Fyers wanted a solution to automatically detect and report regressions such as content_type_change, schema_failure, and key_removed at scale from the outset. This would streamline fixing them before they became critical issues. Customer Success Half a Million Users, Zero Schema Failures: How Fyers Leverages Hypertest Fyers wanted a solution to automatically detect and report regressions such as content_type_change, schema_failure, and key_removed at scale from the outset. This would streamline fixing them before they became critical issues. Pain Points: Slow automation hampered Fyers' rapid development cycles. Bugs snuck through testing, disrupting releases and costing money. Existing frameworks couldn't handle complex scenarios, leading to production issues. Results: Regression testing slashed from days to 3 hours. Automated test coverage soared to 85%. Faster feedback loops sped up bug resolution before release. About: Founded: 2015 Employees: 300 Industry: Stockbroking and Investment Services Clients: 500,000+ investors across NSE, BSE, and MCX Fyers, established in 2015, has been dedicated to transforming trading and investment in India. As a dynamic startup in stockbroking, Fyers supports seamless trading for over half a million investors on major Indian exchanges. From its early days with 1,000 accounts in 2016, Fyers has grown to a formidable force with a strong team of 300 and a consistent month-on-month growth rate of 15%. Fyer’s Requirements: Enhance code management control by 30% to streamline workflows and deployments. Refine automation processes to achieve performance efficiency. Increase code coverage by at least 40% to substantially reduce production defects. Challenge: In the high-stakes fintech sector, where daily transactions exceed 2 billion USD, the reliability of Fyers' technology platforms is paramount. The rapid pace of trading demands equally swift technological responses. Fyers’ technology stack included over 100+ microservices, which were initially supported by an in-house automation framework that struggled to match the speed of transactions. The need to enhance release velocity was critical; however, the existing framework was not only slowing down releases but also failing to capture bugs effectively. The framework’s inability to handle complex edge cases resulted in production issues that were costly both in terms of time and financial resources. Solution: After evaluating various tools and realizing the limitations in documentation, user experience, and support, Fyers chose HyperTest for its integration testing capabilities. HyperTest drastically reduced the regression testing time to under 3 hours, down from 3-4 days. The tool enabled rapid feedback to developers, facilitating quick bug fixes and smooth rollouts of new updates, which was crucial for high release velocity . HyperTest eliminated the labor-intensive process of manual test writing by automatically generating test cases for all critical user flows . This capability saved critical dev time spent on test maintenance increasing overall test coverage to 85% autonomously. HyperTest ensured critical regressions like schema failures or data changes were never missed during testing. The best thing about HyperTest is that you don't need to write and maintain any unit tests. Also any enhancements or additions to the APIs can be quickly tested, ensuring it is backwards compatible. So this makes sure of bug-free deployments. The UI is very easy to use. - Khyati Suthar, Software Developer Read it now How Yellow.ai Employs HyperTest to Achieve 95% API Coverage and Ensure a Flawless Production Environment Read it now Processing 1.5 Million Orders, Zero Downtime: How Nykaa Optimizes with HyperTest View all Customers Catch regressions in code, databases calls, queues and external APIs or services Take a Live Tour Book a Demo

Postman often gets on nerves for the kind of manual effort that it needs for performing API testing. Let's discuss the most common challenges and approach to solve them. 7 September 2024 07 Min. Read Common Challenges in API Testing with Postman WhatsApp LinkedIn X (Twitter) Copy link Get the Guide Postman has a loyal following of over a million ‘ developers ’… all of which I plan to make very angry right now. Why? Because Postman has a VERY limited application for API testing. I have proper reasons to back my statement and even many developers have already started to look out for a “ postman alternative ”. API testing is a critical component of the development process and should be robust since APIs are carrying more than 80% of the business logic of your application. But Postman is not compatible to serve as API testing tool, while it’s good for API development and collaboration. But API Testing is simply not it’s right use-case. Here’s a simple compilation of the most common challenges ( that you as a developer ) must’ve encountered while performing API testing using Postman. Don’t worry, the solution to every challenge is presented, so you don’t have to invest your time looking for the right tool that just does the job of API testing without you bothering too much. Let’s get this blog started then: 1. Environment Configuration Issues Setting up different environments for testing various stages of the API lifecycle (development, testing, production) can be complex and error prone. ➡️Try this: Leverage Postman's environment variables feature. By defining variables for different environments, you can easily switch contexts and ensure your tests are using the right settings. Additionally, use Postman's global variables for values that remain constant across environments. 🤔But again, the Problem: Postman allows you to use environment variables, but managing these across large teams or multiple services can be challenging and error prone. ✅Get rid of maintaining environments only: HyperTest requires no dedicated environment. Tests can be run locally, 100% on-prem, without needing dedicated environments 2. Maintaining and Updating Tests As APIs evolve, tests need to be updated frequently to match new requirements, which can be time-consuming. ➡️Try this: Implement a strategy for regular test reviews and updates as part of your development cycle. Utilize Postman’s version control integrations to keep track of changes and maintain test integrity. This way, you can easily roll back to previous versions if new updates introduce errors. 🤔This is real pain: Postman offers version control, but the process of manually updating tests and collections whenever there are changes can slow down development. ✅Auto-updation of test cases: HyperTest automatically updates tests based on real-time traffic. It captures real API interactions and dynamically generates tests, eliminating the need to manually adjust tests as your API evolves. 3. Data Driven Testing Limitations Running tests with different data sets can be cumbersome without a structured approach. ➡️Try this: Use Postman’s ability to read from external data files (like JSON or CSV) to feed varying datasets into your tests. This approach not only streamlines the testing process but also helps in identifying how different data types affect API performance. 🤔But Postman=Manual Effort: Postman does support external data-driven testing, but it requires manual data input management and setup. ✅No-need to prepare test-data anymore: HyperTest uses real-world traffic data for both read and write requests, making it easier to run data-driven tests without needing to prepare external files. HyperTest keeps the test data reusable and dynamically applies it to your API test cases. 4. Collaboration Difficulties Coordinating API testing across multiple team members and ensuring consistency can be difficult, especially in remote setups. ➡️Try this: Utilize Postman’s built-in collaboration features like shared collections, environments, and the team workspace. Ensure that all team members are aligned on how to use these resources effectively and establish guidelines for managing updates and changes—but larger teams can still run into synchronization issues, with inconsistent test versions across different team members. ✅Easily collaborate on Slack: With HyperTest, there’s no need for constant synchronization of test collections or environments. HyperTest is designed to capture traffic automatically, making collaboration easier as team members don’t need to worry about manually keeping collections in sync. Everyone has access to the same traffic data, and tests are dynamically created based on actual usage. And to top that, automatic notifications are sent to the dependent services owner if an upstream service has been updated, so that the dependent ones can also keep in sync. 5. Integration and Continuous Testing Integrating API testing into CI/CD pipelines can be challenging, especially for teams new to automation. ➡️Try this: Use Postman’s Newman command line tool to integrate your Postman collections into your CI/CD pipelines. This allows automated running of tests every time there’s a change in your codebase, ensuring immediate feedback on the impact of changes. ✅Integrate with CI/CD with ease: HyperTest integrates seamlessly into CI/CD pipelines, with no need for additional tools or complex setup. HyperTest runs tests automatically as part of your CI/CD flow, ensuring that your API is validated with every code change. Don’t take our word for any of it! Engineering team at HyperTest themselves faced all these challenges with Postman, and that’s how we cracked the idea to tailor-made a testing tool that is modern, shift-left and is built for developers . Here’s a working demo of HyperTest to make things really easy for you: Set-it up for your services for free and explore its value. Drop us your details here. Conclusion While Postman is a popular tool for API testing, it falls short in certain areas when scaling to meet the demands of modern API ecosystems. HyperTest solves these challenges by providing a more automated, streamlined approach to API testing. From environment configuration to CI/CD integrations. It offers a comprehensive solution, ensuring your APIs are robust, reliable, and ready for production without manual intervention. With HyperTest, your devs can focus on building and optimizing APIs while knowing your tests are always up to date, thorough, and efficient. Test smarter, not harder, with HyperTest . Frequently Asked Questions 1. How do I handle dynamic parameters in API testing with Postman? Use environment variables, pre-request scripts, or Postman's built-in functions to generate dynamic values. This ensures accurate testing and avoids hardcoding. Avoid all the manual handling of dynamic parameters with the help of HyperTest, it automatically handles them for you. 2. What are some common performance issues encountered in API testing with Postman, and how can they be addressed? Optimize API endpoints, simulate heavy traffic, and monitor network performance. Use Postman's features to identify bottlenecks and improve overall efficiency. 