Limitations of Unit Testing Download now Prevent Logical bugs in your database calls, queues and external APIs or services Book a Demo

Stateful vs. stateless architecture: Understand key differences, pros, and cons to make informed decisions for scalable, efficient systems. 7 March 2025 09 Min. Read Stateful vs Stateless Architecture: Guide for Leaders WhatsApp LinkedIn X (Twitter) Copy link Choose the right architecture @DevOpsGuru: "Hot take: stateless services are ALWAYS the right choice. Your architecture should be cattle, not pets." @SystemsArchitect: "Spoken like someone who's never built a high-throughput trading system. Try telling that to my 2ms latency requirements." @CloudNative23: "Both of you are right in different contexts. The question isn't which is 'better' - it's about making intentional tradeoffs." After 15+ years architecting systems that range from global payment platforms to real-time analytics engines, I've learned one truth: dogmatic architecture decisions are rarely the right ones. The stateful vs. stateless debate has unfortunately become one of those religious wars in our industry, but the reality is far more nuanced. The Fundamentals: What we're really talking about? Let's level-set on what these terms actually mean in practice. In the trenches, here's what this actually means for your team: Stateless Services Any instance can handle any request Instances are replaceable without data loss Horizontal scaling is straightforward Stateful Services Specific instances own specific data Instance failure requires data recovery Scaling requires data rebalancing Real Talk: Where I've seen each shine? ➡️ When Stateless Architecture Was the Clear Winner Back in 2018, I was leading engineering at a SaaS company hitting explosive growth. Our monolithic application was crumbling under load, with database connections maxed out and response times climbing. We identified our authentication flow as a perfect candidate for extraction into a stateless service. Here's what happened: Before : 3-second p95 response time, maximum 5,000 concurrent users After : 200ms p95 response time, handles 50,000+ concurrent users The key was offloading session state to Redis and making the service itself completely stateless. Any instance could validate any token, allowing us to scale horizontally with simple auto-scaling rules. ➡️ When Stateful Architecture Saved the Day Contrast that with a real-time bidding platform I architected for an adtech company. We had milliseconds to process bid requests, and network hops to external databases were killing our latency. We reimagined the system with stateful services that kept hot data in memory, with careful sharding and replication: The business impact was immediate - the improved latency meant we could participate in more bid opportunities and win more auctions. Metric Original Stateless Design Stateful Redesign Improvement Average Latency 28ms 4ms 85.7% 99th Percentile Latency 120ms 12ms 90% Throughput (requests/sec) 15,000 85,000 466.7% Infrastructure Cost $42,000/month $28,000/month 33.3% Bid Win Rate 17.2% 23.8% 38.4% The Hybrid Truth: What nobody tells you? Here's what 15 years of architectural battle scars have taught me: the most successful systems combine elements of both approaches. "It's not about being stateful OR stateless - it's about being stateful WHERE IT MATTERS." Let's look at a common pattern I've implemented multiple times: In this pattern, the majority of the system is stateless, but we strategically introduce stateful components where they deliver the most value - typically in areas requiring: Ultra-low latency access to data Complex aggregations across many data points Specialized processing that benefits from locality The Testing Paradox: Where Both Approaches Fail ➡️ Stateless Testing Pain Points Dependency Explosion : Each service requires mocked dependencies Choreography Complexity : Testing event sequences across services Environment Consistency : Ensuring identical test conditions across CI/CD pipelines Data Setup Overhead : Seeding external databases/caches before each test Example: E-Commerce Order Processing Order Service → Inventory Service → Payment Service → Shipping Service → Notification Service Problem: A simple order flow requires 5 separate services to be coordinated, with 4 integration points that must be mocked or deployed in test environments. ➡️ Stateful Testing Pain Points State Initialization : Setting up precise application state for each test case Non-determinism : Race conditions and timing issues in state transitions Snapshot Verification : Validating the correctness of internal state Test Isolation : Preventing test state from bleeding across test cases Example: Real-time Analytics Dashboard User Session (with cached aggregations) → In-memory Analytics Store → Time-series Processing Engine Problem: Tests require precise seeding of in-memory state with complex data structures that must be identically replicated across test runs. Let me walk you through a real-world scenario I encountered last year with a fintech client. They built a payment processing pipeline handling over $2B in annual transactions: Their testing challenges were immense: Setup Complexity : 20+ minutes to set up test databases, message queues, and external service mocks Flaky Tests : ~30% of CI pipeline failures were due to test environment inconsistencies Long Feedback Cycles : Developers waited 35 minutes (average) for test results Environment Drift : Production bugs that "couldn't happen in test" When a critical bug appeared in the payment authorization flow, it took them 3 days to reliably reproduce it in their test environment. Decision Framework: Questions I Ask My Teams When making architectural decisions with my teams, I guide them through these key questions: What is the business impact of latency in this component? Each additional 100ms of latency reduces conversions by ~7% in consumer applications For internal tools, user productivity usually drops when responses exceed 1 second What is our scaling pattern? Predictable, steady growth favors optimized stateful designs Spiky, unpredictable traffic favors elastic stateless designs What is our team's operational maturity? Stateful systems generally require more sophisticated operational practices What happens if we lose state? Can we reconstruct it? How long would that take? What's the business impact during recovery? How will we test this effectively? What testing challenges are we prepared to address? How much development velocity are we willing to sacrifice for testing? Introducing HyperTest : The Game Changer HyperTest works like a "flight recorder" for your application, fundamentally changing how we approach testing complex distributed systems: How HyperTest Transforms Testing For the payment processing example above: Capturing the Complex Flow Records API requests with complete payloads Logs database queries and their results Captures external service calls to payment gateways Records ORM operations and transaction data Tracks async message publishing Effortless Replay Testing Select specific traces from production or staging Replay exact requests with identical timing Automatically mock all external dependencies Run with real data but without external connections Real-World Impact Setup time : Reduced from 20+ minutes to seconds Test reliability : Flaky tests reduced by 87% Feedback cycle : Developer testing cut from 35 minutes to 2 minutes Bug reproduction : Critical issues reproduced in minutes, not days Get a demo now and experience how seamless it becomes to test your stateful apps Key Takeaways for Engineering Leaders Reject religious debates about architecture patterns - focus on business outcomes Map your state requirements to business value - be stateful where it creates differentiation Start simple but plan for evolution - most successful architectures grow more sophisticated over time Measure what matters - collect baseline performance metrics before making big architectural shifts Build competency in both paradigms - your team needs a diverse toolkit, not a single hammer Invest in testing innovation - consider approaches like HyperTest that transcend the stateful/stateless testing divide Your Experience? I've shared my journey with stateful and stateless architectures over 15+ years, but I'd love to hear about your experiences. What patterns have you found most successful? How are you addressing the testing challenges inherent in your architecture? Dave Winters is a Chief Architect with 15+ years of experience building distributed systems at scale. He has led engineering teams at fintech, adtech, and enterprise SaaS companies, and now advises CIOs and CTOs on strategic architecture decisions. Related to Integration Testing Frequently Asked Questions 1. What is the key difference between stateful and stateless architecture? Stateful architecture retains user session data, while stateless processes each request independently without storing past interactions. 