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A Step-by-Step Guide to Building Microservices in ASP.NET Core

Microservices architecture has gained immense popularity due to its ability to create loosely coupled, extensible, and independently deployable services that communicate through well-defined interfaces. In this article, we’ll delve into microservices architecture, explore its advantages and disadvantages, and demonstrate how to develop a simple microservice using ASP.NET Core. Future articles will cover implementing an API gateway and establishing interactions between microservices.

Before you begin, ensure that you have Visual Studio 2022 installed on your system. If not, you can download it here.

Understanding Microservices Architecture

Microservices refer to a software architecture where a large application is divided into multiple small, autonomous services. Each microservice is designed to perform specific tasks independently, and they work together as a cohesive whole.

In essence, a microservices-based application consists of decentralized, loosely coupled services that can be independently deployed and maintained. This approach offers several advantages:

1. Scalability: Microservices enable individual services to scale independently based on demand, enhancing overall system scalability.

2. Agile DevOps: Teams can independently develop and deploy services, leading to faster development cycles and facilitating continuous delivery and deployment in line with DevOps principles.

3. Fault Isolation and Resilience: In a microservices architecture, a failure in one service does not affect the entire application. The system is more resilient as it isolates faults and handles failures gracefully.

4. Technology Flexibility: Each microservice can use a different programming language, framework, and technology stack. This flexibility allows teams to choose the most suitable technology for each service.

5. Autonomous Teams: Microservices encourage small, cross-functional teams to work on individual services, promoting autonomy, efficiency, and focus.

However, there are potential drawbacks to consider:

1. Complexity: Microservices introduce a higher level of complexity compared to monolithic architecture. This includes challenges such as network latency, synchronous communication, eventual consistency, and distributed data management.

2. Operational Challenges: Managing and monitoring multiple services in a distributed environment requires additional tools for service discovery, monitoring, logging, and tracking.

3. Increased Development Effort: Developing and maintaining multiple individual services can require more effort compared to a monolithic architecture.

4. Data Management: Maintaining data consistency and transaction integrity is more complex in a distributed microservices environment.

Building a Microservice in ASP.NET Core

To demonstrate how to build a microservice in ASP.NET Core, we’ll create a simple Customer microservice. You can follow these steps to create additional microservices like Product and Supplier:

With these steps completed, you’ve created a minimalistic microservice. When you run the application and access the HTTP GET endpoint of the customer microservice, you’ll see customer data displayed in your web browser.

Microservices vs. Monolith

Microservices architecture stands in contrast to monolithic applications, where all business functionality is consolidated in a single process. With microservices, you break down your application into independently deployable services, allowing you to build, deploy, and manage each service separately.

In this article, we’ve demonstrated how to create a simple microservice in ASP.NET Core. In future articles on microservices architecture, we’ll explore using an API gateway for security enforcement and providing a single point of access to backend services. We’ll also cover implementing interactions between services to complete our microservices-based application. Stay tuned for more insights into building robust microservices.

GitHub Enterprise Server 3.10: Elevating Control and Security

GitHub Enterprise Server 3.10, unveiled on August 29th, brings a host of new features aimed at bolstering control, security, and compliance for both developers and administrators within enterprise settings. As GitHub’s premier self-hosted platform for enterprise-grade software development, this release introduces substantial enhancements that promise to streamline workflows and enhance protection.

One of the standout features of this release is the full-fledged availability of GitHub Projects, a powerful tool for planning and monitoring work. GitHub Projects provides users with a dynamic, spreadsheet-like workspace where they can effortlessly filter, sort, and group issues and pull requests, promoting efficient project management and collaboration.

Furthermore, GitHub Enterprise Server 3.10 introduces custom deployment protection rules tailored for GitHub Actions. This feature is designed to facilitate safe and controlled deployment processes, ensuring that software changes are implemented securely. Additionally, administrators gain enhanced policy control over runners, enabling more fine-grained management of job execution.