3. How can I effectively manage and organize API tests in HyperTest for large-scale projects? Use test suites, test cases, test data and auto-mocks to organize your HyperTest tests. This will improve structure, maintainability, and collaboration for large-scale projects. For your next read Dive deeper with these related posts! 04 Min. Read Postman Tool for API Testing Vs HyperTest: Comparison Learn More 5 Min. Read Best Postman Alternatives To Consider in 2025 Learn More 05 Min. Read How to choose the Right Postman Alternative for your Team? 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Struggling to choose the right monitoring tool? This guide helps tech teams find the best fit for reliability, speed, and insights. 22 February 2025 07 Min. Read Choosing the right monitoring tools: Guide for Tech Teams WhatsApp LinkedIn X (Twitter) Copy link Boost Visibility with HyperTest 89% of IT leaders say making systems easier to watch is a top goal for 2025. But picking the right tool from hundreds of options for your tech setup can be tough. Teams often use scattered tools that don't show a complete, up-to-date picture of their microservices. This causes outages and problems when rolling out new versions. What poor monitoring costs? 70% of engineering teams have downtimes because they don't watch their systems well enough. $300K per hour is what big companies lose on average when their systems go down. 58% of teams say their current monitoring stack doesn't give them real-time visibility into dependencies. The best monitoring tools help you find problems before they affect users, make your system run better, and ensure smooth rollouts. This guide will show you the top tools you can use today. In this guide👇 What makes a good monitoring tool? 10 Best Monitoring Tools for tech teams How HyperTest improves distributed tracing? Picking the right tool for your team What makes a Good Monitoring Tool? A strong monitoring tool should provide: 1. Complete Sight: A robust monitoring tool must offer visibility into every aspect of the system—applications, infrastructure (network, servers, databases), and services (APIs, microservices). Example: If an e-commerce app experiences slowdowns, monitoring should help pinpoint whether it's due to a database bottleneck, an overloaded backend service, or a failure in a third-party API. Capture data type error with HyperTest 2. Rapid Notifications: The tool should send alerts before users start to notice issues, allowing teams to address problems proactively. Example: If a payment gateway API begins to respond slowly, the system should alert the team before customers experience transaction failures. See how HyperTest helped Zoop.in in improving visibility into their system 3. Workflow Understanding: It should map and track dependencies between microservices, enabling teams to see how failures affect various parts of the system. Example: If a user authentication service goes down, the system should indicate which services (like checkout or user dashboards) are impacted. 4. Intuitive Issue Detection: The tool should efficiently identify problems without necessitating extensive manual investigation. Example: Rather than simply displaying high CPU usage, a smart monitoring tool would link it to a specific failing API request or a sudden surge in traffic. 5. Adaptive Compatibility: It should function seamlessly across various environments—on-premises, cloud, or hybrid setups. Example: If a company shifts part of its workload to AWS while retaining some services on private servers, the monitoring tool should still deliver a cohesive view of both. 10 Top Monitoring Tools for Tech Teams ➡️ Datadog This tool watches everything from top to bottom. It combines logs, measurements, and traces. Best for: Cloud-native settings and teams that need a single monitoring tool. Why it stands out: Anomaly detection driven by AI, dashboards that update in real time, and monitoring for security. Key Features: Monitoring of infrastructure and applications. Alerts you can customize, and insights based on AI. Integration with AWS GCP, and Azure for cloud-native systems. ➡️ HyperTest A tool to trace distribution and test APIs designed for microservices. Best for: Making sure upstream services stay stable during deployments. Why it stands out: It lets developers know when they're pushing a PR that might break upstream services, both direct and indirect. Key Features: Tracks APIs across microservices. Gives a clear view of distributed systems leaving no blind spots. Stops broken deployments by spotting possible failures. ✅ Try it now ➡️ Prometheus + Grafana Open-source tool to monitor and visualize made for Kubernetes and microservices. Best for: Teams that run applications in containers. Why it stands out: You can customize it a lot and it has advanced alerting based on queries. Key Features: Database that stores time-series data to collect and query metrics. Grafana integration to visualize details. Scales and remains lightweight to suit modern DevOps workflows. ➡️ New Relic An APM and infrastructure monitoring tool that provides deep insights into applications. Best for: Debugging and troubleshooting in real time. Why it stands out: It uses AI to detect anomalies and trace distribution. Key Features: Insights at code level to tune performance. Visibility from end to end across applications and what they depend on. Supports OpenTelemetry to monitor extensively. ➡️ Elastic Observability (ELK Stack) A tool that brings together logs, metrics, and performance tracking in one place. Best for: Groups wanting a solution they can host themselves and scale up. Why it catches your eye: It's open source, so you can tweak it to your heart's content. Key Features: You can analyze and visualize logs in depth. It spots unusual patterns using AI. It works well with cloud-native setups. ➡️ Splunk This is a top-notch tool for keeping an eye on things and analyzing security data for big companies. Works best for: Big organizations that need machine learning to watch over their systems. What makes it special: It gives real-time insights into how things are running, with deep analysis. Main features: It uses AI to predict and watch for issues. You can make your own dashboards to see what's happening right now. It works well with many cloud services and tools that developers use. ➡️ Jaeger This is a free tool that tracks how information moves in systems with lots of small, connected parts. Works best for: Finding out why things are slow or not working in systems with many parts. What makes it special: It works well with OpenTelemetry right out of the box. Main features: It can see how different services depend on each other. In-depth root cause analysis. Visual display of request flows. Excels at tracing microservices and finding latency issues. Why it's unique: Built-in support for OpenTelemetry. ➡️ AppDynamics Software for tracking application performance and business data. Ideal for: Big enterprise applications. What makes it special: AI-driven monitoring of business effects. Main Features: Detailed look at code-level performance. Tracking of end-user experience. Works with business intelligence tools. ➡️ Sentry Tool for tracking errors and monitoring performance in real time. Perfect for: Developers who need to see everything from start to finish. Why it shines: spots code-level problems. Main Features: Instant bug alerts with in-depth stack traces. Speed checks with latency breakdowns. Works with major dev workflows. ➡️ Zabbix Free tool to keep an eye on infrastructure and networks. Ideal for: IT and DevOps groups watching over big setups. What makes it special: Lets you monitor with or without agents. Main Features: Custom dashboard to see live stats. Watches crucial systems to ensure they're always up. Can grow to fit big company needs. How does HyperTest make distributed tracing better? Traditional monitoring tools track logs and metrics but do not provide real-time visibility into service dependencies. HyperTest goes beyond plain tracing, providing developers immediate, actionable insight into microservices interactions—before issues hit production. ✅ Proactive Failure Detection: Identifies direct and indirect service failures prior to deployment, preventing cascading failures. ✅ End-to-End Microservices Visibility: Traces API calls between services, dbs., 3rd-party APIs etc. revealing hidden dependencies in real-time. ✅ Zero Manual Setup: Auto-traces services without additional configurations or code changes. ✅ Seamless Kubernetes & Cloud-Native Support: Ensures high availability across dynamic, container-based environments. You roll out a new feature, but a service you rely on indirectly isn't ready yet. Most tools wouldn't catch this until users run into problems. HyperTest gives developers a heads-up beforehand stopping broken rollouts and outages before they happen. HyperTest is not just a tracing solution—it's your microservices stability safety net. Picking the right tool for your team 📌 Questions to Consider: Do you need to monitor in real-time or look at past data? How important is it to trace requests across your microservices? Does your team want to spot potential failures before they happen? Will the tool work well with the tech you already use? For large-scale microservices teams , a tool like HyperTest can add to existing observability platforms helping debug faster, deploy safer, and keep services running . Monitoring is now essential—it forms the core of system reliability. Whether you pick Datadog Prometheus, or HyperTest putting money into the right tool will: ✅ Stop outages before they affect users. ✅ Cut down debug time from hours to minutes. ✅ Boost developer output with instant insights. Get a demo Related to Integration Testing Frequently Asked Questions 1. What factors should tech teams consider when choosing a monitoring tool? Teams should evaluate coverage, alerting speed, integration with existing systems, and ease of use. 2. How do cloud-native monitoring tools differ from traditional ones? Cloud-native tools offer real-time scalability, distributed tracing, and better compatibility with microservices. 3. How does HyperTest improve monitoring for microservices? HyperTest automates distributed tracing, mapping API calls across services in real-time to reveal hidden dependencies. For your next read Dive deeper with these related posts! 13 Min. Read Understanding Feature Flags: How developers use and test them? Learn More 08 Min. Read Generating Mock Data: Improve Testing Without Breaking Prod Learn More 09 Min. Read RabbitMQ vs. Kafka: When to use what and why? Learn More