2. When should you choose stateful over stateless architecture? Choose stateful for applications requiring continuous user sessions, like banking or gaming, and stateless for scalable web services and APIs. 3. How does stateless architecture improve scalability? Stateless systems distribute requests across multiple servers without session dependency, enabling easier scaling and load balancing. For your next read Dive deeper with these related posts! 07 Min. Read Choosing the right monitoring tools: Guide for Tech Teams Learn More 09 Min. Read CI/CD tools showdown: Is Jenkins still the best choice? Learn More 08 Min. Read How can engineering teams identify and fix flaky tests? Learn More

Discover the differences between gRPC and REST, comparing speed, efficiency, and use cases to find the best fit for your application. 28 October 2024 09 Min. Read gRPC vs. REST: Which is Faster, Efficient, and Better? WhatsApp LinkedIn X (Twitter) Copy link Get Started with HyperTest Microservices teams frequently face challenges in choosing the best communication method for their services, mainly between gRPC and REST. Understanding the pros and cons of both options is crucial to ensuring smooth data exchange and quick responses. The discussion should emphasize the main differences between gRPC and REST. It will help you understand what is faster and better for your software projects. Before we move ahead, if you want to learn about an approach that takes away the pain of manually preparing test data and managing different environments, here's the solution you've been looking for: What is gRPC? gRPC stands for gRPC Remote Procedure Calls. It’s an open-source framework developed by Google to help you build high-performance distributed systems. It simplifies communication between your client and server applications. It uses HTTP/2, which gives you advantages like better data handling, streaming options, and improved flow control. This means your projects can run more smoothly and efficiently. Some of the key features of gRPC that you should know: gRPC is made to create distributed systems that run efficiently. This brings benefits like better data handling and streaming capabilities. It uses Protocol Buffers to serialize data effectively, making it easier to send. gRPC works with a variety of programming languages, so you can use it in different projects. You can choose between single request-response or continuous data streams for your communication. Clients and servers can exchange messages independently, allowing for real-time interaction. It has important features like load balancing, authentication, and encryption to enhance security and performance. What is REST? REST is abbreviated as Representational State Transfer. It is a way to design networked applications. It uses standard HTTP methods to help clients and servers communicate. Here are the main points: Each request from a client includes all the information the server needs to respond, which helps with scalability. This separates the client and server, allowing them to develop independently. REST works with resources identified by URLs and interacts with them using methods like GET. It provides a consistent way to interact with resources, making API design simpler. Resources can be shown in various formats, like JSON or XML. Feature gRPC REST Protocol Uses HTTP/2 Uses HTTP/1.1 Data Format Protocol Buffers (binary) JSON or XML (text-based) Performance Faster due to multiplexing and binary format Slower due to text parsing and larger payloads Streaming Supports bidirectional streaming Typically, stateless and request/response only Error Handling Uses status codes and messages defined in Protocol Buffers Standard HTTP status codes Tooling Fewer specialized tools, but growing Mature ecosystem with many tools available Language Support Strong support for multiple languages Supported in virtually all programming languages Caching Limited due to binary format Leverages HTTP caching mechanisms Use Cases Ideal for microservices, real-time applications Suitable for web applications and public APIs gRPC vs. REST- Performance Comparison When you are deciding between gRPC and REST, performance is a critical factor. This section explores speed, efficiency, and latency to help guide your project choices. Speed gRPC REST Speed often plays a big role in how well an API performs. gRPC, which uses HTTP/2, has significant advantages here. It allows multiple requests and responses to be sent simultaneously over a single connection. This reduces the time it takes to set up new connections, leading to faster response times for you. In contrast, REST usually operates on HTTP/1.1 and can face slowdowns because each interaction often requires a new connection. This adds latency. While you can improve REST with methods like caching and connection pooling, gRPC typically provides quicker data exchange due to its use of Protocol Buffers, which are more compact than the JSON used in REST. Efficiency gRPC REST Efficiency in data transmission is another area where gRPC shines. Its use of Protocol Buffers means smaller payload sizes, which reduces the amount of data you need to send over the network. This compactness speeds up communication and lowers bandwidth usage, making it especially useful in mobile or low-bandwidth situations. While REST is versatile and widely used, it often sends larger JSON payloads. This can lead to increased latency and higher resource consumption, especially when dealing with complex data or large datasets. As a developer, you should consider how efficient your chosen protocol will be based on the type of applications you are building and the expected data sizes. Latency gRPC REST Latency can significantly impact your user experience and overall system performance. In real-world scenarios, gRPC often shows lower latency compared to REST. For example, if you are working on applications that require real-time data streaming, like video conferencing or online gaming, you will find that gRPC’s efficient communication model makes a difference. Its ability to handle bidirectional streaming allows for immediate data exchange, improving responsiveness. On the other hand, REST may introduce delays in situations that need frequent updates or fast data exchange, such as stock price updates or live notifications. The need to establish new connections and larger payloads can slow things down in these cases, affecting your application’s performance. When to Use gRPC vs REST? Deciding whether to use gRPC or REST ultimately depends on your application's specific requirements, performance needs, and the nature of the services being utilized. By comprehending the advantages of each choice, you can make a better decision that fits your structure and objectives. Think about what is most effective for you and your team to ensure seamless progress in your project. ➡️When to Use gRPC? gRPC is great for: Microservices Architectures: It helps different services communicate efficiently. Real-Time Streaming: It works well for applications like chat and online gaming that need fast, two-way data flow. High-Performance Applications: It's suitable for low-latency needs, like video conferencing and trading platforms. Mobile Applications: It reduces bandwidth usage with smaller data packets. Complex Data Types: It handles complex data structures effectively using Protocol Buffers. ➡️When to Use REST REST is effective for: Public APIs: Accessible and easy to use for third-party developers. Web Applications: Fits well with CRUD operations in