Security remains a top priority with this release, as GitHub introduces a user-friendly default setup experience for GitHub Advanced Security code scanning. This feature allows users to quickly identify vulnerabilities across all repositories with just a few clicks. Moreover, it offers a comprehensive view of security coverage and risk management at the enterprise level, empowering organizations to proactively address potential threats.

Developers can access GitHub Enterprise Server from either on-premises or cloud-based deployments via enterprise.github.com, and free trials are readily available for those looking to explore its capabilities.

It’s worth noting that GitHub Enterprise Server 3.10 lays the groundwork for the planned deprecation of team discussions in version 3.12. Users are notified of this upcoming change through a banner displayed in team discussions, which also provides a convenient link to migrate to GitHub Discussions. This strategic move ensures that GitHub continues to evolve and optimize its platform to meet the ever-changing needs of its user base.

Prior to this release, GitHub Enterprise Server 3.9 introduced significant enhancements to GitHub Projects on June 29th, followed by several subsequent point releases, reflecting GitHub’s commitment to regular updates and continuous improvement.

IBM Introduces Cutting-Edge Generative AI Foundation Models

IBM has taken a significant stride in the world of artificial intelligence with the introduction of its innovative generative AI foundation models and enhancements to the Watsonx.ai platform.

On September 7th, IBM unveiled the Granite series of foundation models, which utilize the powerful “Decoder” architecture to apply generative AI capabilities to both language and code-related tasks. These models are versatile and can support a wide range of enterprise-level natural language processing (NLP) tasks, including summarization, content generation, and insight extraction.

What sets IBM’s approach apart is its commitment to transparency. The company plans to provide a comprehensive list of data sources, along with detailed descriptions of the data processing and filtering steps used to create the training data for the Granite series. This transparency is a nod to IBM’s dedication to ensuring the integrity and quality of its AI models. The Granite series is set to become available later this month.

Furthermore, IBM is expanding its AI offerings by including third-party models on its Watsonx.ai platform. This move includes Meta’s Llama 2-chat 70 billion parameter model and the StarCoder LLM, designed for code generation within the IBM Cloud environment.

These Watsonx.ai models are trained on IBM’s enterprise-focused data lake, a testament to the company’s commitment to data quality and governance. IBM has implemented rigorous data collection processes and control points throughout the training process, which is crucial for deploying models and applications in areas such as governance, risk assessment, compliance, and bias mitigation.

IBM’s vision for the Watsonx platform doesn’t stop at foundation models; it includes several exciting capabilities:

Tuning Studio for Watsonx.ai: This tool offers a mechanism to fine-tune foundation models to cater to unique downstream tasks using enterprise-specific data. Tuning Studio is expected to launch this month.
Synthetic Data Generator for Watsonx.ai: This feature empowers users to create artificial tabular datasets from custom data schemes or internal datasets. It provides a safer way to extract insights for AI model training and fine-tuning, all while reducing data-related risks. Like Tuning Studio, this capability is also set to debut this month.
Watsonx.data Lakehouse Data Store: This data store will incorporate Watsonx.ai’s generative AI capabilities, making it easier for users to discover, visualize, and refine data through a natural language interface. It is scheduled to be available in preview in the fourth quarter of this year.
Watsonx.data Vector Database Integration: IBM plans to integrate vector database capabilities into Watsonx.data to support retrieval-augmented generation use cases. This feature is also expected to be available in preview in the fourth quarter.
Model Risk Governance for Generative AI: IBM is launching this as a tech preview for Watsonx.governance. It will enable clients to automate the collection of foundation model details and gain insights into model risk governance through informative dashboards integrated into their enterprise-wide AI workflows.

Beyond these innovations, IBM is seamlessly integrating Watsonx.ai enhancements into its hybrid cloud software and infrastructure. This includes:

Intelligent IT Automation: This feature, entering tech preview this week, leverages automation products like Instana and AIOps. It includes “Intelligent Remediation,” which employs Watsonx.ai generative AI foundation models to help IT ops practitioners summarize incident details and provides prescriptive workflow suggestions to address issues efficiently.
Developer Services for Watsonx: These services aim to bring Watsonx capabilities closer to data on IBM Power for SAP workloads. The SAP ABAP SDK for Watsonx will offer clients new ways to utilize AI for data inference and transaction processing on sensitive data. Expect these services to launch in the first quarter of 2024.

