Data Driven Techniques to Measure How Much Testing is Enough (2024)

Data is powerful. Businesses globally adopt data-driven techniques to make highly informed and mindful decisions. Creating a culture of relying on data adds immense value and lets us interpret data into real, actionable insights. But more than anything, data offers clarity. According to CIO Dive, data-driven organizations have at least a 58 percent of chance to meet their revenue goals in comparison to non-data-driven organizations. But we are here to talk about how you can leverage data to understand how much testing is enough for your application.

We’ve all heard of data driven testing and its benefits. But, some data-driven techniques can help us understand if we’ve carried out sufficient testing.

Based on that, we can discover if and when our products are ready for launch. This article will discuss those data-driven techniques and how we can achieve software reliability using them.

Table Of Contents

  • 1 How Much Testing is Enough?
    • 1.1 Ask the Right Question
    • 1.2 Define Your Project and Testing Requirements
    • 1.3 Do Not Trap Yourself In ‘Is it Enough’ Question
    • 1.4 Understand the Testing Scope
    • 1.5 Make the Right Decision
  • 2 Data Driven Techniques: Measure How Much Testing is Enough?
    • 2.1 When the build breakage is trivial:
    • 2.2 When all the involved parties sign off the stories:
    • 2.3 When the code freeze is effective:
    • 2.4 When all the blockers/bugs are addressed:
    • 2.5 When the test coverage is high:
    • 2.6 When the find-rate of critical bugs is low:
  • 3 Final Words
  • 4 Frequently Asked Questions (FAQs)
    • 4.1 How do you know when to stop testing?
    • 4.2 Is it possible to test 100% of the software?
  • 5 Suggested Readings

How Much Testing is Enough?

You know your application requires testing, but where do you stop once you start the process? Do you continue performing the different types of testing as and when you feel the need? Or do you document your testing requirements first and begin your application testing journey?

We have the answer for you in the below points. These will help you understand how much testing is enough for your project:

Ask the Right Question

As a tester, you must come up with the right queries to understand the project scope and its testing requirements, if it would have API calls, UI needs, integration of modules, payment gateway, cart functionality, or more. Upon understanding what the application will actually have, you can ask your testing team to proceed with the particular test cases and scenarios.

Define Your Project and Testing Requirements

Similar to the above point, defining your project and testing requirements beforehand is a sure-shot way of knowing how much testing you would need to perform. As you start to document test cases and keep adding to them, you will understand how much test coverage the project precisely needs.

Do Not Trap Yourself In ‘Is it Enough’ Question

Often, testers doubt themselves and keep asking if the testing is enough or do they continue to keep doing it. But breaking out of this trap shouldn’t be a huge concern once you have properly defined your testing requirements. Simply run all the tests and perform final Regression testing before calling it a day.

Understand the Testing Scope

It is almost similar to documenting and sticking to the test coverage when new features or updates make an entry. Once you are clear about the scope of testing, you can re-run the same test cases to execute regression and also when upgrades are rolled out.

Make the Right Decision

What does ‘enough’ mean to you? It is subjective, and that is why finding the perfect balance between too little and too much is critical. All of this starts with making the right decision and ensuring that your testing efforts match the testing requirements that do not waste resources, time, and money in abundance.

Data Driven Techniques: Measure How Much Testing is Enough?

When the build breakage is trivial:

Most agile teams and continuous integrations strive to ensure build breakages don’t happen. When a developer pushes new code to the existing source code repository, the software may not perform as intended.

For instance, if your code had no errors in the past 10 days and is now running into errors with every code push, it means the code isn’t tested enough.

In a nutshell, we can assume the build is broken if the build process doesn’t move further, smoothly, for reasons such as bugs, compilation errors, or that a developer is pushing code without testing it enough.

A solid way to avoid build breakage is by ensuring the build is accurate before checking in. In case of any errors, it’s recommended to fix the errors locally before integrating the changes with the source code.

Data Driven Techniques to Measure How Much Testing is Enough (1)

As per Parabuild CI, build breakage can be avoided if we avoid code check-ins beyond 5 pm. Parabuild calls it “Five O’clock Check-In” Pattern and its studies suggest that a developer’s critical and analytical skills often dip after 5 pm or towards the end of the day.

Further, this research also proves that if we avoid code pushes after 5 pm, it brings down build breakage by 20% to 50%.

However, if the software isn’t encountering any errors, despite small code changes, it means the build breakage is insignificant, and that the software has been thoroughly tested.