traditional web environments. Caching Needs: Leverages HTTP caching to enhance performance. Document-Based Interactions: Clear resource-oriented structure for handling documents. Simplicity and Familiarity: Easier for teams experienced with REST, benefiting from extensive documentation. Testing Challenges in API Development Developing APIs presents challenges that can impact the quality and dependability of your services. Here are some typical challenges you may encounter when testing REST APIs and gRPC. Common Testing Issues with REST APIs Inconsistent Responses: You may find that different API endpoints return data in various formats or structures, making it tough to test effectively. Authentication and Authorization: Verifying user credentials and ensuring proper access control can complicate your testing scenarios. Rate Limiting: Many APIs implement rate limiting, which can restrict your ability to conduct thorough tests without hitting those limits. Error Handling: It can be challenging to test how APIs handle errors, especially when different endpoints behave differently. Versioning Issues: Managing multiple API versions can lead to confusion and make testing for backward compatibility more difficult. Unique Testing Challenges with gRPC Binary Protocol: gRPC uses Protocol Buffers for serialization, which makes it harder for you to inspect and debug compared to text-based formats like JSON. Streaming: The support for streaming adds complexity to testing both client and server interactions, especially for bidirectional streams. Compatibility: You need to ensure that gRPC services work well with various programming languages, which can complicate your testing strategies. Latency: Testing for performance and latency in gRPC calls requires a different approach, as the overhead and optimizations differ from REST APIs. HyperTest simplifies gRPC API testing with a no-code approach, allowing your team to focus on functionality instead of writing test code. It automatically generates test cases from your network traffic, saving time and minimizing errors. The user-friendly interface offers clear visualizations of request and response flows, making debugging easier. With comprehensive reporting, you can quickly identify issues and track performance metrics. By reducing the complexities of gRPC testing, HyperTest helps your team conduct efficient tests, boosting your confidence in your APIs' reliability and performance. Why Testing is a Constant Problem? Testing remains a constant challenge because APIs evolve rapidly, requirements change, and frequent updates are necessary. The increasing complexity of distributed systems and microservices architecture adds to these difficulties. As you implement new features, ensuring comprehensive test coverage becomes critical, making ongoing testing a priority for your team. The Need for Specialized Testing Tools You have understood the challenges in testing API with gRPC and REST. To overcome this, you need a specialized tool like HyperTest, which is designed to handle the unique requirements of both REST and gRPC. It provides capabilities such as automated testing, monitoring performance, and seamless integration with CI/CD pipelines. Comparison of Testing Approaches Testing gRPC APIs When testing gRPC APIs, here are some methodologies and best practices you can follow: Use Protocol Buffers: This helps you maintain clear API contracts. Mock Services: Isolate your tests by using mock services to simulate interactions. Focus on Performance Metrics: Pay special attention to performance, especially when it comes to streaming. Implement Automated Testing Frameworks: This can save you time and reduce errors. Testing REST APIs For REST APIs, consider these methodologies and best practices: Validate Each Endpoint: Ensure each endpoint returns the expected responses. Test Authentication and Authorization: Make sure to rigorously test user access controls. Ensure Proper Error Handling: Check how your APIs handle different error scenarios. Manage Multiple API Versions: Keep track of and test different versions of your API. How HyperTest Streamlines Testing for Both? HyperTest simplifies testing for both gRPC and REST APIs by providing a unified platform. It features advanced collaboration tools, allowing teams to share test cases and results easily. It also makes creating mock services and validating responses straightforward, helping you ensure comprehensive coverage and efficiency in your testing processes. Conclusion If you are looking for speed and efficiency, gRPC tends to outperform REST, especially when your applications rely on real-time data streaming or microservices. It’s built for high-performance scenarios, giving you the edge where fast communication is essential. On the other hand, REST remains a versatile and familiar choice for simpler, document-based APIs. Testing both can be challenging, but tools like HyperTest simplify the process for you. It automates the complexities of gRPC testing, allowing you to focus more on development and less on manual testing, making your work smoother and more efficient. Related to Integration Testing Frequently Asked Questions 1. Is gRPC faster than REST? Yes, gRPC is faster than REST due to its binary data format and HTTP/2 support, which enables multiplexing and streaming. 2. Which is more efficient, gRPC or REST? gRPC is more efficient for server-to-server communication, while REST is simpler and more compatible with browsers and external clients. 3. What are the key differences between gRPC and REST? gRPC uses Protocol Buffers and HTTP/2, while REST relies on JSON and HTTP/1.1, impacting speed, efficiency, and compatibility. For your next read Dive deeper with these related posts! 07 Min. Read All you need to know about Apache Kafka: A Comprehensive Guide Learn More 08 Min. Read Using Blue Green Deployment to Always be Release Ready Learn More 09 Min. Read What are stacked diffs and how do they work? Learn More

Comparison Of The Top API Contract Testing Tools Download now Prevent Logical bugs in your database calls, queues and external APIs or services Book a Demo

REST, GraphQL, or gRPC? Learn how to choose the best API design for your architecture based on performance, flexibility, and scalability needs. 26 February 2025 07 Min. Read REST, GraphQL, or gRPC? Choosing the right API Design WhatsApp LinkedIn X (Twitter) Copy link Test your APIs with HyperTest In the complex landscape of software architecture, choosing the right API (Application Programming Interface) design pattern stands as a critical decision that profoundly affects application performance, scalability, and maintainability. This comprehensive analysis examines three leading API design patterns—REST (Representational State Transfer), GraphQL, and gRPC (Google Remote Procedure Call)—each offering unique advantages for different implementation scenarios. List of Sections : ✅ Introduction ✅ REST: The versatile workhorse ✅ GraphQL: The query powerhouse ✅ gRPC: The high-performance contender ✅ Comparative Analysis ✅ Conclusion Introduction APIs function as crucial intermediaries in software system communications, facilitating feature access and data exchange. The selection between REST, GraphQL, or gRPC as an API design pattern must align with specific application requirements, considering factors such as network performance, data structure complexity, and client heterogeneity. Learn more about API Protocols here 1️⃣ REST: The Versatile Workhorse REST is an architectural style that uses HTTP as its backbone, making it an ideal choice for building APIs that can be easily understood and used by a broad range of clients. It operates on a set of guiding principles, such as statelessness and cacheability, which help in building scalable and performant web services. Pros Cons Uses familiar HTTP methods like GET and POST. Can lead to inefficiencies by returning unnecessary data. Stateless interactions promote system scalability. Statelessness: Each request from client to server must contain all the information the server needs to understand the request, independent of any prior requests. Cacheable: Responses must define themselves as cacheable or not, which helps in reducing the load on the server and improving client performance. Ideal Use Cases: Public APIs: Due to its simplicity and widespread adoption, REST is perfect for public APIs accessed by a diverse set of clients. CRUD Operations: Since REST aligns well with CRUD (Create, Read, Update, Delete) operations, it's well-suited for applications that primarily interact with database entities. Mobile and Web Applications: REST can be efficiently used for applications that do not require real-time data updates but need robust and stable communication over HTTP. Twitter API: Provides broad access to Twitter data, including timelines, tweets, and profiles, using a RESTful architecture. 