Early on in my career, we released to production a feature-rich product that we had tested unit tests as being "green" and we would do load tests later. After a marketing push, we doubled our traffic, the CPU hit maximum usage, latency went from 200ms to 1.8 seconds, and users started to leave us for other options. Ultimately, the postmortem concluded that we had no baseline or benchmark on the application. The failure was not the bug we fixed but rather the process of not measuring against a known standard.
Without measuring performance against a gauge of some sort, you aren't an engineer; you're guessing. This is why applications like Keploy exist, because guessing does not scale beyond a certain point.
What is Benchmark Software Testing?
Benchmark software testing is not simply "run JMeter and see what happens".
Benchmark software testing is controlled performance evaluation and quantification based upon:
Speed-response time, latency, throughput
Stability-error rates while under load
Resource Usage-CPU, RAM, I/O, Network
Scalability-the performance of your application as load continues to grow
Benchmarks measure against something real: either prior release data, defined SLA (e.g., 95% of requests completed in under 300 ms), performance data about a competitor, or the internal gold standard for service. Benchmarks exist to help answer one very simple question:
Did the recent change improve or degrade the system?
If the answer takes longer than 5 minutes to determine, then you don't have benchmark data; you have log files.
A lot of teams get benchmark testing wrong; I have observed smart teams making the same mistakes repeatedly. Some common errors: only running benchmarks once and not repeating them again, generating synthetic traffic that is dissimilar to real production traffic, ignoring cold-start and cache-miss conditions, measuring averages instead of percentiles, and treating benchmarking as just a QA issue.
Because performance is a system characteristic made up of a variety of contributing elements: code, infrastructure, network, configurations, and the types of data that are used—all need to be taken into consideration when establishing benchmark data. Failure to take real-world usage patterns into account will result in benchmark data that you cannot trust; they will lie to you; they may be polite but they will lie.
Benchmark testing does not replace other types of testing; it complements all other tests. The following depicts the way I have commonly seen this run through various types of development: local development—perform sanitation benchmarks on critical path; CI—validate new builds by using the last stable build as a benchmark; pre-production—execute the full suite of benchmarks using realistic traffic conditions; and post-release—confirm that there have not been any undiscovered regressions.
The comparison is critical. You cannot look at absolute numbers until you have a frame of reference (or a baseline). 200 ms is very fast… until yesterday, it was 90 ms.
Most benchmark processes fail here. We create fake payloads, mock dependencies, and simplify edge cases. When production load hits, we get:
unusual headers , unexpected payload sizes , burst patterns, real-world user behavior (the worst of all)
Your benchmark has passed. Your production environment is on fire.
This is why modern engineering teams have begun using traffic-based benchmarks instead of maintaining a library of handcrafted test cases. Keploy fits naturally into this process by collecting a record of actual traffic from either production or staging and converting that into automated test cases. No guesswork. Real user behavior. This significantly improves the quality of software benchmarking.
Why Real Traffic Changes Everything
You can run your benchmark against real requests instead of relying on an estimate:
- edge cases will occur
- payload distributions will be accurate
- dependencies behave as expected
- latency patterns match the real scenario
You’re not trying to argue what to test; you’re testing what already occurred.
I’ve used this methodology to catch regressions that traditional synthetic tests would have missed — giant JSON blobs, N+1 queries under specific user sequences, memory leaks that only occur with certain sequences.
Benchmarks no longer are a matter of theory; they are a matter of continued prediction.
How to Approach Measurement (And What to Leave Out)
Don’t measure everything. Instead, focus only on the signals that provide useful insight into performance.
Measurements to Include:
- Latency of p50, p95, and p99
- Error rates under sustained load
- CPU and memory growth over time
- Throughput per instance
- Time taken to recover from spikes
Measurements to Exclude:
- Single runs
- Perfect lab conditions
- Metrics that lack a baseline, such as 'vanity metrics'
- Averages without percentiles
If your p99 is bad, users feel it even if your average is okay.
A Pro Tip:
If you want to have an accurate benchmark, lock your environment first. Use identical instance types, configuration, and data volume. If you don't do this, you'll be comparing your code, not the AWS noise.
Benchmarking is a Cultural Process
This section is deliberately very opinion-based.
If you wait until after an incident occurs to do a performance test, then your culture is already doing it wrong. Performance is a quality attribute just like correctness, and non-negotiable performance issues should block your merge, instead of creating a response system for incident calls.
The behavioural characteristics of high-performing teams that are experienced in scaling are:
- Automating their benchmarks
- Testing benchmarks after any meaningful change
- Tracking trends rather than single points in time
- Treating all performance regressions as bugs
This activity is not going to increase your workload; it will reduce your time spent on reactive measures towards problem resolution.
You Have a Challenge Now
Choose one of your most critical APIs. Just one.
Create a baseline, collect traffic data over time, benchmark before and after your next application change.
Your workflow should be repaired—not your servers—if you can’t confidently determine if that end-user received a faster or slower response from their application.
The primary goal of Benchmarks and Software Testing is not to have high numbers, but rather to have confidence in knowing the facts and not experiencing uncertainty.