Jenkins vs modern CI/CD tools—does it still lead the pack? Explore key differences, pros, and alternatives in this showdown. 25 February 2025 09 Min. Read CI/CD tools showdown: Is Jenkins still the best choice? WhatsApp LinkedIn X (Twitter) Copy link Optimize CI/CD with HyperTest Delivering quality software quickly is more important than ever in today's software development landscape. CI/CD pipelines have become essential tools for development teams to transition code from development to production. By facilitating frequent code integrations and automated deployments, CI/CD pipelines help teams steer clear of the dreaded " integration hell " and maintain a dependable software release cycle. In the fast-paced world of software development, the CI/CD tools that support these processes are crucial. Jenkins has long been a leading player in this field, recognized for its robustness and extensive plugin ecosystem. However, as new tools come onto the scene and development practices evolve, one must ask: Is Jenkins still the best option for CI/CD? Let's explore the current landscape of CI/CD tools to assess their strengths, weaknesses, and how well they meet modern development needs. Scalefast opted for Jenkins as their CI/CD solution because of its strong reputation for flexibility and its extensive plugin ecosystem, which boasts over 1,800 available plugins. Jenkins enabled Scalefast to create highly customized pipelines that integrated smoothly into their existing infrastructure. Understanding Jenkins Jenkins is an open-source automation server that empowers developers to build, test, and deploy their software. It is recognized for its: Extensive Plugin System: With more than 1,000 plugins available, Jenkins can connect with nearly any tool, from code repositories to deployment environments. Flexibility and Customizability: Users can configure Jenkins in numerous ways due to its scriptable nature. Strong Community Support: As one of the oldest players in the CI/CD market, Jenkins benefits from a large community of developers and users who contribute plugins and provide support. pipeline { agent any stages { stage('Build') { steps { sh 'make' } } stage('Test'){ steps { sh 'make test' } } stage('Deploy') { steps { sh 'make deploy' } } } } ➡️ Problems with Jenkins Jenkins has long been a staple in the CI/CD tool landscape, valued for its flexibility and extensive plugin ecosystem. However, various challenges have led teams to explore alternative CI/CD tools that may better suit contemporary development practices and infrastructure needs. Here are some prevalent issues with Jenkins: Jenkins demands a detailed, manual setup and ongoing maintenance, which can become cumbersome and time-consuming as configurations change. The management of its vast array of plugins can lead to compatibility and stability problems, necessitating regular updates and monitoring. Scaling Jenkins in large or dynamic environments often requires manual intervention and additional tools to manage resources effectively. Its user interface is often viewed as outdated, making it less user-friendly for new developers and hindering overall productivity. Jenkins has faced security vulnerabilities, primarily due to its plugin-based architecture, which requires constant vigilance and frequent security updates. While Jenkins excels in continuous integration, it falls short in robust built-in continuous deployment capabilities, often needing extra plugins or tools. Operating Jenkins can be resource-heavy, especially at scale, which may drive up costs and complicate infrastructure management. Sony Mobile transitioned from Jenkins to GitLab CI/CD because of scalability and maintenance issues. This shift to GitLab's integrated platform simplified processes and enhanced performance, resulting in a 25% reduction in build times and a 30% decrease in maintenance efforts Consequently, teams are continually seeking better CI/CD tools than Jenkins. Let’s take a look at some other prominent options now. ➡️ Competitors on the Rise Popular CI/CD Platforms, with more than 80% of the market share, are: GitHub Actions : This is a relatively new CI/CD platform from Microsoft that integrates seamlessly with its GitHub-hosted DVCS platform and GitHub Enterprise. It's an ideal option if your organization is already using GitHub for version control, has all your code stored there, and is comfortable with having your code built and tested on GitHub’s servers. JetBrains TeamCity . TeamCity is a flexible CI/CD solution that supports a variety of workflows and development practices. It allows you to create CI/CD configurations using Kotlin, taking advantage of a full-featured programming language and its extensive toolset. It natively supports languages such as Java, .NET, Python, Ruby, and Xcode, and can be extended to other languages through a rich plugin ecosystem. Additionally, TeamCity integrates with tools like Bugzilla, Docker, Jira, Maven, NuGet, Visual Studio Team Services, and YouTrack, enhancing its capabilities within your development environment. CircleCI : CircleCI is recognized for its user-friendly approach to setting up a continuous integration build system. It offers both cloud hosting and enterprise on-premise options, along with integration capabilities for GitHub, GitHub Enterprise, and Bitbucket as DVCS providers. This platform is particularly appealing if you’re already using GitHub or Bitbucket and prefer a straightforward pricing model rather than being billed by build minutes like some other hosted platforms. Azure DevOps : Azure facilitates deployments across all major cloud computing providers and provides out-of-the-box integrations for both on-premises and cloud-hosted build agents. It features Azure Pipelines as a build-and-deploy service, along with Agile Board and Test Plans for exploratory testing. Additionally, Azure Artifacts allows for the sharing of packages from both public and private registries. GitLab CI : With GitLab CI/CD, you can develop, test, deploy, and monitor your applications without needing any third-party applications or integrations. GitLab automatically identifies your programming language and uses CI/CD templates to create and run essential pipelines for building and testing your application. Once that's done, you can configure deployments to push your apps to production and staging environments. Travis CI : You can streamline your development process by automating additional steps, such as managing deployments and notifications, as well as automatically building and testing code changes. This means you can create build stages where workers rely on each other, set up notifications, prepare deployments after builds, and perform a variety of other tasks. AWS CodePipeline : This service allows you to automate your release pipelines for quick and reliable updates to your applications and infrastructure. As a fully managed continuous delivery solution, CodePipeline automates the build, test, and deploy phases of your release process every time a code change is made, based on the release model you define. Bitbucket : This add-on for Bitbucket Cloud allows users to initiate automated build, test, and deployment processes with every commit, push, or pull request. Bitbucket Pipelines integrates seamlessly with Jira, Trello, and other Atlassian products. Other tools include Bamboo, Drone, AppVeyor, Codeship, Spinnaker, IBM Cloud Continuous Delivery, CloudBees, Bitrise, Codefresh, and more. How to choose CI/CD Platform? There are several things to consider while selecting the appropriate CI/CD platform for your company: Cloud-based vs. self-hosted options . We see more and more companies transitioning to cloud-based CI tools. The web user interface (UI) for controlling your build pipelines is generally included in cloud-based CI/CD technologies, with the build agents or runners being hosted on public or private cloud infrastructure. Installation and upkeep are not necessary with a cloud-based system. With self-hosted alternatives, you may decide whether to put your build server and build agents in a private cloud, on hardware located on your premises, or on publicly accessible cloud infrastructure. User-friendliness . The platform should be easy to use and manage, with a user-friendly interface and precise documentation. Integration with your programming languages and tools . The CI/CD platform should integrate seamlessly with the tools your team already uses, including source control systems, programming languages, issue-tracking tools, and cloud platforms. Configuration . Configuring your automated CI/CD pipelines entails setting everything from the trigger starting each pipeline run to the response to a failing build or test. Scripts or a user interface (UI) can configure these settings. Knowledge about the