Measuring Test Runtime: Optimizing Test Suite Performance

Software development is getting progressively more complex as time passes, making software testing equally essential to releasing a stable and reliable product. But we know bloated test suites are infamous bottlenecks to faster releases. Dev teams need to be able to speed up their tests to keep progress moving, but it isn’t that simple.

Measuring the runtime of your test suites can significantly impact your development cycle, enabling fast feedback loops, effective resource utilization, and better maintainability for your tests. With all that in mind, you can see how important measuring test runtime can be.

Traditional Approaches for Measuring Test Runtime

There are several ways of measuring test runtime, enough to suit almost anyone’s needs. That doesn’t always mean they’re the best approach, however. Let’s talk about some of the most common methods you may have heard of:

• Manual timing - The most basic way possible, it’s not entirely unheard of for timing these tests with traditional clocks and stopwatches. All you need is a way to track the passage of time (like the stopwatch function on your phone) and some good timing. However, it’s nowhere near the most accurate, as it still relies on human interaction.

• Integrations with CI tools - Plenty of CI tools include features for measuring test runtime. Tools like TravisCI and Jenkins can record the start and end time of the jobs provided, giving you a glimpse of how they perform and if it changes.

• Analyzing logs and using timestamps - Another more “manual” way is to simply set up logs in your tests as they run. No matter what language you use, there will always be a print() function equivalent somewhere. You can also set your tests to log these results for easier parsing, but either choice will make writing tests longer.

Analyzing Logs Example in Python

When your test framework generates logs, you can parse them to extract the necessary information such as test start time and test completion time.

import re
def parse_logs(log_file):
    with open(log_file, 'r') as f:
        logs = f.readlines()
    test_start_time = None
    test_end_time = None
    for line in logs:
        if 'Test started' in line:
            test_start_time = extract_timestamp(line)
        elif 'Test completed' in line:
            test_end_time = extract_timestamp(line)
    if test_start_time and test_end_time:
        test_runtime = test_end_time - test_start_time
        print(f"Test runtime: {test_runtime}")
def extract_timestamp(log_line):
    # Regular expression to extract timestamp (assuming format: [YYYY-MM-DD HH:MM:SS])
    timestamp_pattern = r'\[(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2})\]'
    match = re.search(timestamp_pattern, log_line)
    if match:
        timestamp = match.group(1)
        return convert_to_datetime(timestamp)
    return None
def convert_to_datetime(timestamp):
    from datetime import datetime
    datetime_format = "%Y-%m-%d %H:%M:%S"
    return datetime.strptime(timestamp, datetime_format)
# Usage
parse_logs('test_logs.txt')

In this example, the parse_logs function reads the log file line by line and searches for specific markers ('Test started' and 'Test completed') to extract the corresponding timestamps. It uses regular expressions to extract the timestamp and converts it to a datetime object for further calculations.

Timestamps Example with Python

Another approach is to record timestamps directly in your test code. In the below example, the run_test function records the start time using time.time() before executing the test logic. After the test completes, it calculates the runtime by subtracting the start time from the end time, which is also obtained using time.time(). The result is printed as the test runtime in seconds.

import time
def run_test():
    start_time = time.time()
    # Test logic goes here
    # ...
    end_time = time.time()
    test_runtime = end_time - start_time
    print(f"Test runtime: {test_runtime} seconds")
# Usage
run_test()

Regardless of your approach, these methods tend to have some pitfalls. It’s difficult to get wholly accurate results from these methods, whether from the chance of human error or external factors. Plus, many of these can’t scale up with your testing. That means you need to find a more accurate way to measure.

Why Should I Be Measuring Test Runtime Anyway?

We’ve gone on and on about the how, but what about the why? Your tests are critical, but the testing pipeline can be a delivery bottleneck. By tracking how your tests run, you can see the full picture of their performance, string together test suite intelligence, and take steps to streamline your testing further, including three common areas:

Parallelization Optimization

Measuring test suite runtime allows you to spot what tests have the lengthiest runtime. This transparency enables you to optimize testing further, when possible, by parallel testing which gives you faster results and a shorter feedback loop.

Resource Allocation 

Accurately measuring your test runtime can help you plan your testing phases more efficiently. You’ll be able to allocate time and compute power to your lengthier tests and avoid bottlenecks later down the pipeline.

Test Prioritization 

Identifying your slowest tests also allows you to take a step back and prioritize the most critical tests and what can be saved for later. That way, you can have your faster tests run first, giving you crucial insights into the build before your lengthier tests get to work.

Continuous Improvement 

Test runtimes are critical in CI/CD pipelines, where fast feedback is essential. By measuring and optimizing your test runtimes, you can reduce the time required for the CI/CD process, enabling faster deployment. This, in turn, improves the overall agility of the development process and allows for more frequent iterations.

Automate Tracking Test Runtime and Critical KPIs with  Launchable

It’s pretty clear that measuring test runtime can be a huge factor in your overall testing process. And with Launchable, you can start measuring immediately and make your tests more efficient.

How does Launchable Measure Test Runtime

Launchable automates test runtime tracking by integrating with all your favorite CI/CD tools, including TravisCI, Jenkins, and GitHub Actions. That means you can easily slide Launchable into your existing pipeline, allowing our ML model to analyze your tests. And once we’re in, we can seamlessly measure test runtime across multiple builds, giving you critical insights into your tests beyond just runtime.

Deeper Testing Insights Beyond Runtime for Complete Test Suite Health

Empower your team to quantify the impact of changes in your test suites beyond test runtime. Launchable gives test suite health metrics for deeper test suite transparency for data-driven QA.

• Prioritize impactful flaky tests: Flaky tests can be a huge headache for QA teams, sucking up time and effort. Launchable identifies flaky tests based on their impact, allowing you to address and run them more reliably. Get insights from our daily Flakiness report and fix your tests with confidence.

• Track test session duration: Measure the time it takes for your test suite to run across multiple sessions. Our tracking feature highlights tests that exceed expected durations, helping you identify and resolve any performance issues.

• Optimize test session frequency: Monitor how often your test suites are executed and combine it with session duration. Ensure tests are run at the right intervals. Plus, Predictive Test Selection saves time and resources by running the most relevant tests at the optimal times.

• Identify test session failure ratio: Pinpoint tests that fail frequently and investigate potential issues with the tests or the current build. Gain valuable insights into the stability of your testing process and make informed decisions to improve overall quality.

Utilizing your test infrastructure efficiently is key to minimizing idle time and maximizing your resources. Easily identify any bottlenecks hindering performance by measuring test runtime with Launchable. Get all the information you need to test suite patterns and trends within your tests, allowing you to make informed, data-driven decisions to optimize your tests. And by doing so, you can streamline your overall testing process.