API testing and development answers

Automated software testing is the practice of creating, running, and analyzing tests automatically (typically with specialized tooling and testing frameworks), with the goal of minimizing time-consuming and repetitive tasks experienced by manual testers. While using a software test automation…

Knowing the benefits of software test automation is only one piece of the puzzle. Arguably, it is more important to know where to implement it. In many development workflows, such as CI/CD pipelines, it is common to require tests to be run automatically before code is merged or deployed to ensure…

Automated testing allows for the shifting of process development, test automation framework development, test maintenance, and more away from active development. Testing is an ongoing and critical function of any software development process, and this is especially true of the application…

With users expecting quick, reliable, and efficient interactions, your app’s performance directly influences their experience and your app’s overall reputation. It’s not just about functionality; it’s about delivering that functionality swiftly and smoothly. Tracking and optimizing overall app…

Regular testing and monitoring are essential to identify and improve overall app performance. Start by defining and tracking essential Key Performance Indicators (KPIs) such as load times and error rates. These app performance metrics will help you establish benchmarks, pinpoint bottlenecks, and…

Golang testing frameworks are essential tools that streamline the testing process, making it more efficient and effective. These frameworks provide a suite of features designed to help developers write and run tests, ensuring that their code functions as intended and catching bugs early in the…

It’s always important to use the right tool for the job. You can get far with the basic testing capabilities provided by the testing package and go test command. However, it falls short when it comes to larger tests and assertions, which is where test frameworks come in. In this post, you’ve seen a…

Service virtualization is a technique used in software development to simulate the behavior of specific software components or systems that are otherwise unavailable or difficult to access during development and testing. By using a virtualization solution in your testing environment, you can mimic…

Service virtualization utilizes several approaches to create realistic simulations of software components and structures. This is broadly separated into four distinct categories, with product offerings specializing in each domain. The tooling is designed to capture traffic and behavior between…

API mocking creates simple request/response stubs, typically hand-written by developers. Service virtualization goes further — it captures real traffic patterns, simulates stateful behavior, and models performance characteristics like latency and throughput. Service virtualization tools are…

No. Service virtualization works with any architecture that has external dependencies — monoliths calling third-party APIs, mainframe systems communicating over MQ, or microservices calling each other. The technique is architecture-agnostic.

Service virtualization tools like Speedscale and Proxymock integrate directly into CI/CD pipelines. Instead of spinning up real dependencies for every test run, your pipeline uses virtualized services that respond with production-realistic data, making tests faster, cheaper, and more reliable.

Hoverfly is an open-source API simulation tool focused on capture-and-replay for lightweight testing. Speedscale is an enterprise platform that adds production traffic replay, load testing, and automatic mock generation from real Kubernetes traffic. Hoverfly works well for individual developers;…

Continuous performance testing, also known as a continuous performance test, is the continuous monitoring of the performance of an application under increased load. It’s typically done on every code commit, eliminating the need for manual performance tests that can be time-consuming and expensive.…

There are several types of performance testing, but one that is often used is load testing. Load tests are designed to simulate the activity of multiple users accessing software simultaneously. Similar to stress testing, the goal is to test the limits of how many requests the application can…

This depends upon the company’s needs, but generally speaking, some companies will find continuous performance tests more valuable than others. These companies typically have: A large user base High volumes of regular interaction and/or seasonal spikes in traffic Invested a lot of time or money…

According to the Akamai Performance report, 49% of customers expect webpages to fully load within two seconds. 18% of customers expect instant page load. In this day and age, users believe that waiting more than two to three seconds for a page to load is unacceptable. When they encounter issues,…

To begin continuous performance testing, first make sure that you have a continuous integration pipeline, or CI pipeline, in place. Integrating continuous performance testing is crucial for enhancing software performance and user experience. Start by setting up a structured approach that includes…

AI-generated code can indeed be deployed to production, but it requires validation first. Treat it like code from a capable but junior developer, requiring review and testing before deployment, rather than a “rubber stamp”. The key is building validation processes that catch AI-specific issues,…

Better prompting helps significantly. Be specific about libraries and versions, provide context about your architecture and constraints, include example inputs and expected outputs, and explicitly ask for error handling. Provide documentation or examples to guide the AI’s approach.

Hallucinated code occurs when AI generates functions, methods, or APIs that appear plausible but don’t actually exist. You can catch it by systematically verifying that every function call references real library methods, using IDE error checking, and validating against official documentation.…

Yes, several significant risks. The code might contain hallucinated functions, security vulnerabilities, outdated patterns, or logic that doesn’t align with your specific requirements. AI code from chat platforms also lacks context about your application architecture. Always validate, test, and…

Kubernetes visibility refers to the ability to monitor and understand the state and behavior of all aspects of a Kubernetes environment. This includes underlying infrastructure as well as application traffic.

Visibility into your Kubernetes deployment can improve performance in several aspects: Proactive Monitoring: Continuously monitoring pods, nodes, and services helps teams detect and address potential issues before they impact application performance metrics. Resource Allocation: Visibility tools…

However, achieving full visibility in Kubernetes can be extra challenging. For starters this is because: Complexity of Architecture: Kubernetes environments are highly dynamic and distributed. There are also differences between your own application versus Kubernetes overhead such as Kubernetes…

While vanilla Kubernetes offers basic monitoring tools via the Kubernetes API (this powers tools like kubectl and the Kubernetes dashboard), these are often insufficient for comprehensive visibility. Additional tools are used for: Extended Capabilities: Specialized tools can capture more detailed…

At its core, Kubernetes uses a master-worker architecture, consisting of: Control Plane - this system manages the cluster’s state and orchestrates operations. Key components include the API server, scheduler, and other orchestration systems. Nodes (Worker Nodes) - nodes run the containerized…

In essence, Karpenter combines Kubernetes cluster awareness with instance provisioning and auto-scaling solutions. Karpenter is monitoring the Kubernetes API server for pods that cannot be scheduled based on resource demands that are unmet. When these demands are discovered, it figures out what the…

With all of this in mind, let’s take a structured look at how you can optimize Kubernetes clusters with Karpenter in practice. To do this, we’ll explore the concept of rightsizing, a process by which you can maximize efficiency and performance, and the cloud infrastructure underpinning it all.

The degree of mimicking real software components depends on your test scenarios.

A stub or test double is often used as simple, consistent returns to method calls, and are commonly used by an individual developer for unit testing (vs. shared with a team). Stubs typically return static test data and do not dynamically contain state. For example, let’s say one of your dependent…

API mocking or service mocks usually happen over a network and have a more varied library of responses. But service mocks still have trouble keeping track of state beyond a simple unit test scenario. For example, eCommerce web services could have dependent components for the shopping cart. Real…

API service virtualization usually addresses this by “learning” as much behavior about specific software components by recording the network traffic. Service virtualization is more commonly used during functional tests, integration tests, and acceptance tests. For a refresher on service…

Future solutions will require virtual services to be cheap and quick to build. As the complexity of microservices increases, it becomes virtually impossible for humans to reason about. This is why platform engineering espouses the need for internal developer platforms (IDPs), and reduces cognitive…

AI agents are at the forefront of modern software systems, designed to operate autonomously or semi-autonomously in a variety of environments. These agents leverage advanced technologies such as natural language processing and machine learning to interpret user input, analyze data, and adapt their…

AI agents are software systems that use artificial intelligence and machine learning to perform tasks autonomously. They range from simple reflex agents, which are driven by predefined rules to perform a particular and limited function, to advanced goal-based and utility-based agents that maintain…

APIs are particularly appealing to AI agents because they offer a standardized, programmatic way to interact with software systems and services. Through APIs, AI agents can capture traffic, analyze real world conditions, and replay traffic to test how systems respond under various scenarios. This…

API stands for Application Programming Interface. An API is a set of programming code that enables data transmission between one software product and another.