2️⃣ GraphQL: The Query Powerhouse Developed by Facebook, GraphQL is a query language for APIs and a runtime for executing those queries with your existing data. It dramatically improves the efficiency of web APIs and offers clients the power to ask for exactly what they need and nothing more. Pros Cons Clients request exactly what they need, reducing bandwidth usage. Requires more effort on the server side to resolve queries. Powerful Query Abilities: Clients can request exactly the data they need, nothing less, nothing more, which eliminates over-fetching and under-fetching problems. Real-time Data: GraphQL supports real-time updates through subscriptions, making it ideal for dynamic user interfaces. Ideal Use Cases: Complex Systems: Where multiple entities are interrelated, and the client needs varied subsets of data depending on the context. Real-time Applications: Such as collaborative editing tools where the state must be synced between multiple clients in real-time. Microservices: Where a gateway can aggregate data from multiple services and provide a unified GraphQL endpoint. Shopify: Uses GraphQL to offer a flexible, efficient API for its vast ecosystem of e-commerce sites and apps. 3️⃣ gRPC: The High-Performance Contender gRPC is a modern open-source high performance RPC (Remote Procedure Call) framework that can run in any environment. It uses protocol buffers by default as its interface language and is known for its high performance and suitability in polyglot environments. Pros Cons Leveraging HTTP/2 allows for reduced latency. Not inherently compatible with all web environments. Auto-generates client libraries in various programming languages. HTTP/2 Support: gRPC makes use of HTTP/2 as a transport protocol, which allows for long-lived connections, multiplexing multiple requests over a single connection, and server push capabilities. Protobuf (Protocol Buffers): Highly efficient binary serialization tool that defines a flexible, efficient, automated mechanism for serializing structured data. Learn more about gRPC Vs REST here Ideal Use Cases: Microservices: gRPC is excellent for inter-service communication in a microservices architecture due to its low latency and high throughput. Real-Time Communication: Applications that require real-time data exchange, such as live streaming systems and gaming backends, benefit from gRPC's performance characteristics. Multi-Language Environments: gRPC supports code generation in multiple programming languages, making it a good choice for polyglot environments where different services are written in different languages. Many of Google's own APIs are implemented using gRPC, taking advantage of its performance benefits and broad language support. Testing Challenges with REST, GraphQL, and gRPC Testing different API design paradigms—REST, GraphQL, and gRPC—presents unique challenges due to their varied architectural approaches and communication protocols. Understanding these challenges can help developers create more robust testing strategies. ➡️ Testing Challenges with REST REST's stateless nature means each API request must include all necessary information to be processed independently, complicating tests involving user sessions or states. For example, testing a REST API for a shopping cart requires simulating each step of adding or removing items since the server doesn't remember previous interactions. Additionally, ensuring accurate responses across various scenarios presents challenges, particularly when APIs deliver dynamic or uneven data. For instance, an API fetching user profiles needs extensive testing to handle different types of requests correctly—such as ensuring proper responses for invalid user IDs or unauthorized access attempts. This requires a broad set of test cases to verify that the API behaves as expected under all conditions. ➡️ Testing Challenges with GraphQL Query Complexity: The flexible nature of GraphQL allows for complex queries, which can be challenging to test thoroughly. Each possible query permutation may need individual testing. Nested Data Structures: Deeply nested data requests require complex setup and teardown in test environments, increasing the risk of overlooking edge cases. A test needs to mock users → posts → comments → authors , which increases complexity. const mockResponse = { data: { user: { name: "Alice", posts: [ { title: "GraphQL Testing", comments: [ { content: "Great post!", author: { name: "Bob" } }, { content: "Very informative.", author: { name: "Charlie" } } ] } ] } } }; ➡️ Testing Challenges with gRPC Protocol Buffers: Testing requires familiarity with Protocol Buffers, as you must ensure both serialization and deserialization work as expected across different programming languages. Streaming Capabilities: gRPC's support for bidirectional streaming complicates testing, particularly in ensuring data integrity and order across streams. ✅ How HyperTest can help you test REST, gRPC and GraphQL APIs? HyperTest leverages automated test case generation based on real user interactions. This comprehensive approach ensures no test case is missed, offering several key advantages: Automated Test Case Generation from real interactions : HyperTest captures real user interactions with the application. This includes every query, mutation, or API call made in GraphQL, gRPC, or REST. By recording these real-time data flows and user paths, HyperTest automatically generates test cases that reflect actual usage scenarios. This method ensures all possible user interactions are tested, including complex and rare cases that might not be considered in manual test case design. Testing REST APIs in HyperTest Stateful Recording and Mocking : During testing, HyperTest not only captures the requests but also records the state of all components involved in the interaction. This includes databases, external services, and internal states within the application. By recreating the exact state during test playback, HyperTest provides a precise environment for validating how state changes impact the application, identifying potential regressions or errors caused by code changes. Testing gRPC APIs in HyperTest Comprehensive Coverage Across Protocols: Whether it’s a REST API delivering straightforward data, a GraphQL API handling complex, nested queries, or a gRPC service managing streaming data, HyperTest treats all protocols uniformly in terms of testing methodology. It captures, mocks, and replays interactions across all these APIs. This uniform approach simplifies the testing process, ensuring consistency and thorough coverage regardless of the underlying protocol or architecture. Root Cause Analysis : When an error is detected, HyperTest can analyze the recorded test cases and the associated state changes to pinpoint what specific line change or interaction caused the issue. This capability significantly reduces the debugging time for developers by providing clear insights into the source of errors, streamlining the development and maintenance processes. Testing GraphQL APIs in HyperTest This approach not only ensures that every potential user journey is covered but also supports rapid development cycles by automating complex testing workflows and identifying issues at their source. Conclusion Choosing between REST, GraphQL, and gRPC for your API architecture depends on specific project needs, but it's clear that each protocol offers unique advantages. HyperTest enhances the development and maintenance of these APIs as it provides: Comprehensive Coverage Rapid Debugging Stateful Testing Accuracy See how HyperTest helped Zoop.one reduce API test complexity by 60% and cut debugging time by 40% in their GraphQL architecture. Watch the full case study here! With these capabilities, HyperTest supports robust API development, helping teams build efficient, scalable, and reliable applications. This aligns with the goal of choosing the right API design—REST, GraphQL, or gRPC—to best meet the architectural demands of your projects. Get a Demo Related to Integration Testing Frequently Asked Questions 1. What are the key differences between REST, GraphQL, and gRPC? REST uses resource-based endpoints, GraphQL allows flexible queries, and gRPC offers high-performance communication via Protocol Buffers. 