When all the involved parties sign off the stories:

Agile teams work as per their pre-decided schedules and plans. Their core objectives include transparency and involving all the respective stakeholders before a project takes off.

For that reason, agile teams chalk out epics and stories as they offer a broad understanding of the requirements in both short-term and long-term scenarios.

Stories are written from the user’s perspective and they give a clear picture of the requirements while epics are a collection of these stories. When the stories are signed off by all the stakeholders, for instance, the QA team, developers, product managers, and any other involved teams, it means enough testing has been carried out.

When the code freeze is effective:

Code freeze literally means we cannot modify or edit the code when the code is frozen. This is done to effectively eliminate the chances of unintentionally introducing any bugs before the software goes live.

There are cases when developers push code even after the code is frozen. Even a few insignificant, last-minute code changes before the release can lead to a build breakage.

The developers might have tested out those features on their machine, but we don’t know how these changes reflect in the software when the new code is integrated.

This means the code freeze is ineffective and the entire code has to be tested again. Particularly to detect faulty behaviour and to understand which elements are causing breakage when integrated.

Things to consider for an effective code freeze

  • We should confirm there are no new bugs before going ahead with the code freeze. New modifications and bugs can hinder the smooth functioning of our software, and we should address any issues and fix even the smallest vulnerabilities before a code freeze.
  • We should perform a stringent security test that will help us discover any insecure elements or areas of the software.
  • If we’ve faced any bugs in the previous stages, it’s best to check for similar patterns or bugs. Move forward with the code freeze only after validating the features, functionalities, and quality of the software.

These key things can lead to an effective code freeze, thereby ensuring stable software.

When all the blockers/bugs are addressed:

To move forward with the project, it’s crucial to close all the blockers bugs. Blockers can come in various forms that include technical issues, backlogs, environmental errors, rapid changes in priorities or stories, too many external dependencies, hidden complexities, or complex tools.

Teams can address blockers by keeping a track of them using project management frameworks like Kanban, Wrike, etc.

Data Driven Techniques to Measure How Much Testing is Enough (2)

When the test coverage is high:

Test coverage is a metric that determines how much testing is done on the software, which is under testing. This metric helps us gather important information such as the total number of tests that have passed, failed, and executed, the number of test cases, and if the software is thoroughly tested.

Maximum test coverage can be achieved with the following:

  • Automation testing tools
  • By performing thorough unit tests
  • Code reviews

A good rule of thumb is if your test coverage is high, it means the software under test went through maximum testing. But, this metric should not be used independently or it can create more confusion than it is useful. To know more, read here: Are Test Coverage Metrics Overrated?

When the find-rate of critical bugs is low:

Often we encounter errors during production, regression, or acceptance testing. In an ideal scenario, we should find and fix any critical bugs or defections before our software goes into production.

The find rate of the bugs can be tracked based on the number of bugs found during the pre-production testing phase against production. This will help us measure our bug find rate, and if the rate is low or reducing, it means enough testing has been performed.

Final Words

Automation testing ecosystems can offer the right amount of test coverage and reduce build breakage, provided we choose the right tool. Testsigma increases test coverage through its data-driven testing system.

Powered by AI, Testsigma’s automation testing tool makes it very easy to create test cases and improve your test coverage. It provides an in-depth summary of test results, which helps in tackling the bugs better.

This codeless testing tool is fully cloud-based. Besides, whenever any code changes are made, AI suggests which affected areas should be prioritized for testing. While this helps you identify the affected tests easily, you can also avoid failures that are similar to these tests.

Our tool sends out in-depth reports via email, Slack, and other communication platforms, for you to collaborate and fix any bugs with your team. Since it’s cloud-based, remote teams can access it from anywhere, anytime. Read about the simplicity of data-driven testing with Testsigma here.

Opt for quick and efficient data-driven testing with Testsigma

Frequently Asked Questions (FAQs)

How do you know when to stop testing?

There is no quantifiable way to know that you have completed all your testing requirements. But if the application contains very few errors, regression is over, user acceptance testing is complete, and no new features are being added, you can stop your testing efforts till anything new comes up.

Is it possible to test 100% of the software?

Practically, it is impossible to test any application completely. You might be able to reach a very near point where the software has no identifiable issues, and the users are also content with its working. But otherwise, because the area of possible inputs is too huge to completely use in testing a system.

Suggested Readings

Automated Data-Driven Testing Tool for Agile teams

All you need to know about Data-Driven Testing

Top Benefits of Data-Driven Test Automation

Scenarios When Data-Driven Testing Is Useful

Data Driven Techniques to Measure How Much Testing is Enough (2024)

FAQs

How can you determine how much testing is enough? ›

There are no predefined rules that you can use to find how much testing is enough. Visualize project situations, project readiness, and timelines to decide on your own. In practice, the goal is to strike a balance between thorough testing and project constraints.