APIs sit between an application and the web server, acting as an intermediary layer that processes data transfer between systems. Here’s how an API works: A client application initiates an API call to retrieve information—also known as a request. This request is processed from an application to the…

While there are many aspects of API testing, it generally consists of making requests to a single or sometimes multiple API endpoints and validating the response. The purpose of API testing is to determine if the API meets expectations for functionality, performance, and security.

The most used API is a RESTful API (Representational State Transfer API). RESTful APIs allow for interoperability between different types of applications and devices on the internet.

A REST API conforms to the design principles of the REST, or representational state transfer architectural style. Restful APIs are extremely simple when it comes to building and scaling as compared to other types of APIs. When these types of APIs are put into action, they help facilitate…

REST API Testing is a web automation testing technique for testing REST-based APIs for web applications without using the user interface. The purpose of REST API testing is to record the response of REST API by sending various HTTP requests to check if REST API is working correctly. You can test a…

JSON is the most popular response data format amongst developers. JSON (JavaScript Object Notation) is a lightweight >Learn how Speedscale’s solutions can help improve your API testing & performance. Stay tuned for our upcoming post: REST API & Testing Terminology Everyone Should Know.

Traditional security tools have their place. Linters and description validators, such as Spectral and LintGPT, are excellent at enforcing consistency and design quality pre-deployment, and they’re also fairly effective at flagging common issues, including those identified in the OWASP API Security…

Simulating traffic isn’t about blasting endpoints with noise. It’s about reproducing the way legitimate API users, as well as malicious threat actors, interact with your web APIs. This can be done by capturing actual API usage via logging middleware or gateways and replaying it in test…

Automated API testing is only effective when it reflects how your APIs are actually used. Unfortunately, too many API test suites focus on ideal, expected scenarios. They validate status codes, check for expected responses, and verify schema compliance, but often overlook the importance of…

Let’s be clear here - API testing isn’t optional. APIs are Application Programming Interfaces - they are the Rosetta Stone that interfaces your system with every other system, and getting this right is the difference between universal understanding and absolute bedlam. It’s one of the most…

API testing involves a provider employing various tests to confirm their Application Programming Interfaces (APIs) function correctly, perform efficiently, are reliable, and maintain stringent security standards. API tests often focus on a wide range of specific domains, including tests that…

There are different types of API testing, each of which have their own benefits and drawbacks. Often, multiple types can be combined to make for more comprehensive API testing, ensuring comprehensive coverage of the entire API.

With the number of microservices, containers, and dependencies in modern applications, manually scripting test cases for every scenario is impractical. Traffic replay solves this by turning real production behavior into your test suite: Realistic coverage. Tests reflect actual user behavior, not…

API replay is the process of recording real API requests and responses from a live environment and playing them back against a different environment (staging, QA, or development) to validate that the application behaves correctly. It replaces manually scripted API tests with realistic production…

Replay traffic refers to the recorded HTTP requests, responses, headers, and metadata captured from a production or staging environment that are stored and subsequently sent to a target system for testing. The traffic is typically sanitized (PII removed, tokens refreshed) before replay.

Replay testing is a software testing approach where previously captured production interactions are replayed against a system under test to verify behavior, performance, and correctness. It is used for regression testing, load testing, contract validation, chaos testing, migration validation, and…

Traffic replay supports multiple API testing types: regression testing (replay old traffic against new code to catch behavioral changes), load testing (multiply captured traffic to simulate peak load), contract testing (verify API responses match expected schemas), migration testing (compare…

API mocking simulates external service responses using hand-crafted or recorded stubs. Traffic replay is broader: it captures and replays the full request/response cycle, including inbound client traffic and outbound dependency calls. In practice, the two are complementary — replay tools like…

When running tests in CI/CD, external API dependencies can be problematic: Unreliable availability: The API might be down or rate-limited. Slow performance: API calls introduce latency into test runs. Inconsistent data: Live APIs may return different responses over time. Using proxymock in CI…

Want to try this on your own machine before rolling out infrastructure-wide controls? Speedscale’s proxymock is a local-first record, replay, and mocking tool that lets you capture real API traffic and spot dynamic patterns—including hidden PII and secrets—right from your workstation. Let’s walk…

It’s 2024, and testing is still front and center in every serious software development conversation. Why? Because as our software gets more complex and user expectations skyrocket, the risks of skipping proper testing have never been higher. Modern systems are a tangled web of services, APIs, and…

Performance testing is all about making sure your software doesn’t just work—it works well, even when things get busy. Using specialized testing software, teams can simulate how real users interact with web applications, APIs, and other systems. This means throwing different types of system based…

Let’s dig deeper. Why do test tools get deprioritized? Invisible ROI: When testing works, nothing happens. That makes it hard to sell. Cultural inertia: Testing is still often seen as “QA’s problem.” Postponed investment: Tools get pushed off “until after this next release.” Developer fatigue: Most…

Speedscale was built for teams that want to ship fast without sacrificing reliability. We help you: ✅ Record real traffic from production and leverage data from real user interactions to create accurate test scenarios ✅ Replay it automatically in CI/CD, running load tests with concurrent virtual…

In this section, you’ll install Skaffold and create a simple project to get a taste of the inner development workflow with Skaffold.

A local development environment is a setup where you host your project’s files directly on your computer rather than on a remote server. This setup allows developers to test, debug, and refine their code in a safe, isolated space before it goes live. By working locally, you gain full control over…

Using Pub/Sub as part of your load test brings realism that mocks just can’t replicate. For example: Backpressure and bottlenecks – Pub/Sub enforces quotas and flow control. If your subscribers can’t keep up, Pub/Sub will happily slow things down. That’s the kind of production-like behavior you…

In the Traffic Viewer, select the traffic that you want to save as a snapshot using the time picker and filters. Click Save to Tests/Mocks. Snapshots are reusable between replay runs. Each run generates its own report with its own latency graphs, assertion results, and captured traffic, but all…

Throughput in performance testing refers to the rate at which a system or application processes requests or transactions within a specified time frame. It is a critical metric that helps evaluate the performance and scalability of a system, ensuring that it can handle a large volume of data and…

This article was primarily focused on cross-platform container builds, specifically to be run on Kubernetes linux nodes, so native darwin and windows builds were intentionally left out. The xx project does have some support for both of these target platforms, but with some pretty strict…

nettap captures plaintext TCP payloads by attaching BPF fentry and fexit trampolines to tcp_recvmsg and tcp_sendmsg, then reading the application’s buffer directly out of the struct msghdr the kernel was given. For tcp_recvmsg all of the heavy lifting was in the fexit probe since you get both the…

Ephemeral or Preview Environments help balance the need for developer productivity while maintaining high-quality code. The idea of dynamically spinning “mini-staging” environments up and down on command has enormous promise for increasing experimentation and deployment safety. In theory, it lets…

Ephemeral data, by its nature, is temporary. This data typically has a pretty specific lifespan in terms of duration of time or purpose. In other words, ephemeral data typically exists only as long as it’s useful. After that, the data is processed through an automatic deletion mechanism that…

Ephemeral data is a critical piece of network infrastructure, especially in the modern connected device landscape where privacy, security, and high-efficiency are becoming more and more important by the day. Let’s dig into some of the reasons ephemeral data is so important.