In conclusion, IBM’s latest advancements in generative AI foundation models and enhancements to the Watsonx.ai platform showcase the company’s commitment to transparency, data quality, and expanding the horizons of AI across a wide range of industries and applications. These developments are poised to empower enterprises with advanced AI capabilities and data-driven insights.

Adopting Cloud Smart: The New Era in IT Architecture

The era of “Cloud First” has evolved, giving way to a more nuanced approach known as Cloud Smart. In this shifting landscape of IT architecture, hybrid cloud, with a mix of on-premises and off-premises solutions, has become the default choice. It’s not merely a transitional phase en route to “cloud maturity” but rather a preferred state for many IT leaders and organizations.

Hybrid cloud’s appeal lies in its flexibility, enabling organizations to leverage existing data center infrastructure while harnessing the advantages of the cloud. This approach optimizes costs and extends on-premises IT capabilities, making it an attractive and sustainable solution.

Moreover, hybrid cloud is gaining popularity among predominantly on-premises organizations eager to tap into the latest cloud technologies. As businesses increasingly rely on advanced technologies like AI for faster and more efficient data processing and analysis, the cloud offers a scalable and cost-effective solution without the need for significant hardware investments, all while addressing cybersecurity concerns.

However, navigating this transition requires careful planning. Rushing into the cloud can lead to hasty decisions that result in negative returns on investment. Some organizations make the mistake of migrating the wrong workloads to the cloud, necessitating a costly backtrack.

In addition to financial setbacks, organizations that fail to adopt a well-thought-out cloud strategy find themselves unable to keep pace with the exponential growth of data. Rather than enhancing efficiency and productivity, they risk falling behind their competitors and missing out on the potential benefits of a successful cloud migration.

One common pitfall is the failure to involve infrastructure teams in the migration process, leading to a disjointed solution that hampers performance. Cloud projects are often spearheaded by software architects who may overlook the critical infrastructure aspects that impact performance. It’s crucial to strike the right balance by aligning infrastructure and software architecture teams, fostering better communication to optimize hybrid cloud deployments.

The urgency to address these challenges is pressing, given the increasing demand for hybrid cloud solutions. Over three-quarters of enterprises now use multiple cloud providers, with one-third having more than half of their workloads in the cloud. Moreover, both on-premises and public cloud investments are expected to grow, with substantial spending on public cloud services projected by Gartner.

The Growing Demand for Hybrid Cloud

Hybrid cloud empowers organizations to harness the advantages of both public and private clouds, providing flexibility in hosting workloads. This flexibility optimizes resource allocation and enhances cloud infrastructure performance, contributing to cost savings.

Furthermore, hybrid cloud allows organizations to leverage the security benefits of both public and private clouds, offering greater control and advanced security approaches as needed. Many organizations also turn to hybrid cloud to rein in escalating monthly public cloud bills, especially when dealing with cloud sprawl and storage costs.

The “pay as you go” model is a boon, provided organizations understand how to manage it effectively, particularly in the case of long-lived and steadily growing storage needs.

In conclusion, “Cloud First” is giving way to “Cloud Smart.” This shift acknowledges the importance of optimizing the on-premises and cloud-based IT infrastructure. A “Cloud Smart” architectural approach empowers enterprises to design adaptable, resilient solutions that align with their evolving business needs. Striking the right balance between on-premises and cloud solutions ensures optimal performance, reliability, and cost-efficiency, ultimately driving better long-term outcomes for organizations.