platform . As with all tech, we should always consider whether our engineers have expertise and experience on the platform we want to select. If they don’t, we must check if we have a proper document. Some platforms are better documented, and some are not. Integrating HyperTest into Your CI/CD Pipeline Regardless of which CI/CD tool you choose, ensuring that your applications are thoroughly tested before they reach production is crucial. This is where HyperTest comes into play. HyperTest brings a refined approach to automated testing in CI/CD pipelines by focusing on changes and maximizing coverage with minimal overhead. Key Features of HyperTest: ✅ Automatic Test Generation: HyperTest automatically generates tests based on your actual network traffic, ensuring that your tests reflect real user interactions. ✅ Seamless Integration: HyperTest can be integrated with Jenkins, GitLab CI/CD, CircleCI, GitHub Actions, and other popular CI/CD tools, making it a versatile choice for any development environment. ✅ PR Validation: HyperTest analyzes pull requests (PRs) for potential issues by executing the generated tests as part of the CI/CD process. This ensures that every change is validated before it merges, significantly reducing the risk of defects reaching production. See HyperTest in Action Conclusion: Is Jenkins Still the King? Jenkins is undeniably powerful and versatile but may not be the best choice for every scenario. For organizations deeply embedded in the Jenkins ecosystem with complex, bespoke workflows, Jenkins is likely still the optimal choice. However, for newer companies or those looking to streamline their CI/CD pipelines with less overhead, tools like GitLab CI/CD, CircleCI, or GitHub Actions might be more appropriate. Choosing the right CI/CD tool is crucial, but ensuring the robustness of your continuous testing strategy is equally important. Whether you stick with Jenkins or move to newer tools like GitHub Actions or GitLab CI, integrating HyperTest can: Reduce Manual Testing Efforts: HyperTest's automatic test generation reduces the need for manual test case creation, allowing your QA team to focus on more complex testing scenarios. Catch Issues Early: With HyperTest integrated, you catch critical issues early in the development cycle, leading to fewer bugs in production. Speed Up Releases: Since HyperTest ensures thorough testing without manual intervention, it helps speed up the release process, enabling faster delivery of features and fixes to your users. Related to Integration Testing Frequently Asked Questions 1. Why is Jenkins still popular for CI/CD? Jenkins offers flexibility, a vast plugin ecosystem, and strong community support, making it a go-to choice for automation. 2. What are the main drawbacks of Jenkins? Jenkins requires high maintenance, lacks built-in scalability, and can be complex to configure compared to newer CI/CD tools. 3. What are the best alternatives to Jenkins? GitHub Actions, GitLab CI/CD, CircleCI, and ArgoCD offer modern, cloud-native automation with lower setup overhead. For your next read Dive deeper with these related posts! 07 Min. Read Choosing the right monitoring tools: Guide for Tech Teams Learn More 07 Min. Read Optimize DORA Metrics with HyperTest for better delivery Learn More 7 Min. Read How Trace IDs enhance observability in distributed systems? Learn More

Explore the inherent challenges in manual testing, from time-consuming processes to scalability issues. Learn how to navigate and overcome the top obstacles for more efficient and effective testing. 1 February 2024 09 Min. Read Top Challenges in Manual Testing WhatsApp LinkedIn X (Twitter) Copy link Download The Comparison Sheet The software development lifecycle (SDLC) has undergone significant evolution, characterized by shorter development sprints and more frequent releases. This change is driven by market demands for constant readiness for release. Consequently, the role of testing within the SDLC has become increasingly critical. In today's fast-paced development environment, where users expect regular updates and new features, manual testing can be a hindrance due to its time-consuming nature. This challenge has elevated the importance of automation testing, which has become indispensable in modern software development practices. Automation testing efficiently overcomes the limitations of manual testing, enabling quicker turnaround times and ensuring that software meets the high standards of quality and reliability required in the current market. In this blog, we will delve into the various challenges associated with manual testing of applications. While manual testing is often advisable for those at the beginning stages of development or operating with limited budgets, it is not a sustainable long-term practice. This is particularly true for repetitive tasks, which modern automation tools can handle more efficiently and effectively. What is Manual Testing? Manual testing is a process in software development where testers manually operate a software application to detect defects or bugs. Unlike automated testing, where tests are executed with the aid of scripts and tools, manual testing involves human input, analysis, and insights. Key aspects of manual testing include: Human Observation : Crucial in detecting subtle issues like user interface defects or usability problems, which automated tests might miss. Test Case Execution : Testers follow a set of predefined test cases but also use exploratory testing, where they deviate from these cases to identify unexpected behavior. Flexibility : Testers can quickly adapt and change their approach based on the application's behavior during the testing phase. Understanding User Perspective : Manual testers can provide feedback on the user experience, which is particularly valuable in ensuring the software is user-friendly and meets customer expectations. Cost-Effectiveness for Small Projects : For small-scale projects or when the testing requirements are constantly changing, manual testing can be more cost-effective than setting up automated tests. No Need for Test Script Development : This saves time initially, as there is no need to write scripts, unlike in automated testing. Want to perform automated testing without putting any efforts in writing test scripts? Identifying Visual Issues : Manual testing is more effective in identifying visual and content-related issues, such as typos, alignment issues, color consistency, and overall layout. What’s the Process of Manual Testing? Manual testing is a fundamental aspect of software development that involves a meticulous process where testers evaluate software manually to find defects. The process can be both rigorous and insightful, requiring a combination of structured test procedures and the tester's intuition. Let's break down the typical stages involved in manual testing: Understanding Requirements : The process begins with testers gaining a thorough understanding of the software requirements. This includes studying the specifications, user documentation, and design documents to comprehend what the software is intended to do. Test Plan Creation : Based on the understanding of requirements, testers develop a test plan. This plan outlines the scope, approach, resources, and schedule of intended test activities. It serves as a roadmap for the testing process. Test Case Development : Testers then create detailed test cases. These are specific conditions under which they will test the software to check if it behaves as expected. Test cases are designed to cover all aspects of the software, including functional, performance, and user interface components. Example Test Case: - Test Case ID: TC001 - Description: Verify login with valid credentials - Precondition: User is on Login Page - Steps: 1. Enter valid username 2. Enter valid password 3. Click on Login button - Expected Result: User is successfully logged in and directed to the dashboard Setting up the Test Environment : Before actual testing begins, the appropriate test environment is set up. This includes hardware and software configurations on which the software will be tested. Test Execution : During this phase, testers execute the test cases manually. They interact with the software, inputting data, and observing the outcomes to ensure that the software behaves as expected in different scenarios. Defect Logging : If a tester encounters a bug or defect, they log it in a tracking system. This includes detailed information about the defect, steps to reproduce it, and screenshots if necessary. Retesting and Regression Testing : Once defects are fixed, testers retest the software to ensure that the specific issue has been resolved. They also perform regression testing to check if the new changes haven’t adversely affected existing functionalities. Perform regression testing with ease with HyperTest and never let a bug leak to production! Know about the approach now! Reporting and Feedback : Testers prepare a final report summarizing the testing activities, including the number of tests conducted, defects found, and the status of the software. They also provide feedback on software quality and suggest improvements. Test Summary Report: - Total Test Cases: [Number] - Passed: [Number] - Failed: [Number] - Defects Found: [Number] - Recommendations: [Any suggestions or feedback] Final Validation and Closure : The software undergoes a final validation to ensure it meets all requirements. Upon successful validation, the testing phase is concluded. The process of manual testing is iterative and may cycle through these stages multiple times to ensure the software meets the highest standards of quality and functionality. It requires a keen eye for detail, patience, and a deep understanding of both the software and the user's perspective. How Manual Testing is different from Automation Testing? Manual testing and automation testing are two distinct approaches in software testing, each with its own set of characteristics and uses. Since we’ve already explored the concept of manual testing above, let's first understand the concept of automation testing and then move