Ephemeral environment testing uses temporary, disposable environments for testing software. These environments mimic production systems by replicating configurations, dependencies, and data structures. However, they exist only for a short time, lasting just for the duration of the test or related…

Generally speaking, ephemeral environment testing involves these three steps: Create an environment that resembles the production or final application deployment environment. Run the tests. Destroy the environment. In practice, developers spin up ephemeral environments using automation and…

Setting up an ephemeral environment for testing is much like planting a seed that quickly grows, blooms, and then withers away—a process reminiscent of certain plants whose entire life cycle is completed in a matter of days. These environments are like flowers, active and vibrant for a brief period…

KPIs - or Key Performance Indicators - are quantifiable metrics that help track a software development team’s progress toward a predefined goal. In essence, the KPI gives a metric by which progress can be tracked, giving a signal both when you are pacing toward success and failure. KPIs act as a…

Multi-agent workflows are becoming the backbone of enterprise AI automation, where multiple AI agents collaborate across APIs and services to deliver outcomes that previously required significant engineering investment. These systems provide this functionality while offering a human-friendly…

Yes, and it routinely is. The Collector is widely deployed as a general-purpose data pipeline, entity events model infrastructure inventory, baggage carries routing and experiment assignment, and some teams feed usage metrics into billing. The project has also expanded its own scope with profiles,…

Not on its own. OTLP provides at-least-once delivery and the specification calls duplicate data a deliberate, correct tradeoff for telemetry. Collectors drop under backpressure by design. Anything needing completeness or exactly-once semantics requires deduplication and a durable queue you add…

The W3C Baggage specification requires propagation when the result is 64 list members or fewer and 8192 bytes or less. Past those limits entries may be discarded, and a platform that cannot propagate everything must not propagate partial members. The spec gives no guidance on which keys to drop…

Usually not. The default limit is 128 attributes per span, backends price on cardinality, and payloads carry PII across a trust boundary. If the goal is reproducing what happened, capture the traffic itself.

Threshold debates can derail adoption. Keep it mechanical. Start with the last 14 to 30 days of stable production behavior, then define an explicit tolerance band. For example, if p99 sits around 142ms, you can gate at 160ms to absorb normal variance while still catching real regressions. Do the…

No. The package notifier’s function stub is still the fastest way to ask, “Given delayed, should I send a message?” An in-memory fake is still the clearest way to ask whether a second call sees the first write. proxymock answers questions those doubles cannot: what did the application put on the…

It catches your response to a contract change. That is valuable because you can make a missing field, changed type, or renamed property happen locally and repeatably. It does not prove the vendor made that change. Use the edited copy on every commit to keep the failure behavior covered. Use fresh…

In this section, you’ll install Skaffold and create a simple project to get a taste of the inner development workflow with Skaffold.

Dependency wrapping is a nifty technique in Go programming that involves wrapping a dependency, such as a function or a struct, with another function or struct that provides additional functionality or behavior. Think of it as putting a gift in a box and then wrapping it with beautiful paper. The…

This is a very hard question to answer - that being said; we can give you an answer based on our specific use case here at Speedscale.

GraphQL is a client-driven query language over HTTP and JSON, letting clients ask for exactly the data they need from a single endpoint. gRPC is an RPC framework over HTTP/2 and binary Protocol Buffers, focused on exposing service methods with strong typing and bidirectional streaming. GraphQL…

Schema drift. Our backend microservices used protobuf while the front end used a separate GraphQL schema, so every protobuf change had to be duplicated with custom translation logic like converting a map to an array. That was manual, error-prone, and slowed the team down, so we moved the…

In our migration the web client needed roughly 50 to 90% less processing, and network data volume dropped about 70% on average, with even larger gains when messages had a high metadata-to-payload ratio. Schema drift also started surfacing at compile time instead of at runtime.

HTTP/2 maturity was the main tax: nginx required all traffic to be HTTP/2 for grpc_pass to a TLS endpoint, AWS ALB support was thin, we had to maintain certificates for every egress component, and our synthetic monitoring didn’t support HTTP/2. gRPC debugging also needs a new skillset since you…

gRPC code utilizes the HTTP/2 protocol, which offers multiple ways to improve performance: Header compression and reuse to reduce the message size Multiplexing to send multiple requests and receive multiple responses simultaneously over a single TCP connection Persistent TCP connections for…

You might assume HTTP/2 introduces entirely new browser-server interaction features, yet it essentially represents incremental enhancements to the traditional HTTP/1 methods. Let’s go over a few things to consider when testing HTTP/2 APIs.

GoMock is a testing package provided by the Golang team. It integrates with the rest of the Go ecosystem, like go:generate and the built-in testing package. GoMock isn’t the only Golang testing framework, but it’s the preferred tool for many because of its powerful type-safe assertions. As you’ll…

As mentioned in the introduction, a mock is a kind of simulation where you can make requests to the Kubernetes API server with the goal of getting realistic responses. These responses can be used to analyze traffic, detect failures, improve performance, and more. There are two different kinds of…

Production traffic replication imitates real user interactions by capturing user data in a live environment and then replaying it in a testing environment. For example, if you have an online bookstore, conventional test scenarios might cover common use cases like searching for a book, adding it to…

Learn network implementations, how much of the environment to replicate, and best practices when creating mocks.

As the name suggests, MockServer is a powerful tool that allows developers to mock HTTP APIs, making it much easier and more efficient to run an integration test in initial development phases. It aims to solve the problem of testing APIs that are still in development or not yet available, allowing…

Aiming to provide value to as many as possible, this tutorial will use one of the language-agnostic options: docker-compose. MockServer will be used to establish a mock HTTP API for a hypothetical online game platform. This platform offers a RESTful API that game developers use to fetch player…

In any Kubernetes setup, you will have at least one cluster with at least one node. On these nodes, which are essentially just virtual machines, applications will be run inside pods. When you’re working with autoscaling, you’re typically referring to either vertical or horizontal scaling. This…

Record a small sample of real LLM API calls, then use a service mock to replay the responses. This lets you simulate thousands of concurrent users hitting your application while the mock returns realistic responses at a fraction of the cost.

Focus on latency (time-to-first-token and total response time), throughput (requests per second your app can sustain), error rate (timeouts and rate limit hits), and cost per request. Compare these against your provider’s rate limits to find your ceiling.

Yes. Tools like Proxymock and Speedscale record production LLM traffic and replay it as a mock. Your CI pipeline runs against the mock, getting realistic responses with deterministic behavior — no API key needed, no variable costs, no flaky tests from provider outages.

Most of our services are pure Go, so building and packaging them for multiple platforms is a non-issue. It’s an extremely simple setup: let Go do the cross-compilation, then use a COPY-only Dockerfile to package it. However, a few projects aren’t that simple and require CGO to link against C…

Think of a sub-agent as a tiny, self-contained AI. They live as .md files in a .claude/agents directory in your project’s root. For more details on how to implement them, check out the official documentation. Each one has its own: Clean Context Window: This is the most important part. When you call…

Speedscale provides a reliable way to test and deliver applications by simulating high traffic volumes that can then be used for your different testing scenarios. Speedscale helps you pinpoint performance bottlenecks by first capturing actual traffic from your users in a late-stage environment,…

Speedscale allows developers to replicate real-world traffic in a controlled local environment, enabling thorough testing of new features, bug fixes, and performance improvements. By replaying actual user traffic, Speedscale helps identify potential issues early, providing insights into how…

Performance testing in a Kubernetes environment is essential for ensuring applications can handle high network traffic, scale effectively, and maintain high performance under stress. Conducting distributed load tests allows teams to simulate real-world scenarios, such as thousands of virtual users…

Based on the evaluation points of this post—ease of use, CI/CD integration, and documentation quality—Speedscale and K6 emerge as strong choices for Kubernetes load testing. Each tool has its pros and cons. Speedscale prefers an Operator approach to setup which allows for much deeper Kubernetes…