Python: The Versatile Powerhouse of Programming

In the world of software development, Python has transcended its initial role as a simple scripting language to become a cornerstone of modern programming. What was once considered a tool for automating mundane tasks or rapidly prototyping applications has now evolved into a first-class player in software development, infrastructure management, and data analysis. Python is no longer confined to the shadows; it’s at the forefront of web application development, systems administration, and the driving force behind the booming fields of data science, machine learning, and generative AI.

Python’s Key Strengths

Let’s delve into some of the key strengths that have fueled Python’s meteoric rise among both novice and expert programmers.

Ease of Learning and Use: Python boasts a concise feature set, requiring minimal time and effort to produce your first programs. Its syntax is intentionally designed for readability and simplicity, making it an ideal language for newcomers. Python allows developers to focus on problem-solving rather than wrestling with complex syntax or deciphering legacy code.
Wide Adoption and Support: Python’s popularity is undeniable, evident in its high rankings on indexes like the Tiobe Index and its extensive presence on GitHub. Python runs seamlessly on major operating systems and platforms, with support extending even to minor ones. Many major libraries and API-powered services offer Python bindings or wrappers, ensuring effortless integration.
Versatility: Beyond scripting and automation, Python is used to create professional-quality software, including standalone applications and web services. While Python may not be the fastest language, its adaptability compensates for any speed limitations.
Continuous Advancements: Python consistently evolves with each new release. Features such as asynchronous operations and coroutines have become standard, simplifying the development of concurrent applications. Type hints improve program logic analysis, reducing complexity in this dynamic language. The CPython runtime, Python’s default implementation, is also being redesigned for enhanced speed and parallelism.

Python’s Diverse Applications

Python’s utility extends far beyond basic scripting and automation:

General Application Programming: Python allows the creation of command-line and cross-platform GUI applications deployable as self-contained executables. Third-party packages like PyInstaller and Nuitka enable the generation of standalone binaries from Python scripts.
Data Science and Machine Learning: Python is a star player in sophisticated data analysis, boasting interfaces to the vast majority of data science and machine learning libraries. It serves as the go-to language for high-level command interfaces for these domains.
Web Services and RESTful APIs: Python’s native libraries and third-party web frameworks simplify the creation of everything from simple REST APIs to complex data-driven websites. Recent Python versions offer robust support for asynchronous operations, enabling high request throughput.
Metaprogramming and Code Generation: Python’s object-oriented nature allows it to function efficiently as a code generator, facilitating the development of applications that manipulate their own functions and offer remarkable extensibility. It can also drive code-generation systems like LLVM to create code in other languages.
Glue Code: Python’s versatility shines as a “glue language,” enabling the interoperation of disparate code, particularly libraries with C language interfaces. It acts as a bridge, connecting applications or program domains that cannot communicate directly.

Python’s Limitations

However, Python is not without its limitations. It may not be the best choice for certain tasks:

System-Level Programming: Python’s high-level nature makes it unsuitable for tasks like developing device drivers or operating system kernels.
Cross-Platform Standalone Binaries: While possible, creating standalone Python applications for Windows, macOS, and Linux can be complex and inelegant.
Mobile-Native Applications: Developing mobile-native applications in Python is not as straightforward as using languages like Swift or Kotlin, which have native toolchains for mobile platforms.
High-Speed Applications: When speed is paramount in every aspect of an application, other languages like C/C++ or Rust may be better suited. Nevertheless, Python can often achieve competitive speeds by wrapping libraries written in these languages.

In conclusion, Python has transcended its humble beginnings to become a versatile and indispensable tool in the modern programming landscape. Its ease of use, wide adoption, continuous evolution, and diverse applications make it a powerhouse in software development, data analysis, and beyond. While it may not excel in every domain, Python’s adaptability and extensive library support ensure its enduring relevance in the world of programming.

How AI Will Impact The Developers Experience

The rapid advancements in Artificial Intelligence (AI) have already made a profound impact on various industries, and software development is no exception. AI is gradually transforming the way developers work, enhancing their productivity, and streamlining the development process. This article delves into the profound impact of AI on the developer experience and how it is reshaping the future of software development.