2. Which API design is best for microservices architecture? gRPC is often preferred for microservices due to its efficiency, while REST and GraphQL are suitable for broader compatibility and flexibility. 3. When should I choose GraphQL over REST or gRPC? Choose GraphQL when you need precise data fetching, reduced over-fetching, and a flexible schema for frontend applications. For your next read Dive deeper with these related posts! 09 Min. Read gRPC vs. REST: Which is Faster, Efficient, and Better? Learn More 09 Min. Read RabbitMQ vs. Kafka: When to use what and why? Learn More 07 Min. Read Optimize DORA Metrics with HyperTest for better delivery Learn More

Monolithic architecture works best for simple applications because as a single small deployable unit, they are easy to build and maintain. But things do not stay simple all the time. 8 March 2023 06 Min. Read 8 Problems You'll Face with Monolithic Architecture Download the 101 Guide WhatsApp LinkedIn X (Twitter) Copy link Fast Facts Get a quick overview of this blog Understand the differences between monolithic and microservices architectures. Recognize scalability, modularity, deployment, and complexity issues. Explore scalability, agility, easier testing, and alignment with cloud tech. Recognize the importance of microservices for competitiveness. Download the 101 Guide Monolithic architecture is one of the most prevalent software designs. In software engineering, the term " monolithic model ” refers to a single, indivisible unit. The idea behind monolithic software is that all of the parts of an application are put together into a single program like the database, the client/user interface, and the server, all in a single code base. The most important benefit of the monolithic architecture is how simple it is. If you’re just starting out small with your software development, monolithic is easier to test, deploy, debug, and keep an eye on. All of the data is kept in one database, so it doesn't need to be synchronized. Monolithic Design Can’t Keep Up With the Development Needs of Agile teams Monolithic architecture works best for simple applications because as a single small deployable unit, they are easy to build and maintain. But things do not stay simple all the time. As the size and complexity of the app grow, problems start to appear. It becomes harder to make changes to the code without feeling concerned about its cascading effects. Changes to one module can cause other modules to act in unexpected ways, which can cause a chain of errors. Because of how big the monolith is, it takes longer to start up, which slows down development and gets in the way of continuous deployment. Agile teams want to ship new changes quickly and iteratively in short cycles (called sprints) which is difficult to achieve with complex applications built as monoliths. Collaboration is hard because of the complexity of a large code base compressed as a single unit. Quick Question Microservice integration bugs got you down? We can help! Yes Problems with Monolithic Architecture Statista' s 2021 research shows that “ only 1.5% of engineering leaders plan to stick with a monolithic architecture ” . With the demanding need for software expansion, the rise of mobile devices, and the cloud, monolithic applications are not going to help. Worse, since everything in a monolithic architecture is tied to a single codebase, it can be hard to test specific functions or components because it is difficult to separate them, leading to vastly higher costs. 1. Monolithic Architecture Scalability Issues If a monolithic application becomes popular and its user base grows, it can become difficult to scale the application to meet the increased demand. All of the application's features are in a single codebase, so adding more resources requires deploying a whole new version of the application. 2. Lack of modularity Because a monolithic application is a single, cohesive unit, it can be difficult to reuse specific parts of the application in other projects. This can make it hard to update or fix individual parts of an app without changing the whole thing. 3. Slow deployment When a new version of a monolithic application needs to be released, it can take a long time because the whole application needs to be deployed again, even if only a small part of it has changed. 4. Difficulty in identifying and fixing issues When something goes wrong with a monolithic application, it can be hard to figure out why because all of the functionality is in a single codebase. This can make it challenging to fix issues and deploy fixes quickly. 5. Tight coupling Monolithic applications often have tight coupling between different parts of the codebase, which means that changes in one part of the application can have unintended consequences in other parts of the application. 6. Monolithic Architecture Inflexibility Monolithic architecture can be hard to change because it doesn't make it clear which parts of an application are responsible for what. This can make it hard to change or update one part of the application without possibly affecting other parts. 7. Complexity Monolithic applications can become complex over time as they grow and more features are added. This can make it difficult for new developers to understand how the application works and contribute to it. 8. Testing and deployment challenges Testing and releasing a monolithic application can be hard because it can be difficult to test each part of the application separately. This can make it difficult to identify and fix issues before deploying the application. The Emergence of Microservices As software systems got more complicated and had more needs, it became clear that a single-piece architecture couldn't handle everything. As a result, new approaches, such as microservice architecture , have been developed and implemented. However, monolithic architecture is still commonly used, especially for smaller and less complex systems. Again, taking insights from Statista's 2021 research, 81.34% of businesses already use microservices, and 17.34% are planning to make the switch . Microservices, unlike monolithic systems, are designed to scale with changing market demands. Modern businesses are moving away from monolithic systems to microservices so that they can stay competitive . Community Favourite Reads Unit tests passing, but deployments crashing? There's more to the story. Learn More Masterclass on Contract Testing: The Key to Robust Applications Watch Now Related to Integration Testing Frequently Asked Questions 1. What is a monolithic architecture? Monolithic architecture is a traditional software design approach where an entire application is built as a single, interconnected system. In this structure, all components and functions are tightly integrated, making it challenging to modify or scale specific parts independently. It contrasts with microservices architecture, which divides the application into smaller, loosely coupled, and independently deployable components. 2. What are microservices? Microservices are a software development approach where an application is divided into small, independent components that perform specific tasks and communicate with each other through APIs. This architecture improves agility, allowing for faster development and scaling. It simplifies testing and maintenance by isolating components. If one component fails, it doesn't impact the entire system. Microservices also align with cloud technologies, reducing costs and resource consumption. 3. What are the Disadvantages of a monolithic architecture? Monolithic architecture has several disadvantages. It faces scalability challenges, as scaling the entire application can be inefficient and costly. Modifications and updates are complex and risky, given their broad impact. Monolithic apps demand substantial resources and can hinder development speed due to collaboration difficulties. Furthermore, they are susceptible to single points of failure, where issues in one part can disrupt the entire application's functionality, making them less resilient. For your next read Dive deeper with these related posts! 10 Min. Read What is Microservices Testing? Learn More 05 Min. Read Testing Microservices: Faster Releases, Fewer Bugs Learn More 07 Min. Read Scaling Microservices: A Comprehensive Guide Learn More