Speedscale requires more setup in order to make a test, where K6 can be created without any prior setup. Which tool has the advantage depends heavily on what your use case is. K6 can test your services quickly, but you will inevitably also have to be testing the dependencies of your service, unless…

As mentioned in the introduction, these two tools aren’t meant to be substitutions of each other, as they don’t have the same functionality. Because of this, it’s difficult to compare the two tools as it’s like comparing apples and oranges. They look the same, many will even put them in the same…

As you can see, these two tools serve very different use cases. Rather than trying to figure out which tool to use, it’s more important to figure out when one of these two tools should be used. You’ll often find that you should be implementing both, especially as your company scales. Docker will be…

With all this talk about how Docker and Kubernetes work together, you’d imagine that Kubernetes uses Docker under the hood. The truth is that it doesn’t. With Kubernetes, you can choose what runtime you want to use, and at a point, it was indeed possible to use Docker as the container runtime.…

Firstly, let’s define what LLM testing even is. LLM testing is the process of taking a Large Language Model and evaluating it through a series of LLM testing tools. The results of these test scenarios - the LLM outputs - are then rigorously reviewed for everything from quality to speed in…

Mocks are essential in software development, particularly for testing code in isolation without the unpredictability of real external dependencies. By replacing real components with mocks, developers can focus on testing the logic of their code, leading to more efficient, reliable, and maintainable…

The problem with testing in Kubernetes is that there’s some fundamental misalignment between the Kubernetes approach and the approach to testing common in CI/CD pipelines. Kubernetes is a dynamic system - pods are created and destroyed constantly, services scale up and down, and configurations…

An alternative approach to replicating in synthetic data is to utilize the actual data from your service. A good process for this is Traffic Replay. Traffic replay is the process of capturing traffic, the actual requests and responses from your API server and the constituent parts in the network,…

Use a stub to put the application into a specific condition. A declined card, an empty search result, or a dependency timeout can be difficult to reproduce on demand against a live service. A stub makes the condition repeatable. For the checkout example, set the gateway response to {"status":…

Use a mock when the application must perform a particular interaction. A payment should use the correct amount. An email should go to the intended recipient. A failed operation might need to avoid writing a success record. Assert the arguments or behavior that express that requirement. Avoid…

A stub supplies a controlled response. A mock verifies an expected interaction with a dependency. One test-double library can support both uses.

Yes. A mock object can return configured responses and record calls. The assertions determine whether the test checks application output, interactions, or both.

Choose based on the requirement. Use response stubbing to test outcomes under controlled conditions and interaction verification when a dependency call is part of the contract.

A unit-test mock replaces an object inside a process. An API mock server supplies responses across a network boundary and may also verify received requests.

MySQL is one of the world’s most popular open-source relational database management systems (RDBMS). It powers countless applications, from small web apps to enterprise-grade systems. But when it comes to local development and CI/CD testing, running a full MySQL instance can be unnecessary or…

Do not start with a giant “replay everything” initiative. Start narrow. Pick one workflow with all three traits: High user impact (auth, checkout, provisioning) Frequent change velocity Existing incident history Then ship one deterministic replay gate for that path. Once the team trusts it, expand…

Orchestrated Service Virtualization is a service virtualization solution which allows you to create simulations of interconnected services, APIs, and their constituent parts within a controlled environment. In effect, you are creating a simulation of the build given a certain constraint or target,…

Traffic analysis reveals that transformation turns noisy, environment-specific network traffic into stable, portable artifacts you can reuse. Make replay deterministic: replace timestamps, UUIDs, and tokens so runs are repeatable. Improve portability: adapt prod-shaped traffic to dev/CI/staging…

Find and normalize the dynamic tokens. Detectors catch ISO-8601 timestamps, UUIDs, JWTs, and high-entropy secrets, and transform rules replace them (timestamps to now(), UUIDs to uuid()). The hard cases span multiple requests, like a JWT that gets mutated across an OAuth flow, so the classifier has…

Redact at transform time with a layered approach: exact regexes, then ML or NLP classifiers, then human overrides. For fidelity, instead of blanking a value you can replace it with one of the same shape, so a redacted customer number still looks like a customer number during replay.

Outside the data. Transforms are rules about the data, not the data itself, so keep them as small text artifacts (YAML or JSON) in version control near your tests. That makes re-runs comparable, keeps review safe, and lets you promote rule bundles from dev to CI to staging like code.

Traffic replay takes captured request and response pairs and sends the requests at your service under test to validate a change against real production patterns. It catches the edge cases, timing dependencies, and integration bugs that simple hand-written tests miss.

Three things. State mismatch, because captured traffic references data that only exists in production. Time staleness, because timestamps and tokens expire. And non-determinism, because responses contain generated UUIDs and timestamps, so exact byte comparison fails every time. You solve them with…

For most cases, replay inbound requests to your service with a live database but mock the downstream dependencies from captured outbound traffic. That is the sweet spot: you can run repeatedly, validate responses in detail, and never risk corrupting production data. Shadow traffic is fast but risky…

Define objective quality gates instead of eyeballing it: status pass rate at or above 99%, JSON-path body match rate at or above 95% with timestamps and UUIDs ignored, p95 latency delta within +10%, and mock match rate at or above 98% with zero unmatched calls. Promotion between phases is automatic…

On a prompt, Cursor sends a gRPC request to api2.cursor.sh containing your workspace launch.json, relevant code snippets (the first 4K of each selected file), a bulk upload of your directory structure, and your repository’s git status. On a large monorepo that is a lot of directory walking.

It can if you put them where it looks. Cursor uploads your launch.json verbatim, so anything sensitive left in it, like an AWS_SESSION_TOKEN or an API key, goes to the server with it. Keep secrets in dotenv or envrc and turn on Privacy Mode in a corporate setting.

Its GetDefaultModelNudgeData endpoint returned a portfolio that included Claude 3.7 Sonnet (thinking and max), Gemini 2.0 Flash, GPT-4o-mini, Claude 3.5 Haiku, and DeepSeek v3 and r1. Auto mode is not a static pick, Cursor nudges you between models server-side based on context and subscription…

Wrap it with proxymock, which records complete API requests and responses and converts binary protobuf and gRPC into readable JSON. That is how we decoded these calls. It is like Wireshark for developers instead of packet spelunkers.

Your prompt goes to api.anthropic.com/v1/messages as plain JSON, along with the model version and, through structured tool calls, whatever it reads from your working directory such as directory listings and file contents. It uses REST rather than the gRPC that Cursor uses, which tends to survive…

Yes. In our capture it rewrote a short prompt into a longer, more specific set of instructions before executing, and it broke the task into sub-tasks and parallel tool calls on its own. It behaves like a project manager, not just a raw model call.

It can request to. The remote model issues structured tool calls that can run things on your local desktop, gated by Claude’s permissions and sandboxing. What is in your working directory is effectively context it can read, so think about what lives there.

A lot. Command-level events go to statsig.anthropic.com (Statsig is owned by OpenAI) and detailed logs go to Datadog, tagged by model and user, down to token counts, cost, and flags like is_running_with_bun. Sticky organization, account, and user identifiers tie the events together.

Platform engineering is an approach that focuses on building and maintaining a centralized and reusable platform for developers within an organization. This platform offers a methodology to streamline software development and deployment processes. It allows a self-service capability that empowers…

The term Platform Engineering (PE) continues to evolve, but fundamentally, it encompasses the practice of building and maintaining the foundational infrastructure and systems that support business-critical applications. That usually means pursuing excellence in the domains of continuous integration…

Choosing between Platform Engineering and DevOps comes down to the organization’s specific needs, goals, and challenges. Both options are strong choices, and in many cases, they are complimentary, not competing. For this reason, it’s better to consider this question in terms of the strengths of…

If your organization is in the early stages of adopting agile or DevOps principles, starting with DevOps practices is an excellent option as it lays the groundwork for collaboration and automation. For mature organizations facing scalability challenges, platform engineering can complement existing…

Yes. Use the Collection Runner Performance mode to run virtual users concurrently. A functional collection run with repeated iterations is a different test.