ahead with the differences. Automation Testing: Automation testing uses software tools and scripts to perform tests on the software automatically. This approach is ideal for repetitive tasks and can handle large volumes of data. Speed and Efficiency : Automated tests can be run quickly and repeatedly, which is a significant advantage for large projects. Accuracy : Reduces the risk of human error in repetitive and detailed test cases. Cost-Effective in the Long Run : While the initial investment is higher, it's more cost-effective for long-term projects. Non-UI Related Testing : Better suited for non-user interface testing such as load testing, performance testing, etc. Requires Technical Skills : Knowledge of scripting and programming is necessary to write test scripts. For better clarity, here’s a comparison table between the two types of testing: Aspect Manual Testing Automation Testing Execution Performed by human testers Performed by tools and scripts Time-Consumption Time-consuming, especially for large-scale testing Faster, can run tests repeatedly Cost Initially less costly, more for long-term Higher initial cost, but cheaper long-term Accuracy Prone to human error in repetitive tasks High accuracy, minimal human error Suitability Ideal for exploratory, usability, and ad-hoc testing Best for regression, load, and performance testing Technical Skills Required Generally not required Requires programming knowledge Flexibility More flexible in test design and execution Less flexible, requires predefined scripts Feedback on User Experience Better at assessing visual and user experience aspects Does not assess user experience Top Challenges in Manual Testing Manual testing, while essential in many scenarios, faces several key challenges. These challenges can impact the effectiveness, efficiency, and overall success of the testing process. Here we are going to discuss the most prominent challenges in manual testing as faced by majority of testers. Time-Consuming and Labor-Intensive Manual testing requires significant human effort and time, especially for large and complex applications. Consider manual testing in a retail banking application. The application's vast array of features means a significant number of test cases need to be executed. For example , just the fund transfer feature might include test cases for different types of transfers, limits, recipient management, transaction history, etc. Human Error Due to its repetitive nature, manual testing is prone to human error. Testers may miss out on executing some test cases or fail to notice some bugs. Consider a scenario where a tester needs to verify the correctness of user input fields across multiple forms. Missing even a single validation, like an email format check, can lead to undetected issues. Example Missed Test Case: - Test Case ID: TC105 - Description: Validate email format in registration form - Missed: Not executed due to oversight Difficulty in Handling Large Volume of Test Data Managing and testing with large datasets manually is challenging and inefficient. For instance, manually testing database operations with thousands of records for performance and data integrity is not only tedious but also prone to inaccuracies. Example: Healthcare Data Management System A healthcare data management system needs to manage and test thousands of patient records. The manual testing team might struggle to effectively validate data integrity and consistency, leading to potential risks in patient data management. Inconsistency in Testing Different testers may have varied interpretations and approaches, leading to inconsistencies in testing. For example, two testers might follow different paths to reproduce a bug, leading to inconsistent bug reports. For example, inconsistencies might come when testing a mobile app for delivery services, leading to varied bug reports and confusion. A particular testing team might report an issue with the GPS functionality, while another might not, depending on their approach and device used. Documentation challenges Comprehensive documentation of test cases and defects is crucial but can be burdensome. Accurately documenting the steps to reproduce a bug or the test case execution details demands meticulous attention. Bug Report Example: - Bug ID: BUG102 - Description: Shopping cart does not update item quantity - Steps to Reproduce: 1. Add item to cart 2. Change item quantity in cart 3. Cart fails to show updated quantity - Status: Open Difficulty in Regression Testing With each new release, regression testing becomes more challenging in manual testing, as testers need to re-execute a large number of test cases to ensure existing functionalities are not broken. Lets say you’re performing manual testing of a financial analytics tool since a new feature is added to the app. You need to perform manual testing for all the existing functionalities to check its compatibility with this new feature. This repetitive process can become increasingly burdensome over time, slowing down the release of new features. Limited Coverage Achieving comprehensive test coverage manually is difficult, especially for complex applications. Testers might not be able to cover all use cases, user paths, and scenarios due to time and resource constraints. Manually testing an ever-expanding application is increasingly impractical, especially when trying to meet fast-paced market demands. Complex applications often feature thousands, or even lakhs, of interconnected services, resulting in a multitude of possible user flows. Attempting to conceive every possible user interaction and subsequently creating manual test scripts for each is an unrealistic task. This often leads to numerous user flows being deployed to production without adequate testing. As a result, untested flows can introduce bugs into the system, necessitating frequent rollbacks and emergency fixes. This approach not only undermines the software's reliability but also hinders the ability to swiftly and efficiently respond to market needs. Tired of manually testing your half-found user-flows? Get rid of this and achieve up to 95% test coverage without ever writing a single line of code. See it working here. Conclusion In conclusion, manual testing remains a critical component in the software testing landscape, offering unique advantages in terms of flexibility, user experience assessment, and specific scenario testing. However, as we have seen through various examples and real-world case studies, it comes with its own set of challenges. These include being time-consuming and labor-intensive, especially for complex applications like retail banking software, susceptibility to human error, difficulties in managing large volumes of test data, limited scope for non-functional testing, and several others. The future of software testing lies in finding the right balance between manual and automated methods, ensuring that the quality of the software is upheld while keeping up with the pace of development demanded by modern markets. For more info about what we do, just swing by hypertest.co . Feel free to drop us a line anytime – we can't wait to show you how HyperTest can make your testing a breeze! 🚀🔧 Related to Integration Testing Frequently Asked Questions 1. What are limitations of manual testing? Manual testing is time-consuming, prone to human error, and lacks scalability. It struggles with repetitive tasks, limited test coverage, and challenges in handling complex scenarios, making it less efficient for large-scale or repetitive testing requirements. 2. What are the types of system testing? The main challenge lies in repetitive and time-consuming test execution. Manual testers face difficulties in managing extensive test cases, making it challenging to maintain accuracy, consistency, and efficiency over time. 3. Is manual testing difficult? Yes, manual testing can be challenging due to its labor-intensive nature, human error susceptibility, and limited scalability. Testers need meticulous attention to detail, and as testing requirements grow, managing repetitive tasks becomes more complex, making automation a valuable complement. For your next read Dive deeper with these related posts! 07 Min. Read What is Functional Testing? Types and Examples 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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Isolate your app in API testing! Learn how mocking & stubbing create controlled environments for faster, reliable integration tests. 1 July 2024 08 Min. Read Mocking & Stubbing in API Integration Testing Download the Checklist WhatsApp LinkedIn X (Twitter) Copy link Fast Facts Get a quick overview of this blog Use mocks and stubs to isolate your application's code from external services. Employ mocks and stubs to create various scenarios, including error conditions, for comprehensive testing. While mocks and stubs are valuable, avoid over-reliance. Combine them with occasional real-world testing for robust integration coverage. Checklist to Implement Integration Testing "Using mocks and stubs in our API integration tests has drastically improved our test reliability. We can now simulate various edge cases and ensure our service handles them gracefully." -John Doe, Senior Engineer As engineering managers, we all crave reliable, fast, and efficient testing . Integration testing is a key solution to test all those tiny services when 1000s of them are interacting with each other in a complex setup like Netflix. But what comes as a problem in such a situation? Every service is talking to one external service or to say the least, with the database. Having 1000s of such databases and services, up and running to proceed this communication is the issue. And here comes mocking and stubbing as an effective solution. Instead of letting the real db and service live, mocking them out for the test purpose always helps. What is API Integration Testing? API integration testing is a type of software testing that focuses on verifying how well different systems communicate with each other through APIs (Application Programming Interfaces). It essentially checks if the data exchange between these systems happens smoothly and as intended. Here's a scenario to illustrate: Imagine an e-commerce website that integrates with a payment gateway API. When a customer places an order and chooses to pay, the website would send the order details (products, price, etc.) to the payment gateway API. This API would then handle the secure payment processing and send a confirmation back to the website. 