Explore load testing! Learn how it simulates user traffic to expose performance bottlenecks and ensure your software stays strong under pressure. 19 March 2024 09 Min. Read What is Load Testing: Tools and Best Practices WhatsApp LinkedIn X (Twitter) Copy link Checklist for best practices What is Load Testing? Load testing is the careful examination of the behavior of software under different load levels, mimicking real-time usage patterns and stress scenarios under specific conditions. It is primarily concerned with determining how well the application can handle different load levels, including concurrent user interactions, data processing and other functional operations. 💡 Cover all your test scenarios including all the edge-cases by mimicking your production traffic. Learn how ? While traditional testing focuses on identifying individual errors and faults, load testing goes deeper and evaluates the overall capacity and resilience of the system. They are comparable to a stress test, where the software is pushed to its limits to identify problems and vulnerabilities before they manifest themselves in real-time failures that could spell disaster. Stress testing uses sophisticated tools to simulate different user scenarios to replicate the traffic patterns and demands expected at peak times. The system is put under stress to measure its responsiveness and stability. This provides an in-depth analysis of system behavior under expected and extreme loads. While load testing allows developers and engineers to identify performance issues and make informed changes to improve the overall experience by subjecting the system to a simulated high load. Load testing uncovers and highlights performance issues such as: ➡️ slow response times, ➡️ exhausted resources or even complete system crashes. These findings are invaluable as they allow developers to proactively address vulnerabilities and ensure that the software remains stable and performant even under peak loads. This careful evaluation helps to determine the system's load limit and create a clear understanding of its operational limitations . Load testing is a continuous process and not a one-off activity. There are many iterations as new features are added and the user base is constantly expanding. Why Load Testing? The value of load testing extends far beyond technical considerations. Load testing fosters harmonious interactions, user trust and satisfaction by ensuring optimal performance under peak loads. For example , users navigate a website that crashes during a sale or an app that freezes during peak usage hours. In such a case, frustration and negativity are inevitable. Load testing helps avoid such scenarios, contributing to a positive user experience and brand loyalty which ultimately helps in building a reputation. While the core principles remain the same, load testing encompasses a host of methodologies - from simple stress testing to sophisticated performance analysis. The specific approach depends on the software, its target audience and the anticipated usage patterns. Load testing is not just about fixing problems, but also about preventing them. It is pertinent to note that the insights gained from load testing help development teams: ➡️ to make informed decisions, optimize performance and enhance the overall efficiency of the application. ➡️ serves as a proactive measure to prevent performance degradation, downtime or user dissatisfaction under high-demand situations. 💡 Interested to achieve more than 90% of code coverage autonomously and at scale. We can write 365 days of effort in less than a few hours. Get on a quick call  now! Best Practices to Perform Load Testing Load testing ensures the proper performance and reliability of software systems and applications through its pre-emptive mode of operation. To make an informed decision about an application’s scalability and derive accurate insights, it is important to adopt best practices in load testing. Here are some of the best practices for effective load testing: 1.Define Clear Objectives: The goals and objectives of the load testing process should be clearly outlined. The performance metrics to be measured, such as response time, throughput and resource utilization need to be measured. 2. Realistic Scenario Design: Realistic usage scenarios should be created that mimic actual user behavior and system interactions. Consider various parameters like user load, data volume and transaction types to simulate conditions. 3. Scalability Testing: The application's scalability should be tested by gradually increasing the load to identify performance thresholds and breakpoints. Assess how the system handles increased user loads without compromising performance. 4. Unique and Different Test Environments: Load tests in different environments (e.g., development, staging and production) should be conducted to identify environment-specific issues. 💡 Ensure that the test environment closely mirrors the production environment for accurate results. We have this sorted in HyperTest’s approach, see it working here ! 5. Monitor System Resources: Compatible monitoring tools to capture key performance indicators during load tests can be implemented. CPU usage, memory consumption, network activity and other relevant metrics should be monitored to identify resource issues. 6. Data Management: Use representative and anonymized datasets for load testing to simulate real-time scenarios without compromising on privacy. Consider database optimization to ensure efficient data retrieval and storage during high load periods. 7. Ramp-Up and Ramp-Down Periods: Gradually increase the user load during the test to mimic realistic user adoption patterns. Include ramp-down periods to assess how the system recovers after peak loads, identifying issues with resource release. 8. Scripting Best Practices: Well-structured and modular scripts should be developed to simulate user interactions accurately. Scripts should be regularly updated to align with application changes and evolving user scenarios. 9. Continuous Testing: Integrate load testing into the Continuous Integration/Continuous Deployment (CI/CD) pipeline for ongoing performance validation. Regularly revisit and update load testing scenarios as the applications change with each iteration. 10. Documentation and Analysis: Document test scenarios, results and any identified issues comprehensively. Conduct thorough analysis of test results, comparing them against predefined performance criteria and benchmarks. Following these load testing best practices ensures a complete assessment of an application's performance, enabling development teams to proactively address scalability challenges and deliver a smooth user experience. Metrics of Load Testing Load testing is not just about stressing the software, but also analyzing the data generated during the process to illuminate weaknesses. This analysis is based on a set of metrics that act as vital clues in the quest for ideal software performance. The following are the metrics of load testing: Response Time: This metric that is measured in milliseconds, reflects the time taken for the system to respond to a user request. In load testing, it is critical to monitor the average, median and even percentile response times to identify outliers and performance issues. Throughput: This metric gauges the number of requests processed by the system within a specified timeframe. It is essential to monitor how throughput scales with increasing user load. Resource Utilization: This metric reveals how efficiently the system utilizes its resources, such as