Intelligent Code Assistance

One of the most significant ways AI is impacting the developer experience is through intelligent code assistance tools. These AI-powered code editors analyze vast amounts of code repositories, learning patterns, and best practices to suggest code completions, identify errors, and offer helpful insights. AI-driven autocomplete and suggestions not only speed up coding but also improve code quality by reducing typos and potential bugs.

Moreover, AI can assist developers in refactoring code, suggesting more efficient algorithms, and even predicting possible issues, making the development process more efficient and seamless.

Automated Testing and Debugging

Testing and debugging are critical but time-consuming aspects of software development. AI-powered testing frameworks and debugging tools can automate much of this process. AI algorithms can detect patterns in test data and generate additional test cases, increasing code coverage and reducing the likelihood of undiscovered bugs.

AI-driven debugging tools can analyze error logs and stack traces to pinpoint the root causes of issues more accurately, saving developers valuable time that would otherwise be spent manually sifting through logs and code.

Natural Language Processing (NLP) for Documentation

AI and Natural Language Processing (NLP) are transforming documentation processes. Instead of relying solely on traditional text-based documentation, developers can now interact with AI-powered chatbots and virtual assistants. These assistants can answer queries, offer code examples, and provide contextual explanations in a conversational manner. This not only simplifies the learning process for new developers but also helps experienced developers find solutions more quickly.

Code Generation and AutoML

AI has also paved the way for the development of AI-generated code itself. AutoML (Automated Machine Learning) platforms use AI algorithms to generate and optimize machine learning models without requiring extensive manual intervention. This enables developers with little AI expertise to incorporate machine learning functionalities into their applications.

Furthermore, AI-driven code generation is becoming more sophisticated, where AI systems can create code snippets based on high-level requirements or descriptions. This streamlines the development process, especially for repetitive tasks, and allows developers to focus on more complex and creative aspects of their projects.

Continuous Integration and Deployment (CI/CD)

AI can significantly enhance the Continuous Integration and Deployment (CI/CD) pipeline. By analyzing past deployment data and code changes, AI can predict potential issues and their impacts on production environments. This foresight helps developers avoid potential bottlenecks and ensures smoother and more reliable releases.

Additionally, AI-driven automation tools can optimize resource allocation and scaling decisions based on real-time usage patterns, ensuring applications perform optimally under varying workloads.

Personalized Development Environments

AI can create personalized development environments for individual developers. These AI-powered IDEs (Integrated Development Environments) learn from developers coding habits, preferences, and past code patterns to offer tailored suggestions and shortcuts. This level of personalization not only boosts productivity but also provides a more enjoyable and efficient coding experience.

Conclusion

As AI continues to evolve, its integration into the software development process is revolutionizing the developer experience. From assisting in code completion and debugging to automating testing and providing personalized development environments, AI is becoming an indispensable ally for developers worldwide.

As the technology matures, it is essential to strike a balance between leveraging AI’s capabilities and preserving human creativity and intuition. The future of software development lies in embracing AI as a collaborative partner that empowers developers to build innovative and cutting-edge solutions more efficiently than ever before.

Overture Maps Foundation challenging Google Maps and Apple Maps

Overture Maps Foundation Unleash Open Map Dataset to Challenge Google Maps and Apple Maps

The Overture Maps Foundation, a collaborative effort involving Meta, Microsoft, Amazon, and mapping company TomTom, has taken a significant stride in challenging the dominance of Google Maps and Apple Maps. Established last year, the group aims to empower developers by providing them with the data necessary to create their own maps and navigation products.

In a recent development, the Overture Maps Foundation has released its first open map dataset. This dataset includes a vast amount of valuable information, comprising 59 million “points of interest,” such as restaurants and landmarks, as well as details about transportation networks and administrative boundaries. Meta and Microsoft have played a crucial role in collecting and donating this data to the foundation.