💡 API integration testing in this scenario would involve creating tests that: 1. Simulate the website sending order data to the payment gateway API. 2. Verify that the API receives the data correctly and in the expected format. 3. Check if the API interacts with the payment processor as intended (e.g., sending payment requests, handling authorization). 4. Ensure the API sends a successful confirmation response back to the website. 5. Test what happens in case of errors (e.g., insufficient funds, network issues). By testing this integration thoroughly, you can ensure a smooth checkout experience for customers and avoid issues where orders fail due to communication problems between the website and the payment gateway. Quick Question Are you Planning to Automate your Integration Testing? Yes Introduction to Mocking and Stubbing Mocking and stubbing are techniques used to simulate the behavior of real objects. They help in isolating the system under test and provide controlled responses. These techniques are particularly useful in API integration testing, where dependencies on external systems can make testing complex and unreliable. Mocking refers to creating objects that mimic the behavior of real objects. They record interactions, which can be verified later to ensure the system behaves as expected. Get to know about auto-generated mocks approach here. Stubbing involves providing predefined responses to method calls made during the test. Unlike mocks, stubs do not record interactions; they simply return the expected output. Why Mock and Stub in API Integration Testing? According to a survey by TechBeacon, 70% of development teams use mocking and stubbing in their integration tests. Isolation: Isolate the component under test from external dependencies. Control: Provide controlled responses and scenarios for testing edge cases. Speed: Reduce the time taken to run tests by eliminating dependencies on external systems. Reliability: Ensure consistent test results by avoiding flaky external dependencies. Scenario Imagine you're developing a social media scheduling application that integrates with a weather API and a fictional content delivery network (CDN) called " Nimbus ." During integration testing, you want to isolate your application's code from these external services. This ensures your tests focus on the functionality of your scheduling logic, independent of any external factors. Mocking and stubbing come in handy here. ➡️Mocking Weather API Use Case: Your application relies on a weather API to schedule social media posts based on weather conditions. Mocking: Within your integration tests, leverage a mocking framework to generate simulated responses for weather API calls. This enables testing various scenarios, like sunny days or rainy forecasts, without interacting with a real weather service. Example: Consider this weather API endpoint (GET request) for a specific location: GET https://api.weather.com/v1/forecasts/hourly/3day?geocode=40.7128,-74.0059 Mocked Sunny Day Response: { "location": { "name": "New York City, NY" }, "forecast": [ { "period": 0, "conditions": "Sunny", "temperature": 78 }, { "period": 1, "conditions": "Clear Skies", "temperature": 65 }, // ... (other hourly forecasts for 3 days) ] } Mocked Rainy Day Response (for testing alternative scenarios): { "location": { "name": "New York City, NY" }, "forecast": [ { "period": 0, "conditions": "Rain", "temperature": 52 }, { "period": 1, "conditions": "Scattered Showers", "temperature": 50 }, // ... (other hourly forecasts for 3 days) ] } ➡️Stubbing Nimbus CDN API Use Case: Your application interacts with the Nimbus CDN API to upload and schedule social media content for delivery. Stubbing: During integration tests, create stubs that mimic the expected behavior of the Nimbus CDN API. These stubs provide predefined responses to your application, simulating the functionality of uploading and scheduling content without requiring a live connection to the actual Nimbus service. Advantages: Stubbing the Nimbus CDN API ensures your integration tests are not affected by the availability or changes in the external service. This allows you to focus solely on testing your application's logic for scheduling content delivery. Benefits of Mocking and Stubbing: Isolation: Mocks and stubs isolate your application's code from external dependencies. This allows you to test your app's logic in a controlled environment, independent of the availability or behavior of external services. Going back to the food delivery app, you can mock the Restaurant API to test your order processing logic without placing real orders or stub the Payment Gateway to verify how your app handles successful or failed transactions without processing actual payments. Speed and Reliability: Tests that interact with external services can be slow and unreliable due to network delays or external service outages. By mocking and stubbing, you can create predictable responses, making your tests faster and more reliable. Mocking the Restaurant API ensures your tests run quickly without waiting for real API responses, and stubbing the Payment Gateway guarantees consistent test results regardless of the payment gateway's actual state. Testing Edge Cases: Mocks and stubs allow you to simulate various scenarios, including error conditions or unexpected responses from external services. In the food delivery app example, you could mock the Restaurant API to return an empty menu (testing how your app handles unavailable items) or stub the Payment Gateway to simulate a declined transaction (ensuring your app gracefully handles payment failures). Pain Points of Mocking and Stubbing: Imagine your mocks are like training wheels on a bike. They help you get started, but if you never take them off, you'll never learn to ride on your own. Over-Mocking: Over-reliance on mocks and stubs can lead to a situation where your tests don't accurately reflect the real-world behavior of your application. For instance, if you always mock the Restaurant API to return successful responses, you might miss potential bugs in your app's code that arise when encountering actual errors from the API. False Positives: Mocks and stubs that are not carefully designed can lead to false positives in your tests. If a mock or stub always returns a predefined successful response, your tests might pass even if there are underlying issues in your application's logic for handling real-world scenarios. Learning Curve: Using mocking and stubbing frameworks effectively can have a learning curve for developers. Understanding the concepts and choosing the right tools can take some time and practice. Maintenance Overhead: Mocking and stubbing can be great for initial tests, but keeping them up-to-date with the ever-evolving real services can be a burden. 💡 How HyperTest solves this problem? HyperTest mocks external components and auto-refreshes mocks when dependencies change behavior. Want to learn more about this approach? Click here . Conclusion Mocking and stubbing are powerful tools for integration testing, but they should be used judiciously. By understanding their benefits and pain points, you can leverage them to write efficient and reliable tests that ensure the smooth integration of your application with external services. Remember, a balanced approach that combines mocks and stubs with occasional tests against real external services can provide the most comprehensive test coverage for your integration points. Community Favourite Reads Confidently implement effective mocks for accurate tests. Learn More Masterclass on Contract Testing: The Key to Robust Applications Watch Now Related to Integration Testing Frequently Asked Questions 1. What is stubbing in API integration testing? Stubbing in API integration testing involves creating lightweight substitutes for external APIs. These stubs provide pre-defined responses to your application's requests, mimicking the behavior of the real API without actual interaction. This allows you to test your application's logic in isolation and control how it reacts to different scenarios. 2. What is mocking in API integration testing? Mocking in API integration testing uses a mocking framework to create more sophisticated simulations of external APIs. Mocks can not only provide pre-defined responses but also verify how your application interacts with them. They can check if your code calls the API with the correct parameters and handle different response formats. 3. Can stubs and mocks be used together in API testing? Absolutely! Stubs and mocks can be powerful allies in API testing. You can use stubs for simpler interactions where just the response data matters. Meanwhile, mocks are ideal for complex scenarios where verifying how your application interacts with the API is crucial. For your next read Dive deeper with these related posts! 13 Min. Read What is Integration Testing Learn More 07 Min. Read How Integration Testing Improve Your Software? 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Discover why PACTFlow might not fully meet your contract testing needs and what alternatives you should consider. 