CPU, memory and network bandwidth. Monitoring resource utilization helps identify issues and areas requiring optimization. Error Rate: This metric measures the percentage of requests that fail due to errors. While some errors are bound to happen, a high error rate during load testing indicates underlying issues impacting system stability. Concurrency: This metric reflects the number of concurrent users actively interacting with the system. In load testing, increasing concurrency helps identify how the system handles peak usage scenarios. Hits per Second: This metric measures the number of requests handled by the system per second. It provides insights into the system's overall processing capacity. User Journey Completion Rate: This metric reflects the percentage of users successfully completing a specific journey through the system. It highlights any points of user drop-off during peak usage which critical for optimizing user experience. System Stability: This metric assesses the system's overall stability under load, measured by uptime and crash-free operation. Identifying and preventing crashes is necessary for maintaining user trust and avoiding downtime. Scalability: This metric reflects the system's ability to adapt to increasing load by adding resources or optimizing processes. It is important to assess how the system scales to ensure it can meet future demand. Cost-Effectiveness: This metric considers the cost of performing load testing compared to the losses incurred due to performance issues. While upfront costs may seem high, investing in load testing can prevent costly downtime and lost revenue, ultimately proving cost-effective. Understanding and analyzing these key metrics is necessary for businesses to gain invaluable insights from load testing, thus ensuring their software performs well, scales effectively and ultimately delivers a positive user experience under any load. Tools to Perform Load Testing Here are some tools in the load testing arena: 1. HyperTest: HyperTest , is a unique API testing tool that helps teams generate and run integration tests for microservices without writing a code. It auto-generates integration tests from production traffic. It regresses all APIs by auto-generating integration tests using network traffic without asking teams to write a single line of code, also giving a way to reproduce these failures inside actual user-journeys. HyperTest tests a user-flow, across the sequence of steps an actual user will take in using the application via its API calls. HyperTest detects every issue during testing in less than 10 minutes, that other written tests would definitely miss. HyperTest is a very viable answer for all load testing needs. For more, visit the website here . 2. JMeter: This open-source tool offers extensive customisation and flexibility, making it a good choice among experienced testers. However, its steeper learning curve can be daunting for beginners. JMeter excels in web application testing and supports various protocols. 3. The Grinder: Another open-source option, The Grinder focuses on distributed testing that permits distribution of load across multiple machines for larger-scale simulations. Its scripting language can be challenging for novices but its community support is valuable. 4. LoadRunner: This industry-standard tool from Micro Focus offers unique features and comprehensive reporting. However, its higher cost and complex interface might not suit smaller teams or those new to load testing. 5. K6 - Tool to perform Load Testing: This cloud-based tool boasts scalability and ease of use, making it a great choice for teams seeking a quick and efficient solution. Its pricing structure scales with usage, offering flexibility for various needs. The best tool depends on specific needs, team expertise and budget. Factors like the complexity of the application, desired level of customization and technical skills of the team should be considered. Advantages of Load Testing Now that we have read about what load testing means and what testing tools can be used. Let us now discuss about some advantages and disadvantages of the same, we have already covered the advantages of performing load testing in the above sections. So here’s an overview of the benefits of load testing: Disadvantages of Load Testing: The following are the disadvantages of load testing. Resource intensive: Load testing requires significant hardware and software resources to mimic realistic user scenarios. This can be expensive, especially for smaller development teams or applications with high concurrency requirements. Time commitment: Setting up and executing load testing can be time-consuming, requiring skilled personnel to design, run and analyse the tests. Complexity: Understanding and interpreting load testing results can be challenging, especially for those without specific expertise in performance analysis. False positives: Overly aggressive load testing can lead to false positives, identifying issues that might not occur under real-time usage patterns. Limited scope: Load testing focuses on overall system performance, therefore sometimes missing specific user journey issues or edge cases. Disruptive: Load testing can impact production environments, requiring careful planning and scheduling to minimize disruption for users in real-time. Not a one-size-fits-all: While immensely valuable, load testing is not a one-size-fits-all solution. It needs to be integrated with other testing methodologies for a holistic assessment. Continuous process: Load testing is not a one-time activity. Tests need to be revisited and updated regularly to ensure continued performance and stability. Conclusion Load testing may seem like an arduous journey in software testing but its rewards are substantial. Valuable insights are gained into the software’s strengths and weaknesses just by simulating real-world user demands. This helps in building a strong software foundation. Load testing is not just about achieving peak performance under artificial pressure but also understanding the system’s limits and proactively addressing them. Investment in load testing is about achieving future success by preventing expensive downtime. This helps in the delivery of a product that thrives in the digitals space. Using right tools like HyperTest , along with the expertise that comes with it, paves the way for a software journey that is filled with quality and user satisfaction. Related to Integration Testing Frequently Asked Questions 1. What is a load tester used for? A load tester is used to simulate multiple users accessing a software application simultaneously, assessing its performance under various loads. 2. Why is Shift-Left Testing important? The steps in load testing typically include defining objectives, creating test scenarios, configuring test environment, executing tests, monitoring performance metrics, analyzing results, and optimizing system performance. 3. What is an example of load testing? An example of load testing could be simulating hundreds of users accessing an e-commerce website simultaneously to evaluate its response time, scalability, and stability under heavy traffic conditions. For your next read Dive deeper with these related posts! 09 Min. Read What is Smoke Testing? and Why Is It Important? Learn More 11 Min. Read What is Software Testing? A Complete Guide Learn More Add a Title What is Integration Testing? A complete guide Learn More