Postman supports cloud performance runs on eligible paid plans. This tutorial uses the desktop App runner because its target is a localhost API.

No. Virtual users repeat a request workflow. Its length and response times affect the throughput the test produces.

Yes. Postman can send inbound HTTP requests while proxymock supplies recorded responses for downstream dependencies. Configure unmatched-request behavior for the isolation the test needs.

Python observability refers to the ability of a developer or provider to monitor and understand the flow and state of data within a Python application. This is often done through the analysis of traffic and data such as logs, metrics, traces, attributes of data, telemetry, and other states. Whereas…

Python code is used in an incredibly diverse set of use cases and industries - it’s likely that, even if you didn’t know it, you interacted with Python at some point today. Accordingly, this ubiquity across simple to complex use cases shows that Python is powerful but diverse - and it is this…

Before I go further, I want to make sure we want the same things. Prior to product-market-fit (PMF), I believe my job as CTO or VP of Engineering at Speedscale to be limited to the following goals: Minimize time between product experimentation and customer feedback. Drop the cost of producing each…

It’s never been cheaper and easier to bring a SaaS business app to market and you may not want to pay full-price local engineers for some kinds of work. “Web forms on top of databases” is a well worn path and that type of engineering will be most impacted by these kinds of trends. That doesn’t mean…

You’ll notice that I use the word “partner” instead of “vendor” and this is intentional. A good outsourcing partner can give you access to top shelf talent in a part of the world you could never reasonably access yourself and at a reasonable cost. The best partner can really accelerate your ability…

For project-based work it’s important to remember that you can give up the management of the people but never give up your management of the project. Here are a few tips to keep things on track: Secure Senior Technical Review - Maybe you have a hotshot CTO that can do regular code reviews. Maybe…

Software performance refers to how effectively a software application responds to specific demands. Performance encompasses several key aspects, including load time, responsiveness, and the efficiency with which it processes data. Performance metrics are vital for gauging an application’s health…

Software regression occurs when new changes introduce errors in existing parts of a software application. Regressions are typically seen more once software products become increasingly more complex.

Software regressions come from errors while programming around existing code. This could be a bug fix that resolves the original issue but introduces another issue. Regressions could also result from poor configuration file changes which cause unexpected behavior down the line. Even if these…

Once you’ve implemented the tooling for traffic replication, you need to identify which performance metrics you’ll actually monitor. Most cloud platforms and APIs can use the following as a starting point: Request duration: The average time taken for each request to be served. Network latency: The…

Before we dive into ROI calculations, let’s clarify what we mean by “digital twin” in the context of testing. A digital twin for testing is a lightweight replica of your production system that uses real traffic patterns instead of synthetic test data. Unlike traditional staging environments that…

Static analysis tools like SonarQube, ESLint, Checkstyle, and Qodo scan source code without executing it. They identify problems by examining the code’s structure, syntax, and known vulnerability patterns. What static analysis is good at: Code smells and style violations. Unused variables, overly…

Runtime validation tests code against actual production traffic patterns. Instead of analyzing code in isolation, it replays real API calls, database queries, and service-to-service interactions against the changed code and compares the behavior to a known baseline. Runtime validation catches…

No. Integration tests use synthetic data in pre-configured environments. Runtime validation replays actual production traffic. The difference is coverage: production traffic contains edge cases, timing patterns, and data shapes that synthetic tests never include. Integration tests verify what you…

It adds minutes, not hours. A typical replay of captured traffic for a single service takes 2-5 minutes depending on traffic volume. Compare that to the hours or days spent debugging a production incident caused by a behavioral regression that static analysis and unit tests missed.

Some static tools are adding AI-powered analysis that attempts to reason about runtime behavior. These are useful incremental improvements but fundamentally limited: you cannot determine how code behaves with real data without actually running it with real data. This is the halting problem in…

You can start by capturing traffic from any environment where real or realistic requests are flowing — staging, QA, or even local development with proxymock. Production traffic provides the highest fidelity, but any real traffic is better than synthetic test data for catching behavioral regressions.

E-commerce is one of the most prominent examples of industries that experience seasonal traffic spikes during events. To give you an idea of the scale, Amazon’s Prime Day in 2023 generated 15.35 trillion requests on EBS. While only a few companies in the world get to that scale, many companies…

Service mocking as part of your reference architectures can ensure your services are efficiently integrated for all possible interactions, thus improving software quality and easing infrastructure management. It’s especially useful in unit testing, where you test individual components or functions,…

Shift Left testing is a proactive approach to software testing where testing activities, such as deploying functional tests, API tests, unit testing, and even performance testing, are moved to the early stages of the software development lifecycle. Instead of waiting until the final testing phase,…

Key characteristics of Shift Left testing include: Early Bug Detection: Catching defects when they’re cheaper and faster to fix. Continuous Feedback: Integrating test results into every step of the development cycle. Comprehensive Testing: Running unit tests, integration tests, and contract testing…

Despite all this, the benefits of shift left are obvious and quite alluring. Early Bug Detection: Detect performance bottlenecks, latency issues, or resource contention before production, reducing costly rework. Defining clear performance requirements early in the process helps guide testing…

While Shift Left emphasizes early validation, many readers are likely wondering about Shift Right testing. Shift Right testing focuses on validating in production environments or real-world scenarios. Cloud performance testing plays a crucial role here, as it evaluates application performance…

Key aspects of Shift Right testing include: User Feedback and Monitoring: Gathering insights from real users to detect performance issues, usability concerns, or unexpected behaviors. Continuous Monitoring: Tracking network traffic, application access, security posture, and resource usage in live…

No. Unit tests verify individual functions work as designed. Runtime validation verifies that the entire service behaves correctly with real production data. They test different things and catch different bugs.

Yes. Speedscale captures traffic at the service boundary, so each microservice can be validated independently against its actual production traffic. This is especially valuable for teams running 50-200+ services where maintaining test environments is expensive.

Tools like BigQuery are fundamental in database management because they provide the infrastructure needed to handle large volumes of structured and unstructured data. These tools enable distributed storage and parallel processing, thus allowing you to manage petabytes of data across multiple…

HTTP/2 quirks (TLS end-to-end, inconsistent H2C upgrades, status codes buried in trailers, spotty firewall support), binary protobuf payloads that aren’t human-readable, protobuf-to-JSON dropping default values, and connection management where streams can hang. Most of it comes down to needing…

The payloads are binary protobuf, so you can’t read them in a browser or text editor without the .proto files and a decoder. HTTP/2 carries gRPC status codes in trailers that many tools hide, and the transport layer is abstracted, so you often can’t tell whether a message is truly in flight or just…

Protocol Buffers omit default values in JSON output. If an enum or field is set to its default (like 0), the serialized JSON just drops that key. It’s efficient but confusing, especially without the .proto on hand, so document how defaults behave or generate JSON that includes them.

That the message was queued successfully, not that it was sent. The real send happens asynchronously through gRPC’s internal scheduling and channel logic, so a success from SendMsg doesn’t guarantee delivery. Use debug logs or OpenTelemetry gRPC tracing to see what’s actually in flight.