The release of this dataset allows third-party developers to build their own mapping and navigation solutions, effectively breaking the stronghold of the Apple-Google duopoly. A key aspect of the data is the “Places dataset,” which offers an unprecedented open dataset, enabling the mapping of various entities worldwide, from large and small businesses to pop-up street markets.

Marc Prioleau, the executive director of the Overture Maps Foundation, emphasized the significance of this release in establishing a comprehensive and high-quality open map dataset that can adapt to our ever-changing world. He also highlighted the ongoing challenge of maintaining data accuracy to meet user expectations. To tackle this challenge, Overture plans to foster a collaborative environment that can continually update and expand its database of points of interest.

Since its initial announcement, the Overture Maps Foundation has outlined its long-term vision, which includes expanding the dataset to encompass more places, routing and navigation information, and 3D building data.

The primary objective of the foundation is to simplify and reduce the cost burden for developers seeking to create mapping applications. Presently, developers often have to pay fees to access Google Maps’ API, while Apple Maps, although free for native app developers, requires payments from web app developers.

The availability of map and location data holds immense significance in various domains, powering applications ranging from IoT devices and self-driving cars to logistics and big data visualization tools. Previously, access to this data was largely limited to major corporations, restricting the capabilities and features accessible to other companies. With the Overture Maps Foundation’s initiative, a more open and collaborative approach aims to unleash the potential of mapping data for a wider array of applications and industries.

The Future of Software: Building Products with Privacy at the Core

In an era where personal data has become one of the most valuable commodities, the need for software products that prioritize privacy has never been greater. As technology continues to advance and data breaches become more common, users are becoming increasingly concerned about the security of their personal information. In response to this growing demand, the future of software lies in building products with privacy at their core.

Privacy has emerged as a fundamental right in the digital age. Users expect their data to be handled responsibly and protected from unauthorized access. Software developers and companies have a responsibility to prioritize privacy and implement robust security measures to safeguard user information.

Building products with privacy at the core involves several key considerations. Firstly, data minimization is crucial. This means collecting only the necessary data required for the product’s functionality and ensuring that any additional data is anonymized or encrypted. By minimizing the collection and storage of personal information, software developers can mitigate the risk of data breaches and unauthorized access.

Another important aspect is transparency. Users should have clear visibility into how their data is being collected, used, and shared. This can be achieved through user-friendly privacy policies and consent mechanisms that provide individuals with a comprehensive understanding of the data practices employed by the software product.

Furthermore, privacy by design is a critical principle for the future of software. This approach involves integrating privacy considerations into the development process from the outset. It means implementing privacy controls and security measures as the default settings rather than as an afterthought. By incorporating privacy into the design of software products, developers can ensure that privacy is not an optional feature but a fundamental aspect of the user experience.

An essential aspect of building privacy-centric software is robust data protection. This includes adopting strong encryption practices to safeguard data both during transit and at rest. Encryption ensures that even if data is intercepted or accessed by unauthorized individuals, it remains unreadable and unusable. Additionally, implementing stringent access controls, such as multi-factor authentication and role-based permissions, helps prevent unauthorized access to sensitive data.

The future of software also involves embracing emerging technologies that enhance privacy. One such technology is homomorphic encryption, which allows computations to be performed on encrypted data without the need for decryption. This ensures that sensitive information remains protected even during data processing, opening up new possibilities for secure cloud computing and data analysis.

Blockchain technology, known for its decentralized and immutable nature, can also play a role in enhancing privacy. By leveraging blockchain, software developers can create transparent yet privacy-preserving systems that give users greater control over their data and enable secure and auditable transactions.

Furthermore, advancements in artificial intelligence and machine learning can be harnessed to enhance privacy through privacy-preserving algorithms. Techniques such as federated learning, differential privacy, and secure multi-party computation enable data analysis while preserving the privacy of individual user data.

As the demand for privacy-centric software grows, it is important for companies to adopt privacy as a core value and a competitive advantage. By building products that respect user privacy, companies can build trust, foster user loyalty, and differentiate themselves in the market. Furthermore, regulatory frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have emphasized the importance of privacy, making it essential for companies to comply with these regulations to avoid legal consequences.

