When it comes to testing new features or changes in software development, there are several methods available. Two popular options for testing are Canary and A/B testing. These techniques allow developers to assess the impact of updates on a smaller scale before implementing them live for all users.
The Canary approach involves rolling out changes to a small percentage of users, typically a subset of the total user base. These users act as the testing group, or “canary in the coal mine,” providing valuable feedback and data on the new features or updates. The main advantage of Canary testing is the ability to identify and address any potential issues or bugs before rolling out the changes to all users. This method minimizes the risk of negatively impacting a large user base with untested or faulty updates.
On the other hand, A/B testing involves creating two or more versions of a feature or design element and randomly assigning users to different groups, each experiencing a different version. This testing method allows developers to compare the performance and user satisfaction of different variations to determine which one is optimal. A/B testing is useful for understanding user preferences and maximizing the effectiveness of features or design choices.
Choosing between Canary and A/B testing depends on your specific testing needs. If you want to evaluate the impact of specific changes or updates on a smaller scale, Canary testing is the better option. It allows for more targeted testing and provides valuable insights before rolling out changes to your entire user base.
Alternatively, if you’re interested in comparing two or more variations of a feature or design element to see which one performs better, A/B testing is the way to go. This method allows for direct comparisons and can help optimize your software or application based on user feedback and preferences.
Ultimately, both Canary and A/B testing are valuable tools in the software development process. By choosing the right method for your specific testing needs, you can ensure the successful implementation of new features and updates while minimizing risks and maximizing user satisfaction.
What is Canary Testing?
Canary testing is a type of software testing that involves releasing a new version of an application or feature to a small group of users or servers before deploying it to the entire user base. It allows developers to gather feedback and discover any potential issues or bugs that may arise before rolling out the update to a larger audience.
The term “canary” is derived from the practice of using canaries in coal mines to detect poisonous gases. Similarly, in software development, a canary release acts as an early warning system, identifying any problems that may occur when a new version of an application is deployed.
Canary testing is especially useful for complex or critical applications where a small bug can have a significant impact. By releasing the update to a select group of users, developers can monitor how the new version performs and quickly address any issues that arise.
This type of testing is often used in conjunction with A/B testing. A/B testing involves comparing two versions of an application or feature to determine which one performs better. Canary testing allows developers to gather data from a small group of users to determine the performance of one version compared to another.
In conclusion, canary testing is a valuable testing technique that allows developers to gather feedback and identify any issues or bugs in a new version of an application before deploying it to a larger audience. It acts as an early warning system, helping developers ensure a smooth and successful rollout of their updates.
What is A/B Testing?
A/B testing is a method of comparing two versions of a webpage or other marketing element to determine which one performs better. It is also known as split testing or bucket testing. In A/B testing, two versions, A and B, are created, with only one element being different between the two versions. This can be a headline, a call-to-action button, or even the color scheme of the webpage. The goal is to understand which version leads to higher conversion rates, click-through rates, or any other metric that is important for the success of the webpage or campaign.
A/B testing allows marketers and designers to make data-driven decisions and optimize their websites or marketing materials based on real user behavior. By testing different versions of a webpage, they can identify the elements that have the biggest impact on user engagement and conversion. Over time, A/B testing can help improve the effectiveness of a website or marketing campaign, leading to higher conversion rates and better overall performance.
A/B testing is different from canary testing, as canary testing involves releasing a new version of a software or feature to a small group of users, called the “canary group,” before rolling it out to the larger user base. The purpose of canary testing is to detect any issues or bugs in the new feature before it is widely released. A/B testing, on the other hand, focuses on comparing the performance of two different versions of a webpage or marketing element and choosing the one that performs better.
Pros and Cons of Canary Testing
When it comes to testing new features or changes in software, there are various strategies to choose from. Two popular options are canary testing and A/B testing. In this article, we will focus on the pros and cons of canary testing and how it stacks up against A/B testing.
Pros of Canary Testing:
- Better risk management: Canary testing allows for gradual rollout of new features to a subset of users, reducing the risk of widespread issues.
- Early detection of bugs: By releasing the feature to a small group of users first, canary testing helps to uncover any bugs or issues that may not have been caught during development and testing phases.
- Real-world performance assessment: Canary testing provides an opportunity to evaluate how the feature performs in a real-world environment, which can uncover performance-related issues that may not have been predicted during development.
- Targeted feedback collection: By selecting a specific group of users for the canary release, it becomes easier to collect targeted feedback and make necessary improvements before a wider release.
Cons of Canary Testing:
- Requires infrastructure support: Setting up and managing the infrastructure required for canary testing can be time-consuming and may require additional resources.
- May delay feature release: Since canary testing involves a gradual rollout, it may result in a delay in the release of the feature to all users.
- Requires careful monitoring: Canary testing requires continuous monitoring to ensure that any issues or bugs are detected and addressed promptly.
- Limited sample size: The canary group represents only a subset of users, which may not always provide a comprehensive understanding of how the feature will be received by the entire user base.
It is important to carefully consider the pros and cons of canary testing before deciding if it is the right approach for your testing needs. By weighing the benefits against the challenges, you can make an informed decision that aligns with your goals and resources.
Pros and Cons of A/B Testing
A/B testing is a popular method used in digital marketing to compare two versions of a webpage or element to determine which one performs better. While A/B testing has its benefits, it also has some drawbacks that should be considered. Here are the pros and cons of A/B testing:
Pros:
1. Data-driven decision making: A/B testing provides concrete data on user behavior and preferences, allowing businesses to make data-driven decisions and optimize their websites or campaigns accordingly.