The proxymock skill integrates our traffic replay tool with Claude, enabling developers to record, inspect, mock, replay, and generate API traffic without leaving their AI-assisted development environment. By observing production traffic in minute detail, proxymock adds critical context for AI…

If you’ve set up your infrastructure and want to add load tests, choosing a load testing framework can be tricky. Both Speedscale and JMeter are great options, but Speedscale is one of the best tools for Kubernetes load testing, as it was specifically built for this purpose. Speedscale is a more…

Let’s dive into what these two API mocking tools are and how they mock cloud services.

No. WireMock is still useful for deterministic integration tests where you need precise control over responses. Traffic replay complements WireMock by providing realistic test data derived from actual API responses. Many teams use WireMock for contract tests and traffic replay for regression tests.…

The demo app runs on Java 17 with Spring Boot 3.1. proxymock and Speedscale work with any Java version and any Spring Boot version because they operate at the network level, not the JVM level. Your application’s HTTP traffic is captured and replayed regardless of the Java runtime.

proxymock and Speedscale support redaction rules that strip sensitive data from recordings. API keys, passwords, and PII can be masked or replaced with tokens before the recording is stored. For the demo app, the HMAC secret is configured in application.properties and JWT tokens are regenerated…

Yes. Speedscale’s MCP integration exposes captured traffic to AI agents like Cursor and Claude Code. The AI can see real request/response pairs from production while generating handler code, closing the context gap that causes most AI-generated code failures.

It might be tempting to just throw as much synthetic data at the problem as possible and validate that your OAuth flows work. Unfortunately, synthetic testing is often quite problematic. Simple synthetic tests generate uniform HTTP requests, but they lack the dynamics of real OAuth clients and…

Container probes are diagnostic checks that run periodically against containers within a pod. The outcome of these checks allows Kubernetes to control aspects of a pod’s lifecycle, with kubernetes liveness probes being crucial for maintaining application health. In other words, they can help…

Container probes, including liveness probe configuration, are defined as a part of your application’s pod spec with several options available, most importantly being the mechanism by which they are executed. Kubernetes provides four mechanisms that can be used: exec: probes of this type will…

proxymock web is the place to write a rule next to the traffic it has to redact. Rules are workspace files at proxymock/dlprules/<id>.json, so they can be reviewed in a pull request like any other change. 1. Create the rule. Open DLP Rules under Config and click + New. The template is already a…

Teams that manage rules in Speedscale Cloud get the same model in the dashboard. Open DLP Rules, create or pick a rule, and open the Scope tab. Choose Group rule, fill in Owner, and name the workloads. The summary line above the tabs states in one sentence what the rule is and what it covers. The…

CI/CD is built around four stages: source (commit) stage, build stage, testing stage, and deploy stage. Since AI-generated code can introduce security vulnerabilities and quality issues, it’s important to incorporate AI-specific tests across each stage of the CI/CD pipeline. Source stage: The first…

These faults can happen for a large variety of reasons, ranging from simple ingress rules mismatches to more complex issues with load balancers or web servers. With ingress specifically, the problem often has to do with a fundamental misalignment between the data being used in testing and the…

In this post, you’ve seen just a simple example of how traffic replay can help you test your Ingress resources, but this is only the tip of the iceberg.

There are many different variables that can be tracked to understand system performance and system health. Fortunately, right now, there are a couple of common frameworks we can borrow from: Google SRE Handbook on Monitoring Distributed Systems with the Four Golden Signals: latency, traffic,…

The Golden Signals are an incredibly powerful set of metrics that unlock a variety of insights into system health and potential tool integrations. The Golden Signals concept is based around providing as much information as possible around system stability, user demand, resource allocation, and…

The Golden Signals provide some incredible benefits and use cases. Let’s take a look at a few of them.

Detecting anomalies in your system leverages the Golden Signals as a foundational metric, testing actual system performance against a preferred baseline. There are quite a few techniques that can be deployed to make this process more effective.

The 4 golden signals are latency (how long requests take), traffic (how much demand your system handles), errors (the rate of failed requests), and saturation (how full your system’s resources are). They were defined by Google’s SRE team as the minimum metrics needed to understand system health.

Golden signals (latency, traffic, errors, saturation) focus on user-facing service health. The USE method (utilization, saturation, errors) focuses on infrastructure resource health. Golden signals tell you “is the user experience good?” while USE tells you “are the servers healthy?” Most teams use…

Capture production traffic and replay it against a pre-release build. Measure the golden signals during replay and compare them to your production baselines. If latency spikes, throughput drops, or error rates increase, you’ve caught a regression before it reaches users. Tools like Speedscale…

Prometheus and Grafana are the most common open-source stack. Datadog, New Relic, and Dynatrace are popular commercial options. For pre-release golden signal validation, Speedscale measures all four signals during traffic replay and compares them against production baselines automatically.

The quality of AI-generated code is directly correlated with the quality of the prompts you provide. By improving your prompting technique, you can significantly reduce debugging overhead and achieve more reliable code from the outset. Be Specific About Context: Instead of asking “write a function…

AI models predict what code should look like based on training patterns, but they can’t actually check if functions exist in real libraries. They’ll confidently create pandas.DataFrame.merge_smart() or combine elements from different libraries into something that sounds right but doesn’t work. It’s…

AI-generated code can be a solid starting point, but don’t ship it blindly. Treat it like code from a junior developer—useful, but needs review and testing. With proper debugging workflows and tools like proxymock for real traffic validation, AI-generated code can definitely work in production.…

Layer your validation: write unit tests for edge cases, use traffic replay tools to test with real data patterns, do manual code reviews for business logic, and run static analysis tools. AI code breaks in creative ways, so you need multiple approaches to catch everything.

You can’t eliminate them completely, but better prompting helps a lot. Be specific about libraries and versions, paste in documentation when needed, and always validate against official docs. Using tools like proxymock to test against real production scenarios will quickly show you when the AI made…

This, of course, raises the question: what makes a Developer Experience good? While there are as many development preferences as there are developers in the world, some general categories should be focused on to deliver an exceptional developer experience.

Preventing security misconfigurations takes more than good intentions. It requires proactive effort, automation, and embedded security awareness. Regular api security testing is essential to identify vulnerabilities in APIs and ensure that security controls like authentication and rate limiting are…

“Using production data” typically means copying a database snapshot into staging. Observability-driven testing is more precise: it captures the actual API traffic (requests, responses, timing) flowing through your services and replays it as tests. You’re not just using production data. You’re using…

Production traffic must be sanitized before replay. This means stripping PII fields, redacting payment data, and rewriting auth tokens. Speedscale and proxymock handle this automatically via configurable DLP rules that scrub sensitive data before it leaves the capture pipeline. The sanitized…

So, what is data coupling at its core? Data coupling occurs when your testing is overly dependent on the specific state of the data layer. Issues such as pre-populated database records, static user IDs, or shared data setups between test cases can undermine the test results and the inferences based…

While it may seem like a minor inconvenience, data coupling has far-reaching consequences across your API lifecycle: False confidence - your test results may look green, but they’re green for the wrong reasons. Regression risks - seemingly unrelated changes to the database schema or seed data can…

To avoid data coupling, API testing needs to embrace isolation, repeatability, and determinism. Your tests should: Spin up and tear down their own test data Avoid assumptions about the application’s state Run reliably in parallel and in CI/CD Validate only the API layer, not downstream systems or…

When choosing or evaluating your API testing platform, look for platforms that support: Test data generation and cleanup CI/CD pipeline integration Test automation for functional API testing Load testing and performance monitoring Security testing and API validation Replay-based testing (traffic…

If data coupling is the silent API killer, Speedscale is your shield. Traditional API testing tools often assume clean environments, stable data, and perfectly isolated services - but that’s rarely the case in modern software systems. Speedscale flips the script by bringing real traffic into your…