18 March 2025 09 Min. Read Why PACTFlow is not enough as a contract testing tool? Implement Contract Testing for Free WhatsApp LinkedIn X (Twitter) Copy link Contract testing has become essential in microservices architecture, ensuring that different services can reliably communicate with each other. While PACTflow has been a go-to solution for many teams, it comes with limitations that impact developer efficiency and scalability. If you are new to contract testing or PACT, I would recommend you first check out these blogs before you take a deep dive here: Contract Testing for Microservices: A Complete Guide How to Perform PACT Contract Testing: A Step-by-Step Guide The Promise of Contract Testing Contract testing addresses a critical challenge in microservices architectures: ensuring that independently developed services can communicate effectively. As systems grow more complex with dozens or hundreds of services, traditional end-to-end testing becomes impractical and inefficient. The core idea is elegantly simple: define contracts between services that act as a shared understanding of how they should interact. These contracts specify expected requests and responses, allowing teams to develop and deploy independently while maintaining confidence in their integrations. PACTflow: A Good Start, But Not Enough PACTflow has gained popularity as a contract testing tool, offering several benefits: Creates a shared understanding between consumer and provider services Enables independent development and deployment Catches integration issues early in the development cycle Serves as living documentation of service interactions However, as microservices architectures mature and scale, PACTflow reveals significant limitations: 1. Manual Contract Creation and Maintenance The biggest challenge with PACTflow is the extensive manual effort required: # Example: Manually defining a contract in a consumer test @pact.given('user exists') @pact.upon_receiving('a request for a user') @pact.with_request(method='GET', path='/user/1') @pact.will_respond_with(status=200, body={'id': 1, 'name': 'John Doe'}) def test_get_user(): # Test logic here As services evolve, these contracts need constant manual updating. In a rapidly changing environment with dozens of microservices, this becomes an overwhelming burden. 2. Complex Setup and Learning Curve Setting up PACTflow requires significant initial effort: Implementing Pact libraries in multiple languages Configuring broker services Setting up versioning and tagging Integrating with CI/CD pipelines Teams often struggle with this complexity, especially those newer to microservices or contract testing concepts. 3. Test Data Management Challenges PACTflow struggles with test data consistency: Creating representative test data for all scenarios is difficult Maintaining synchronized data across services is challenging Mock services can lead to false positives if actual behavior differs As one Reddit user put it: "Our team spent more time managing test data than actually writing tests. Eventually we just gave up on some of the more complex scenarios." 4. Multi-Consumer Complexity When multiple services depend on a single provider, managing these relationships becomes complicated: In this scenario: Provider adds a mandatory 'birthdate' field ConsumerB must update its contract and implementation ConsumerA now has a failing contract with ConsumerB Changes require careful coordination between all three teams 5. CI/CD Integration Issues Many teams struggle with integrating PACTflow into their CI/CD pipelines: Coordinating contract verification across multiple repositories Managing breaking changes during continuous deployment Handling version compatibility across services HyperTest: Bridging the Gap HyperTest addresses these limitations with an innovative approach to contract testing. It works in two modes: ✅ Record Mode HyperTest's SDK monitors your services in production or staging environments: Captures all incoming requests to your services Records outgoing requests to downstream dependencies Documents the complete sequence of service interactions Automatically generates contracts based on real usage patterns ✅ Replay Mode When testing service changes: HyperTest replays captured transactions Mocks responses from downstream dependencies Compares actual service responses with expected behavior Highlights any regressions or contract violations Book a demo now This approach eliminates the need for manual contract maintenance while ensuring that tests reflect real-world scenarios. It has several advantages over the traditional contract testing performed by PactFlow. 1. Automated Contract Generation HyperTest revolutionizes contract testing by automatically generating contracts from real traffic: Captures actual service interactions in production or staging Documents all incoming requests and outgoing responses Records real user flows, ensuring comprehensive coverage This eliminates the need for manual contract creation, saving significant development time. As one Engineering Director at Nykaa put it: "HyperTest transformed contract testing from a time sink into a competitive advantage. What once required dedicated engineering time now happens automatically." 2. Real-World Test Data HyperTest solves the test data challenge by design: Uses actual production data patterns (properly sanitized) Captures real transaction flows for authentic scenarios Automatically updates test data as production patterns evolve With HyperTest, we've eliminated our test data maintenance overhead completely. 3. Intelligent Dependency Management HyperTest automatically: Maps relationships between services Identifies potential impact of changes Notifies affected teams through integrated Slack channels This visibility helps teams collaborate effectively without extensive manual coordination. 4. Seamless CI/CD Integration HyperTest integrates elegantly with modern development workflows: Automatically runs tests on PRs and commits Provides immediate feedback on breaking changes Delivers notifications directly through Slack Enables one-click approval for intentional contract changes Engineering leads at companies using HyperTest report significant time savings and fewer production incidents related to service integration. Why HyperTest's approach matters? For engineering leaders managing complex microservices architectures, the difference between PACTflow and HyperTest goes beyond technical implementation—it impacts fundamental business metrics: Developer Productivity : Eliminating manual contract creation and maintenance frees engineers to focus on building features rather than maintaining tests. Release Velocity : Automated contract generation and verification enable faster, more confident deployments. Production Reliability : Real-world traffic-based testing catches integration issues that contrived test cases often miss. Cross-Team Collaboration : Automated dependency tracking and notifications improve coordination without manual overhead. Making the Right Choice for Your Organization To determine if HyperTest might be a better fit than PACTflow for your organization, consider these key factors: System Complexity : For larger microservices architectures (6+ services), HyperTest's automatic contract generation provides increasingly significant advantages. Resource Constraints : Teams with limited bandwidth for test maintenance will see greater ROI from HyperTest's automated approach. Development Velocity : Organizations prioritizing rapid releases benefit from HyperTest's frictionless CI/CD integration and real-time verification. Documentation Quality : HyperTest's contracts generated from actual traffic provide more accurate and comprehensive documentation than manually created contracts. Conclusion: The Future of Contract Testing Contract testing remains essential for reliable microservices architectures, but the traditional manual approach embodied by PACTflow is increasingly misaligned with modern development practices. HyperTest represents the next evolution in contract testing—automatically generating and maintaining contracts based on real interactions rather than developer assumptions. This approach not only saves significant engineering time but also produces more accurate tests that better reflect actual service behavior. For engineering leaders looking to optimize their microservices testing strategy, HyperTest offers compelling advantages: Zero manual contract maintenance Realistic testing based on actual traffic Automatic dependency tracking Seamless CI/CD integration Proactive team notifications As microservices architectures continue to grow in complexity, tools like HyperTest that reduce cognitive load and maintenance burden while improving test quality will become increasingly essential for engineering teams focused on delivering reliable software at scale. Get a demo now Related to Integration Testing Frequently Asked Questions 1. What are PACTFlow's main drawbacks for contract testing? PACTFlow may not provide sufficient support for varied message formats or complex integration scenarios, which can limit its effectiveness. 2. Can I integrate PACTFlow with other tools? While PACTFlow integrates well with some CI/CD tools, it might require additional configurations or third-party plugins to work seamlessly with other development environments. 3. What better alternatives exist to PACTFlow for contract testing? Tools like Spring Cloud Contract or tools that offer broader protocol support and detailed test configurations might serve as better alternatives. For your next read Dive deeper with these related posts! 09 Min. Read Understanding Contract Testing and its Role in Microservices Learn More 14 Min. Read PACT Contract Testing: A Step-by-Step Guide Learn More 09 Min. Read Top Contract Testing Tools Every Developer Should Know in 2024 Learn More