In conclusion, the future of software lies in building products with privacy at the core. By prioritizing data minimization, transparency, privacy by design, and robust data protection, software developers can create products that meet the evolving expectations of users in an increasingly privacy-conscious world. Embracing emerging technologies and complying with privacy regulations will be vital for companies to thrive and succeed in the software landscape of the future.

Efficiency Unlocked: Automated Software Testing for Continuous Delivery

The need for a faster testing approach arose when manual testing began to break down due to the pressure of development and new emerging methodologies. When software was released every six months to a year with just a few new features, all was good. But then, we started making things complex and reduced the release cycles to every 15 days. With new development technologies and methods like Agile and Insprint, a tool was required to share the testers’ load. Hence, automation testing tools were born.

Today automated software can be found throughout all testing phases, helping the testers reduce time and effort. In this post, we will look at one such testing domain that has reduced the time to delivery by incorporating techniques that include an automation testing approach.

What is automation testing?

Efficiency unlocked: Automated software testing for continuous delivery — Automation testingSource: LambdaTest

Automated testing is a technique in which software (called automation software) performs the execution of actions that were earlier done manually. For example, launching the browser, entering the text into fields, sending data through APIs, etc. This is made possible through test scripts that contain these commands, which are converted to actions by a tool.

Automated testing is generally applied to areas where repetitive work is taking a toll on the testers. For instance, constantly verifying hundreds of input data into a field is something no tester would want to do every fifteen days. We can invest this time into other high-value tasks, which can be made possible only through automated testing.

Continuous delivery and its relation with automated testing

Continuous delivery is the phase we encounter after continuous integration. In continuous integration (or simply CI), we integrate the new code into the existing code base and verify whether the integrated code works well. Once this phase is passed, continuous delivery (or simply CD) is triggered, where we release the software to the users. This includes all the features created and merged between two continuous delivery cycles.

The process of CI and CD can be done manually – meaning we must integrate the code manually and then run multiple tests. Lastly, we push the changes to production and release them to the end-users manually as well (including the diffs and merge conflicts). If we could implement automation into this cycle, this complete task can be run on auto-pilot, and we can focus on other testing domains.

Automated software testing for continuous delivery creates a pipeline in which the first part is CI, and the successive part is the CD. The pipeline is then attached to the normal SDLC flow, where a developer can push the code into the pipeline, and the pipeline can take care of the rest. The actual work of testers comes into scripting those automated test cases for quick delivery.

Best practices for automated software testing for continuous delivery

Here are a few best practices to help you run the software with the pipeline smoothly.

1. Choosing an appropriate tool

Firstly, always prioritize the best tool for your requirements rather than jumping directly on creating test cases. An automation tool that can integrate CI and CD with precision and efficiency can increase the output of the team manifolds.

2. Bring everything into the cloud

Cloud technology has improved and optimized a lot in recent years. It provides essential benefits, especially in an environment where more people collaborate and work together. If your arrangement is in the cloud, you can work from anywhere, any place, and with any system. But choosing a good tool is important in this case.

When we need to bring automated software testing for continuous delivery into the cloud, we need to ensure that the cloud tool supports all these technologies. These may include automation framework support, automation programming languages support, and CI/CD tools support. Cloud testing platforms like LambdaTest may be beneficial and help you shed off all the infrastructure load from on-premise to the cloud.

LambdaTest is a digital experience testing platform that enables devs and QA engineers to perform manual and automated testing of web and mobile applications. It offers testing support with supported automated frameworks and tools like Selenium, Cypress, Playwright, Appium, etc. The real device cloud by LambdaTest lets you test web and mobile app testing in real-world conditions.

3. Implement continuous monitoring

When continuous delivery is implemented and automated, much of the code gets pushed without any manual intervention. This is good, but only as long as all the code is good and properly tested. However, we cannot be ensured in software development, and mishaps can happen at every stage.