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Catch bugs early & write rock-solid code. This unit testing guide shows you how (with examples!). 3 July 2024 07 Min. Read How To Do Unit Testing? A Guide with Examples Download The 101 Guide WhatsApp LinkedIn X (Twitter) Copy link Fast Facts Get a quick overview of this blog Start unit testing early in the development process. This will help you catch bugs early on and make your code more maintainable. Isolate units of code for testing. This will make it easier to identify and fix bugs. Write clear and concise test cases that cover different scenarios. This will help you ensure that your code is working correctly under a variety of conditions. Download The 101 Guide Before discussing how to do unit testing, let us establish what it actually is. Unit testing is a software development practice where individual units of code are tested in isolation. These units can be functions, methods or classes. The goal is to verify if each unit behaves as expected, independent of other parts of the code. So, how do we do unit testing? There are several approaches and frameworks available depending on your programming language. But generally, writing small test cases that mimic how the unit would be used in the larger program is the usual procedure . These test cases provide inputs and then assert the expected outputs. If the unit produces the wrong output, the test fails, indicating an issue in the code. You can systematically test each building block by following a unit testing methodology thus ensuring a solid foundation for your software. We shall delve into the specifics of how to do unit testing in the next section. Steps for Performing Unit Testing 1. Planning and Setup Identify Units: Analyze your code and determine the units to test (functions, classes, modules). Choose a Testing Framework: Select a framework suitable for your programming language (e.g., JUnit for Java, pytest for Python, XCTest for Swift). Set Up the Testing Environment: Configure your development environment to run unit tests (IDE plugins, command-line tools). 2. Writing Test Cases Test Case Structure: A typical unit test case comprises three phases: Arrange (Setup): Prepare the necessary data and objects for the test. Act (Execution): Call the unit under test, passing in the prepared data. Assert (Verification): Verify the actual output of the unit against the expected outcome. Test Coverage: Aim to cover various scenarios, including positive, negative, edge cases, and boundary conditions. 💡 Get up to 90% code coverage with HyperTest’s generated test cases that are based on recording real network traffic and turning them into test cases, leaving no scenario untested . Test Clarity: Employ descriptive test names and assertions that clearly communicate what's being tested and the expected behavior. 3. Executing Tests Run Tests: Use the testing framework's provided tools to execute the written test cases. Continuous Integration: Integrate unit tests into your CI/CD pipeline for automated execution on every code change. 4. Analyzing Results Pass/Fail: Evaluate the test results. A successful test case passes all assertions, indicating correct behavior. Debugging Failures: If tests fail, analyze the error messages and the failing code to identify the root cause of the issue. Refactoring: Fix the code as needed and re-run the tests to ensure the problem is resolved. Example: Python def add_numbers(a, b): """Adds two numbers and returns the sum.""" return a + b def test_add_numbers_positive(): """Tests the add_numbers function with positive numbers.""" assert add_numbers(2, 3) == 5 # Arrange, Act, Assert def test_add_numbers_zero(): """Tests the add_numbers function with zero.""" assert add_numbers(0, 10) == 10 def test_add_numbers_negative(): """Tests the add_numbers function with negative numbers.""" assert add_numbers(-5, 2) == -3 Quick Question Having trouble getting good code coverage? Let us help you Yes Best Practices To Follow While Writing Unit Tests While the core process of unit testing is straightforward, following best practices can significantly enhance their effectiveness and maintainability. Here are some key principles to consider: Focus on Isolation: Unit tests should isolate the unit under test from external dependencies like databases or file systems. This allows for faster and more reliable tests. Use mock objects to simulate these dependencies and control their behavior during testing. Keep It Simple: Write clear, concise test cases that focus on a single scenario. Avoid complex logic or nested assertions within a test. This makes tests easier to understand, debug, and maintain. Embrace the AAA Pattern: Structure your tests using the Arrange-Act-Assert (AAA) pattern. In the Arrange phase, set up the test environment and necessary objects. During Act, call the method or functionality you are testing. Finally, in Assert, verify the expected outcome using assertions. This pattern promotes readability and maintainability. Test for Edge Cases: Write unit tests that explore edge cases and invalid inputs to ensure your unit behaves as expected under all circumstances. This helps prevent unexpected bugs from slipping through. Automate Everything: Integrate your unit tests into your build process. This ensures they are run automatically on every code change. This catches regressions early and helps maintain code quality. 💡 HyperTest integrates seamlessly with various CI/CD pipelines, smoothly taking your testing experience to another level of ease by auto-mocking all the dependencies that your SUT is relied upon. Example of a Good Unit Test +----------------+ +-----------------------+ | Start |---->| Identify Unit to Test | +----------------+ +-----------------------+ | v +-----------------------+ +-----------------------+ | Analyze Code & |------>| Choose Testing Framework| | Define Test Cases | +-----------------------+ +-----------------------+ | v +-----------------------+ +-----------------------+ | Write Test Cases |------>| Set Up Testing Environment | | (Arrange, Act, Assert)| +-----------------------+ +-----------------------+ | v +-----------------------+ +-----------------------+ | Run Tests |------>| Execute Tests | +-----------------------+ +-----------------------+ | v +-----------------------+ +-----------------------+ | Analyze Results |------>| Pass/Fail | +-----------------------+ +-----------------------+ | (Fix code if Fail) v +-----------------------+ +-----------------------+ | Refactor Code (if |------>| End | | needed) | Imagine you have a function that calculates the area of a rectangle. A good unit test would be like a mini-challenge for this function. Set up the test: We tell the test what the length and width of the rectangle are (like setting up the building blocks). Run the test: We ask the function to calculate the area using those lengths. Check the answer: The test then compares the answer the function gives (area) to what we know it should be (length x width). If everything matches, the test passes! This shows the function is working correctly for this specific size rectangle. We can write similar tests with different lengths and widths to make sure the function works in all cases. Conclusion Unit testing is the secret handshake between you and your code. By isolating and testing small units, you build a strong foundation for your software, catching errors early and ensuring quality. The key is to focus on isolated units, write clear tests and automate the process. You can perform unit testing with HyperTest. Visit the website now ! Community Favourite Reads Unit tests passing, but deployments crashing? There's more to the story. Learn More How to do End-to-End testing without preparing test data? Watch Now Related to Integration Testing Frequently Asked Questions 1. What are the typical components of a unit test? A unit test typically involves three parts: 1) Setting up the test environment: This includes initializing any objects or data needed for the test. 2) Executing the unit of code: This involves calling the function or method you're testing with specific inputs. 3) Verifying the results: You compare the actual output against the expected outcome to identify any errors. 2. How do I identify the unit to be tested? Identifying the unit to test depends on your project structure. It could be a function, a class, or a small module. A good rule of thumb is to focus on units that perform a single, well-defined task. 3. How do I integrate unit tests into my CI/CD pipeline? To integrate unit tests into your CI/CD pipeline, you can use a testing framework that provides automation tools. These tools can run your tests automatically after every code commit, providing fast feedback on any regressions introduced by changes. For your next read Dive deeper with these related posts! 10 Min. Read What is Unit testing? A Complete Step By Step Guide Learn More 05 Min. Read Unit Testing with Examples: A Beginner's Guide Learn More 09 Min. Read Most Popular Unit Testing Tools in 2025 Learn More

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