API testing is a critical part of the software development process. It ensures that Application Programming Interfaces, or APIs, function as intended and designed. Testing these APIs requires some best practices and tools, especially when considering specific forms and functions, such as RESTful…

Get The Tutorial Consider a situation where a developer is working on an HTTP-based API for a complex online marketplace. Developers can define detailed requests and responses for each service. This enables thorough testing of each component in isolation, as well as their interplay, without relying…

Get The Guide When it’s time for testing, an instance of this mock struct is created and expectations are set, specifying what parameters are expected when methods are invoked, what the mock should return, and how many times a method should be called. When the tests are run, any deviation from…

With the wide range of API mocking tools available, choosing the ‘best’ one isn’t a one-size-fits-all scenario. The right tool will heavily depend on the specific needs and circumstances of your project, as well as what phase of testing you are in. Whether you require the comprehensive…

The tools in this list have been chosen mainly because of the capabilities they offer. They make debugging easier and deliver information that helps you solve problems. They are also accessible and are easy to use and set up. In addition, they are generally open source and either free or…

Traffic replay is a powerful web analytics tool that allows website owners to record and replay user sessions in real time. This feature is essential for understanding user behavior, identifying usability issues, and optimizing website performance. By replaying user sessions, website owners can…

Traffic shadowing, also called traffic mirroring, is an approach for software testing that utilizes recorded user traffic as the source of data for later replay against a service. This duplication of production traffic allows for effective testing and refactoring with data that is based on the real…

A mock service is a development tool that imitates a real software service by returning realistic responses–without requiring computed logic to produce the response. For many developers and engineering teams, this capability can be very useful, particularly for cases such as: using third-party APIs…

To run a mock service, you’ll need to decide what data to mock and how to expose this mock service.

Stress testing is more than just a checkbox in the software development process—it’s a strategic approach to safeguarding the end user experience and the business as a whole. By subjecting systems to extreme load conditions, developers can identify and fix issues that might otherwise go unnoticed…

There are several reasons why Kubernetes testing is important from a business perspective. For starters, by performing extensive Kubernetes testing, organizations can ensure high availability of applications and thus minimize costly downtime. Kubernetes testing also allows DevOps teams to…

In the process of developing software, designing and performing testing is a critical aspect of ensuring high software reliability, improving software quality, and deploying strong fit and function. The shift-right testing approach moves testing to later in your production cycle as a way of doing…

Shift-Right testing moves the focus to production post-release, and accordingly, utilizes tooling built for this process. Shift-right testing practices arose from the need to get tests that better reflect how users interact with a product and from faster deployments that leave less room for robust…

Traffic-driven testing uses real traffic for testing but doesn’t restrict you to only running tests in production. You capture the traffic and reuse it later. Like shift-right, traffic-driven testing uses real user data to power your tests, but you can manipulate and control this data. It’s…

A JSON mock API is essentially a simulated interface that mimics the behavior of a real API using predefined JSON data. This API type is particularly useful for testing front-end applications without the need for an actual backend server. It allows developers to work on the front end independently…

Creating a JSON mock API involves several steps. Firstly, you must choose the right tool to create and manage your mock. From here, you’ll need to identify your environmental constraints and desired final structure, which will allow you to actually create the environment itself. Next, you’ll need…

While MockServer is excellent for creating custom request-response mocks by hand or via configuration, Speedscale goes further by automatically capturing real-world production traffic and turning it into tests. This approach removes the guesswork of manually creating or updating mocks, ensures your…

A mock service is a development tool that imitates a real software service by returning realistic responses without requiring computed logic to produce the response. For many developers and engineering teams, this capability can be very useful, particularly for cases such as using third-party APIs…

In software testing methodologies, services are defined as being in-scope or out-of-scope. The services that are in scope are the ones you care about when testing. This can be a single service or a cluster of services. Services that are out-of-scope are the ones you don’t want to test, however they…

To run a mock service, you’ll need to decide what data to mock and how to expose this mock service.

Depending on the CDE provider, the exact specifics of setting up the environment can vary quite a bit. But here’s a general guide that discusses the major steps in the process.

Ephemeral environments are temporary environments used as infrastructure for development, testing, staging, and other non-production functions. While they replicate production-like conditions, they are generally not intended to be used as production environments. These environments are…

Ephemeral environments utilize automated systems to provision and manage resources. These systems typically leverage a cloud provider to provision resources based on a pre-determined build guide, utilizing cloud resources and replicated data to create environments for specific purposes. Once a…

Developing anything requires a high level of control. The ability to create in an environment you understand is incredibly impactful, but getting to that understanding has often required deep introspection of your data sources, the network, the systems tied into both, and the very code being worked…

A preview environment is a cloud-based, temporary environment that closely replicates the production setting of an application. These environments are designed to test code changes before they are merged into the main branch of a Git repository, offering developers and QA teams a safe space to…

When creating production-level applications, enterprises want to ensure the high availability of services. This often results in a lengthy development process that requires extensive testing for the applications or a new release. This involves testing the behavior of the application under load,…

After building our TLS capture tool and staring into the abyss of what a full observability agent would require, here’s my take:

A component that captures plaintext HTTP and HTTPS payloads from Go and non-Go (OpenSSL) applications without proxies. For Go it attaches probes to crypto/tls (*Conn).Read and Write, and for OpenSSL apps it hooks socket read/write syscalls plus the OpenSSL read and write calls, so traffic can be…

Capture has fixed target functions, simple byte-buffer data types, stateless probes, and a stable ABI, so it is a tractable 1x baseline. Full APM with spans, traces, correlation, and stack unwinding is 50 to 100x harder because the complexity grows with each language runtime, not with scope.

ABI differences across languages. Go passes arguments in version-dependent registers and strips symbols in production, the JVM and .NET generate code at runtime so addresses move, and interpreted languages force you to walk interpreter structs to find a single variable. On top of that, uprobes in a…

Use it for targeted, narrow-scope instrumentation like network capture, infrastructure monitoring, and security enforcement, where the surface area is small and bounded. Avoid it as a single-program solution for general-purpose APM across many languages, deep application-level tracing with full…

Memory leaks can happen due to programming errors, poorly written algorithms, or resource-intensive processes. As available memory depletes over time, the system’s performance suffers, causing increased load times and bad user experiences. This can cause long-term tack on effects across the entire…

Production traffic replication is a technique that uses traffic replay and automatic mocks to isolate a service under testing, providing a realistic environment for performance testing. Deeper dives can be found in other posts like the definitive guide to traffic replay or the advantages of mock…

Identifying and isolating memory leaks in complex systems like Kubernetes can be a daunting challenge for developers, but it’s crucial to maintain optimal system performance and prevent cyber attacks. Using production traffic replication tools like Speedscale can make this process much more…

Load testing is a type of software performance testing that verifies the stability and scalability of a software application by subjecting it to a set amount of expected usage. Imagine this scenario: you’re launching a new eCommerce website or a highly anticipated new product. After completing your…

Load testing can benefit everyone, including developers, the greater organization, and even end-users. Developers benefit from the ability to identify issues or bottlenecks without disrupting user experience and relying on real-user reports. They can then fix these bottlenecks preemptively rather…

Despite many great use cases for load tests, it may not be optimal in some situations. For example, if you’re a small startup still trying to find product-market fit, load testing may not be a top priority. Similarly, if your application has low usage, or serves mainly static content, the benefits…

Load testing is critical for determining the resiliency and scalability of an application under different kinds of load. It measures an application’s performance and stability by simulating real usage within realistic production conditions. In theory, you should measure any metric that can identify…

Resilience testing is a type of software testing where a system is tested against simulated adverse conditions. Resilience testing focuses on testing the software against common issues like hardware or system failures, network interruptions, or high traffic loads. Resilience testing can help test…