Due to this, we implement various checkpoints to keep things in check. If you are performing automated testing for continuous delivery, monitoring is a must and should consistently be implemented. This can also be made automated if the cloud-based tool supports it.

4. Keep the team updated

Finally, keep the team updated about everything that goes through the CI/CD. Even if everything is “green,” it should be tallied with the team so they know about the health of the pipeline. The same goes for failure, as the team can quickly debug any problems.

Automated testing and continuous delivery are two words that we hear often when testing software, as they have become an integral part of the complete system. Automated testing is used throughout almost all domains, while continuous delivery ensures software releases can be quicker and smoother than before. Combining these two methodologies gives us a robust system where software delivery becomes automated, and all we need to do is to push the code in the pipeline.

It goes without saying that it is best to do this efficiently using the best practices experienced by the testers. In the end, don’t forget to combine a cloud-based tool that supports automation and continuous delivery to make your system robust and leverage the powers of the cloud from any system.

WordPress Introduces Jetpack AI Assistant for Enhanced Blog Post Creation and Editing

WordPress has unveiled a new AI-powered writing assistant named Jetpack AI Assistant, designed to assist users in the creation and editing of blog posts. This tool is now readily accessible on WordPress.com and seamlessly integrates into the platform’s editor interface.

Even users with WordPress blogs hosted on other platforms can benefit from the Jetpack plugin, which not only grants access to the AI assistant but also offers additional features to enhance marketing efforts, bolster security measures, and combat spam.

Jetpack AI Assistant utilizes generative AI technology similar to OpenAI’s large language model (LLM) chatbot, ChatGPT. However, specific details about the AI architecture employed are not provided in the company’s blog post.

Similar to its counterparts, Jetpack relies on user prompts to generate content. Additionally, the AI assistant has the capability to adapt and refine text to align with specific tones and styles, enabling writers to tailor their writing to resonate effectively with their target audience.

Currently, Jetpack AI Assistant supports 12 languages, including English, Hindi, Spanish, French, Chinese, and Korean. The tool boasts automatic rectification of spelling and grammar errors while facilitating seamless translation among the supported languages.

Automattic, the company behind WordPress and Jetpack AI Assistant, believes that this functionality empowers writers to create content in their native language while providing them the means to market their content in multiple languages.

Utilizing Generative AI for Tailored Content Delivery

The company characterizes the tool as a “creative writing partner” that empowers users to effortlessly generate diverse content, substantially streamlining the content creation process.

With Jetpack AI Assistant, users can summarize a blog post into a headline and adjust the tone of their text by selecting options such as “formal,” “provocative,” or “humorous.”

If users prefer to write their post, they can still use Jetpack AI Assistant to generate a headline based on their writing.

Moreover, the company asserts that the assistant surpasses the standard built-in tools in WordPress by offering advanced spelling and grammar correction features.

WordPress’s introduction of this generative AI feature seems well aligned with the prevailing trend of companies embracing AI-powered assistants for business automation and content creation. Notably, OpenAI’s AI models have quickly gained adoption by various companies, including Grammarly Inc.’s GrammarlyGo and Microsoft Corp.’s Office365 products, such as Word.

Users can avail themselves of a complimentary Jetpack AI Assistant block trial, allowing for up to 20 requests. However, to continue using the feature after that, there is a monthly fee of $10.

The Future of Content Development in Changing Tech Culture

Responding to the WordPress news, Josh Koenig, co-founder & chief strategy officer at WebOps platform Pantheon, said that the ability to generate text and image content has become indispensable in the age of large language models (LLMs).

He stated that while AI will greatly accelerate content creation, it cannot completely replace content creators.

“In a world where it becomes virtually free to produce large amounts of average/mediocre content, human creativity, and insight will still be needed to create something truly excellent that can break through,” Koenig told VentureBeat. “With AI taking over more of the grunt work of putting together individual insights, creators’ work will become more interesting and strategic. Creators will need to upskill, as more and more purely operational tasks are automated. The human value-add will be in creativity and decision-making.”