And in online retail, even after so much effort, one can never know if the product will turn out to be expected.

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Let’s look at some of the issues that concern the businesses of this industry.

Business Problems

With Online Retail:

  • Shipment Costs – The trade-off between bearing the shipment costs of product-returns vis-a-vis making the customers pay for it is a tough choice for any business.

Doing Offline Retail:

  • Compensation for salespeople – Since the whole process is very manual, from the salesperson showing the items to the customer to making the sale, a constant involvement is needed.

Problems common to Online and Offline Retail:

  • Conversion Rates – Due to a lack of personalized suggestions and ads, and a tiring shopping experience, conversion rates of businesses are lower than they can be.
  • Brand Awareness – Extensive marketing is needed for businesses to create awareness of their brands, and it is invariably a very expensive matter.
  • Customer Acquisition – Customer Acquisition costs eat up a big portion of a business’s profits. These are mainly un-targeted advertisements having low conversion rates.

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How AR helps solve these problems

Try And Buy Functionality:  AR can overlay any item onto the real world to make it seem like it is a part of it. Powerful Machine Learning (ML) algorithms can detect the face and body of a person in an image or even in real-time. An application using a combination of AR and ML can allow its users to try on a virtual version of any item they would like to buy, from the comfort of their houses. Another possible feature is the placement of virtual 3D models of furniture inside a user’s house. Such features will reduce the number of returns the buyer makes which helps save time and reduce the return.

Saves User’s Time and Energy:  as now they have the whole inventory of products available to them and they can try anything on with a click rather than manually trying every item on.

Eliminate Hygiene-related Problems: Trying items in this new way is much efficient and can be made as aesthetically appealing as needed, making the whole process a joyous experience for the user.

Increase Brand Awareness: Users can click a picture of them trying on an item and can share it on social media. This will lead to free marketing and increased brand awareness.

Automated Processes: For offline retail, the need for a salesperson is heavily reduced. A user will enter a fitting room with a screen instead of a mirror and a camera attached to the screen. Users can touch and select their choice of clothing from the screen and can try a virtual version of it instantly. If they like the item they can ask to try the real item on. Hence the need for a salesperson is reduced considerably.

Attracting Customers and Increasing Conversion Rates: A screen mounted with a camera-enabled with AR can show how someone standing in front of the mirror would look like wearing a certain item. Such a setup outside a retail store will attract flocks of customers who after seeing them trying on a virtual item would want to buy it if it looked good.

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Things To Know Before Introducing AR Into A Business

Accuracy: An AR experience that isn’t accurate will not be useful for the customers or the business. For example, a user won’t like if the sunglasses they’re trying on, fits on their forehead instead of eyes, or if the virtual couch they are trying to place doesn’t rest on the ground properly.

Speed: An AR experience must be fast and lag-free. Long loading time and high latency always drive the user away.

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If you have any queries in this field, talk to Mindfire Solutions. For over 20+ years now, we have been the preferred Software Development Partner of over 1000+ Small and Medium-sized enterprises across the globe.

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How To Overcome Cross-Cultural Issues In Global Software Outsourcing?

How to overcome cross-cultural issues in global software outsourcing

Software outsourcing has been a booming business since 1990’s. The reasons are reduced cost, access to wide range of labor markets and improved performance. Today a lot of software development occurs at offshore locations as companies get plenty of talented resource at low cost. Most software development vendors place small teams at client locations to smoothly execute projects. This may sometimes be troublesome if the person does not have some understanding of the country’s culture. Some particular societies tend to have distinct ways of working, and this may lead to cross-cultural issues while attempting collaboration or may sometimes lead to project failure. To avoid project failure  companies should learn to overcome cross-cultural issues in global software outsourcing.

Continue reading How To Overcome Cross-Cultural Issues In Global Software Outsourcing?

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