Chaos testing, sometimes referred to as chaos engineering, is a method of resilience testing that intentionally introduces faults and unpredictability into the system. Chaos testing aims to identify weaknesses or vulnerabilities in a system that may not have surfaced during regular testing. By…

Tailoring your resilience strategies to your specific user experience and business requirements is critical. An e-commerce app might prioritize maintaining its payment systems over its recommendation engine during a fault. However, a social media application might prioritize user feeds over direct…

Resilience testing works by causing controlled chaos in a very controllable environment. In essence, you are trying to systematically subject the service or system to conditions that would impact its operation in a way that doesn’t affect the actual operations of the service. Providers typically…

Resilience testing, and chaos testing especially, is crucial for a health service to maintain its flexibility and ensure a specific level of coordinated service. Consider the infamous 2012 AWS outage. Many services experienced significant downtime, yet Netflix remained largely operational due to…

Service virtualization is a critical tool for solving the intrinsic problems of testing a production system. Testing the actual services for faults or issues means invoking those problems, and as such, it has a necessary impact on the production services themselves—by virtualizing those systems,…

Simply put, shadow traffic refers to web traffic that standard analytics tools fail to accurately capture, analyze, or record. This traffic can arise for a variety of reasons, but ultimately they reflect a dark data source which is ill-defined and hard to surface - hence the term “shadow-sm”.…

Effective management of shadow traffic involves proactive measures and continuous monitoring. You can help manage shadow traffic by implementing solutions such as server-side tracking, which shift tracking responsibilities from the client-side to the server-side. You can also deploy firewall and…

Traffic mirroring sounds straightforward—it involves capturing network traffic and replicating it to another location. In local networking, this is often called port mirroring or a network tap, but in web applications, this is commonly referred to as traffic capture and replay. While the process is…

Volume testing — also known as flood testing — examines the stability and response time of a system by transferring huge volumes of data, and evaluates a variety of different system components like databases, software, etc. This type of software testing is a critical part of avoiding issues under…

Your volume testing checklist must be created in detail, and should represent the live environment conditions as closely as possible. To achieve this, your test cases should cover all the use scenarios and data needed to accurately simulate production conditions, including the real-life data your…

Yes. Every command you run, along with its output, exit code, working directory, and shell details, is sent to Warp’s /analytics/block endpoint and associated with your account. Warp applies secret redaction, but credential patterns in shell output are too varied to catch reliably, so vendor trust…

A protobuf request to /ai/multi-agent or /ai/passive-suggestions that includes your prompt, your entire AGENTS.md file, and every skill definition in ~/.claude/skills/ with full file paths. Our capture included 24+ skills covering internal infrastructure tools. This happens on AI autocomplete too,…

Routing is server-side and doesn’t always match the model you pick in the UI. One conversation in our test used GPT-5.2 and GLM 5, a model from Zhipu AI in China. The catalog has about 25 models from four providers, and the client never contacts OpenAI or Anthropic directly. Everything routes…

Its core API calls bypass the HTTP_PROXY variable, so tools like Charles, Proxyman, and proxymock don’t see them. It uses rustls with NoKeyLog, which disables SSLKEYLOGFILE so raw packet captures can’t be decrypted, and the AI endpoints use protobuf instead of JSON. We got in with transparent TLS…

The ones whose truth is in the request or response. In my benchmark, race and write-path bugs went from 22% to 89% with traffic, state-machine transitions from 72% to 92%, streaming and multipart framing from 52% to 85%, and cross-service contract drift from 44% to 81%. Bugs that live on the wire…

A little, but far less than payloads. Testing four ways (alert alone, alert plus service map, alert plus traffic, alert plus both), the map alone added 6 percentage points while traffic added 28, and traffic still added more than 20 on top of the map. The failing request carries the broken field…

Deep framework internals, where the fix hides in a buffer or state machine that no request ever touches. Those went from 75% to 79%, essentially flat, while wire-level bugs climbed toward 90%. Traffic is the tiebreaker when the bug’s truth is in the request or response, and dead weight when it…

Close to free. The full second run, including the four-way test, was about 18,000 model calls for roughly $118, and 94% of the input tokens were cached because the codebase repeats across every run. Bolting a few captures onto each prompt adds little on top of that. 100 bugs in a private…

Every major platform vendor is racing to build what they’re calling the governed version of OpenClaw. The market is projected to grow from $7.84B to $52.62B by 2030 at a 46.3% CAGR. Gartner predicts 40% of enterprise apps will have embedded AI agents by end of 2026, up from less than 5% in 2025.…

Both support HTTP request matching, configured responses, and request verification. Compare their APIs and lifecycle integration against the expectations your tests need.

Yes. WireMock supports record and playback. Captured fixtures still need review and updates when the real dependency contract changes.

Use proxymock when you need recorded dependency responses or want to replay inbound traffic against a code change. Follow the setup requirements for each protocol and client.

Migrate incrementally: keep targeted WireMock tests and record one representative integration flow with proxymock. Compare the results before expanding coverage. A recording is not a one-to-one translation of WireMock matchers and verification assertions.

Yes. Follow the Java and protocol-specific setup instructions, including any client proxy or port-mapping configuration. Capture a representative flow and confirm its downstream calls appear in the recording before relying on the mocks.

Configure redaction rules, inspect tokens and personal data, and review recordings before sharing them. A traffic recording does not by itself establish privacy or compliance.

Testcontainers starts real dependencies in containers. API mocks supply controlled dependency responses. Use a real database container for database behavior and an HTTP mock for a payment timeout when those are the requirements you need to test.

Not very, on their own. The best agents fix about 50% of real bugs on SWE-bench verified, and in my own benchmark on a codebase the model had never seen, an agent working from a monitoring alert passed 51% of the tests. AI-generated code also tends to produce more issues than human-written code, so…

Yes, substantially, when the bug shows up on the wire. Handing the agent the captured request and response for the failing call raised the pass rate from 51% to 77% in my test, a 26 percentage point jump with non-overlapping confidence intervals.

With only an alert, the agent has to guess which of hundreds of services is broken and greps for vague keywords like latency or error rate that match half the codebase. In my 240-service test it edited the wrong service 34% of the time. Captured traffic names the specific endpoint and field, which…

When the real fix is below the HTTP boundary. On 11 bugs the captures gave the agent a confident theory that pointed at the symptom but the wrong layer, like an exception base class or a race condition in internal state that never appears in the trace. There it was faster, more decisive, and wrong.…

QABot uses structural analysis, not guesswork. It identifies JWTs by their three-part base64 structure and checks for exp/iat claims. It finds timestamps by matching ISO 8601, RFC 2822, and Unix timestamp patterns. It traces ID propagation by matching values that appear in a response and then…

The generated transforms are saved as YAML files in your repo. You can inspect and edit them. If QABot incorrectly identifies a static value as dynamic (or misses a dynamic one), you adjust the YAML and commit the change. Over time, QABot’s detection improves as it sees more recordings from your…

Yes. If you already have WireMock stubs for contract tests or specific fault injection scenarios, keep them. Use proxymock + QABot for integration and regression tests where you need realistic data across many dependencies. The two approaches complement each other. See our WireMock vs MockServer vs…

QABot is in beta as part of the Speedscale Cloud platform. The proxymock CLI is free for recording and replaying traffic locally. QABot’s analysis and recommendations live in the Speedscale dashboard. You push your local recordings with proxymock cloud push snapshot, review the recommendations in…

While older approaches to the internal network viewed these sources as relatively trustworthy, the rise of internal attacks, privilege escalation, and data breaches led many organizations to seek an alternative trust approach and model. The Zero Trust approach was the result - a view of systems…