How do you determine when you have done enough testing? ›

You should analyze your test data, such as test cases, test execution, test coverage, defect reports, and defect resolution, to assess the quality and completeness of your testing process. You should also compare your test results with your testing goals and exit criteria, to verify if you have achieved them or not.

What is the Data-Driven Testing technique? ›

What is Data-Driven Testing? Data-driven testing (DDT) is data that is external to your functional tests, and is loaded and used to extend your automated test cases. You can take the same test case and run it with as many different inputs as you like, thus getting better coverage from a single test.

How do you measure effectiveness of testing? ›

Test Effectiveness measures the effect of the testing on the software application. It is usually measured by dividing the number of test cases executed by the unit of time (per hour). It is calculated by dividing the total number of defects found in the software by the total number of test cases executed.

What is the best way to measure test coverage? ›

To measure the test coverage for unit testing, you can use code coverage tools that analyze the source code and report the percentage of statements, branches, functions, or lines that are executed by the test cases.

How do you determine which piece of software required how much testing? ›

One way to decide how much testing you need is to define your quality goals for your software product. Quality goals are measurable objectives that specify the desired level of performance, reliability, usability, security, and other attributes of your software.

How do you know when to stop testing and how much testing is enough? ›

Testing can be Stopped When:

The entire testing budget has been depleted. All testing-related documents and deliverables have been created, reviewed, and shared with the appropriate stakeholders. All the high-priority bugs must be resolved and the bug rate level comes at a low level.

How much testing is enough answer? ›

It's impossible to answer this question, as there is no such thing as exhaustive or fully comprehensive testing. There is always going to be something missed by a team, no matter how large or well trained, and no matter which type of testing is being performed.

What are data driven techniques? ›

What Does “Data-Driven” Mean, Exactly? In a data-driven approach, decisions are made based on data instead of intuition. Following a data-driven approach offers measurable advantages. That's because a data-driven strategy uses facts and hard information rather than gut instinct.

What is the TDD methodology of testing? ›

Test Driven Development (TDD) is a software development approach where tests are written before the actual code. It offers several advantages: Comprehensive Test Coverage: TDD ensures that all new code is covered by at least one test, leading to more robust software.

What are KPIs in testing? ›

KPIs are the detailed specifications that are measured and analyzed by the software testing team to ensure the compliance of the process with the objectives of the business. Moreover, they help the team take any necessary steps, in case the performance of the product does not meet the defined objectives.

How do you measure testing success? ›

Metrics, such as the percentage of automated tests and test duration, provide insight into the impact of automation on the testing process. Additional metrics, such as flakey tests, pass rates, coverage, and issues detected, are critical to measuring success and reliability.

How much test coverage is enough? ›

With that being said it is generally accepted that 80% coverage is a good goal to aim for. Trying to reach a higher coverage might turn out to be costly, while not necessary producing enough benefit.

How to measure test coverage in Agile? ›

If you have to perform cross browser testing to ensure that your web application renders well from different browsers, you would be covering many browsers and operating systems. To calculate test coverage, you can divide the number of lines covered by a test by the total number of lines in your application's test code.

How do you decide the testing is enough? ›

The find rate of the bugs can be tracked based on the number of bugs found during the pre-production testing phase against production. This will help us measure our bug find rate, and if the rate is low or reducing, it means enough testing has been performed.

How do you know you have enough requirements for testing? ›

The mnemonic I use for what to look for is the four C's and one T: completeness, clearness, correctness, consistency, and testability. To make testing requirements more effective, you can use the approach called heuristics testing, or testing with a strategy that relies on past data about probabilities.

How do you decide if you have tested enough? ›

An analysis of the requirements helps determine the set of test conditions by identifying transition points in behavior (or edge conditions). The regions of operation at and between these boundaries are used to determine the Test Cases that will ensure the design has been satisfied.

How do you know you have enough test coverage? ›

Test coverage can be measured by using code coverage tools that track which parts of the code are executed during testing. These tools generate reports showing the percentage of code covered, helping identify areas that require additional testing and improving overall testing effectiveness.

How would you know that enough testing has been performed? ›

The find rate of the bugs can be tracked based on the number of bugs found during the pre-production testing phase against production. This will help us measure our bug find rate, and if the rate is low or reducing, it means enough testing has been performed.

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