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Discover the importance of test execution in software development. Learn about the different stages, activities, and best practices to ensure a successful release. 12 August 2024 07 Min. Read Test Execution: Everything You Need To Know WhatsApp LinkedIn X (Twitter) Copy link Checklist for best practices Test Execution is all about running tests to see if your product or application performs as expected. After development, we move into the testing phase, where different testing techniques are applied, and test cases are created and executed. In this article, we’ll dive into what test execution involves and how it helps ensure your software meets the desired results. What is Test Execution? Test Execution is where you run tests to ensure your code, functions, or modules are delivering the results you expect based on your client or business requirements. In this phase, you will categorize and execute tests according to a detailed test plan. This plan breaks down the application into smaller components and includes specific test cases for each. You might choose to write and run these tests manually, use test scripts, or go for automated testing. If any errors pop up, you will report them so the development team can address the issues. Once your tests show successful results, your application is ready for deployment, with everything properly set up for the final stage. Significance of Test Execution Test execution takes your software projects to the next level by ensuring they run smoothly and meet global standards. When test results align with your goals, it means that you are ready to launch the product. The phase of test execution evaluates how well everyone has contributed to the project and checks if the requirements were gathered, understood, and integrated correctly into the design. By focusing on each test case—whether it's a major task like database operations or smaller details like page load times—you can significantly improve your application’s quality and support your business growth. After executing tests, you gather important data, such as which tests failed, why they failed, and any associated bugs. With this information you can easily track the progress of your testing and development teams as you release updates in future sprints. Now let us learn which activities you need to include during test execution to leverage the above-mentioned significance. Activities in Test Execution To have better test execution, developers need to be very particular in the including right test activities. This is because it allows easy identification of bugs and issues, and their early fixation. Let us learn about those test activities in brief: Defect Finding and Reporting: When you run your tests, you will identify any bugs or errors. If something goes wrong, you will record the issue and let your development team know. Sometimes, users might also spot bugs during acceptance testing and report them to developers. The developers then fix these issues based on your reports. Defect Mapping: Once the development team has addressed the bugs, you need to include test activities of re-testing. This involves testing of the unit or component of a software application to ensure everything now works as expected. Re-Testing: Re-Testing means running the tests again to confirm that no new issues have appeared, especially after adding new features. This helps you to ensure a smooth release. Regression Testing: It verifies that recent modifications have not interfered with current features of the software application. This makes sure the continuous working of your application. System Integration Testing: This involves testing the entire system at one time to confirm that all components operate smoothly together. HyperTest is your go-to no-code automation tool, making it easy to integrate into your codebase and quickly create unit tests for various service interfaces. With HyperTest, you can let the tool autogenerate integration tests by analyzing network traffic, so you can spend less time on manual setup. Stages of Test Execution Following are the stages of test execution that you need to follow: Test Planning or Preparation Before you move into test execution, you need to make sure that you have everything set. This means finalizing your test plan, designing test cases, and setting up your tools. You should have a process for tracking test data and reporting defects, with clear instructions available for your team. Your preparation should cover: Designing your test strategy Defining objectives and criteria Determining deliverables Ensuring all resources are ready Setting up the test environment Providing necessary tools to your testers Test Execution With everything in place, it's time to execute your test cases. Testers will run the code, compare the expected results with the actual outcomes, and mark the status of each test case. You will need to report, log, and map any defects. This stage also involves retesting to confirm that issues have been resolved and regression testing to ensure that fixes haven’t introduced new issue. It involves steps like creating test case, writing the test script and then running the test case. Test Evaluation After execution, check that if you have met all your deliverables and exit criteria. This means verifying that all tests were run, defects were logged and addressed, and summary reports are prepared. Now let us be more specific to test execution and see what the different ways are we can use to execute the test of software applications. Ways to Perform Test Execution Run Test Cases Simply run your test cases on your local machine. You can enhance this by combining it with other elements like test plans and test environments to streamline your process. Run Test Suites Use test suites to execute multiple test cases together. You can run them sequentially or in parallel, depending on whether the outcome of one test relies on the previous one. Record Test Execution Document your test case and test suite executions. This practice helps reduce errors and improves the efficiency of your testing by keeping track of your progress. Generate Test Results without Execution Sometimes, you can generate test results for cases that haven’t been executed yet. This approach helps ensure you have comprehensive test coverage. Modify Execution Variables Adjust execution variables in your test scripts to fit different test scenarios. This flexibility allows you to tailor tests to specific needs. Run Automated and Manual Tests Decide whether to run your tests manually or automate them. Each method has its advantages, so choose based on what works best for your situation. Schedule Test Artefacts Use artefacts like videos, screenshots, and data reports to document past tests. This helps you review previous results and plan for future testing. Track Defects Keep track of any defects that arise during testing. Identifying what went wrong and where helps you address issues effectively and improves your overall testing process. Now knowing the different ways by which you can run the test execution, it is important to note the current state of test execution. But how? Read the below section. States of Test Execution Having good understanding of the test execution states will help developer to manage the test process. It helps to measure the progress and evaluate whether their software is functioning as expected. Here’s a quick guide to the key execution states: Pass : Your test has run successfully and achieved the intended results, showing that everything is working as it should. Fail : The test did not meet your expected results. Inconclusive: The test outcome is not at all clear. Block : The test cannot be executed because some requirements have yet to be met. You will need to resolve these issues before proceeding. Deferred : The test has not been run yet but is planned for a future phase or release. In Progress: The test is currently underway, and you are actively executing it. Not Run: The test has not been started so no results are available yet. Best Practice for Test Execution Here’s how you can ensure a smooth test execution process: Write Test Cases Create detailed test cases for each module of your function. This step helps in assessing every part of your application effectively. Assign Test Cases Allocate these test cases to their respective modules or functions. Proper assignment ensures that each area of your application is tested thoroughly. Perform Testing Carry out both manual and automated testing to achieve accurate results. This combined approach helps cover all bases. Choose an Automated Tool Select a suitable automated testing tool for your application. The right tool can streamline your testing process and improve efficiency. Set Up the Test Environment Ensure your test environment is correctly set up. This setup is crucial for simulating real-world conditions and obtaining reliable results. Run HyperTest from any environment be it staging, pre or production and catch all regressions beforehand. Record Execution Status Document the status of each test case and track how long the system takes to complete them. This helps in analyzing performance and identifying bottlenecks. Report Results Regularly report both successful and failed test results to the development team. Keeping them informed helps in quick resolution of issues. Recheck Failed Tests Monitor and recheck any previously failed test cases. Update the team on any progress or persistent issues to ensure continuous improvement. Conclusion In your software development life cycle, Test execution is crucial for spotting defects, bugs, and issues. It’s an integral part of the testing process, helping you ensure that your product meets end-user requirements and delivers the right services. By focusing on Test execution, you can create a more reliable and user-friendly product. Related to Integration Testing Frequently Asked Questions 1. What is the purpose of test execution in software development? Test execution is crucial for ensuring software quality and identifying potential issues before release. It helps verify that the software meets requirements, functions as intended, and delivers the desired user experience. 2. What is shift left testing approach in performance testing? The key stages of test execution include test planning, test case design, test environment setup, test execution, defect tracking and reporting, and test evaluation. Each stage plays a vital role in the overall testing process. 3. How can test execution be made more efficient? Test execution can be made more efficient by leveraging automation tools, writing clear and concise test cases, prioritizing test cases based on risk, and continuously improving the testing process through feedback and analysis. For your next read Dive deeper with these related posts! 09 Min. Read Code Coverage vs. Test Coverage: Pros and Cons Learn More 12 Min. 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