2. Improved user experience: By testing different versions of a webpage, businesses can identify the design, layout, and content elements that resonate best with their audience, resulting in an improved user experience.
3. Increased conversion rates: A/B testing allows businesses to test different strategies and elements that can lead to increased conversion rates, such as call-to-action buttons, headlines, or pricing options.
4. Cost-effective: A/B testing can be a cost-effective way to optimize marketing efforts, as it allows businesses to focus on the changes that are most likely to generate positive results, rather than making sweeping changes to their websites or campaigns.
Cons:
1. Time-consuming: A/B testing requires time and resources to set up and analyze the results. It can take several iterations and significant time investment to gather enough data to make informed decisions.
2. Limited scope: A/B testing is often used to test isolated elements or small changes on a webpage. It may not be suitable for testing complex or interconnected changes that require a holistic approach.
3. Statistical significance: A/B testing requires a sufficient sample size and statistical significance to draw accurate conclusions. Small sample sizes or inconclusive results can lead to unreliable findings.
4. Potential biases: A/B testing relies on user behavior and preferences, which can be influenced by various factors. Biases and external factors may impact the results, making them less reliable.
| Pros | Cons |
|---|---|
| Data-driven decision making | Time-consuming |
| Improved user experience | Limited scope |
| Increased conversion rates | Statistical significance |
| Cost-effective | Potential biases |
When to Use Canary Testing
Canary testing is a valuable strategy to use when you want to test a new feature or code change in a real-world production environment, but in a controlled and limited way. By releasing the feature or code change to a small subset of users, known as the “canary group,” you can gather valuable feedback and data about its performance and impact.
This allows you to identify and address any issues, bugs, or performance problems before rolling out the feature or code change to a larger audience. Canary testing not only helps mitigate risks but also enables you to make data-driven decisions by analyzing the feedback and metrics from the canary group.
Besides, canary testing is an effective method for testing the compatibility of new features or code changes with other systems or dependencies. By gradually introducing the feature to the canary group, you can closely monitor how it interacts with other components and services.
In summary, use canary testing when:
- You want to test a new feature or code change in a real-world production environment
- You want to mitigate risks by gathering feedback and data in a controlled and limited way
- You want to ensure compatibility with other systems or dependencies
When to Use A/B Testing
A/B testing is a powerful tool that allows you to compare two or more variations of a webpage or app to determine which one performs better in terms of user engagement, conversions, and other metrics. It is particularly useful in situations where you want to make data-driven decisions about design changes or marketing strategies.
If you are unsure about which version of a webpage or app will be more effective, A/B testing can help you make an informed choice. By splitting your audience into different groups and exposing them to different variations, you can collect data on user behavior and preferences. This data can then be analyzed to identify which version performs better and ultimately decide which direction to take.
Furthermore, A/B testing is valuable in situations where you want to optimize your current design or marketing strategy. By testing different variations, you can identify specific elements that have a significant impact on user engagement and conversions. This allows you to make targeted improvements and refine your approach for better results.
It’s important to note that A/B testing is not suitable for every scenario. If you are making significant changes to your website or app, or if you are testing a completely new concept, other methodologies like canary testing may be more appropriate. Additionally, A/B testing requires a sufficient sample size and a clear understanding of the metrics you are tracking to generate meaningful results.
In summary, A/B testing is a valuable technique that can help you make data-driven decisions and optimize your website or app. It is particularly useful when you are unsure about which variation will perform better or when you want to improve your current design or marketing strategy. However, it’s important to consider the specific requirements of your project and choose the testing method that aligns with your goals and resources.
Key Differences between Canary and A/B Testing
Canary testing and A/B testing are both widely used methods for testing and improving the performance of software applications. While they share some similarities, there are key differences between the two approaches.
Definition:
Canary testing is a technique where a small group of users or devices are exposed to a new version of an application, while the majority of users continue to use the current version. This allows for the identification of any issues or bugs before rolling out the new version to all users.
A/B testing, on the other hand, involves dividing users into two or more groups and exposing each group to a different version of the application. The performance of each version is then measured and analyzed to determine which version is more effective or successful.
Usage:
Canary testing is often utilized when introducing new features or making significant changes to an application. By gradually rolling out the changes to a small group of users, any potential issues can be identified and addressed before impacting a larger user base.
A/B testing, on the other hand, is commonly used for refining and optimizing existing features or design elements. By testing different variations of an application’s user interface or functionality, developers can gather data to make informed decisions about which version performs better.
Implementation:
Canary testing typically requires setting up infrastructure to redirect a portion of users or devices to the new version of the application. This can involve the use of load balancers, routing rules, or feature flags to control access to the different versions.
A/B testing often involves using specialized tools or platforms that allow for easy creation and management of different versions or variations of an application. These tools typically include features for tracking user behavior, analyzing data, and conducting statistical tests to determine the significance of any observed differences.
In conclusion, canary testing and A/B testing have different purposes and approaches, but both can be valuable for improving software applications. Canary testing is ideal for introducing new changes gradually and identifying potential issues, while A/B testing is well-suited for refining and optimizing existing features. Knowing the differences between these two methods can help you choose the right approach for your testing needs.
Similarities between Canary and A/B Testing
When it comes to testing methodologies, Canary and A/B testing share some similarities in their approach and purpose. Both methods aim to validate changes before fully implementing them and help businesses make data-driven decisions. Let’s explore these similarities in more detail.
Data-Driven
Both Canary and A/B testing rely on collecting and analyzing data to assess the impact of changes. By comparing the performance metrics of different versions, businesses gain insights into user behavior and preferences.
Testing Variations
In both Canary and A/B testing, variations or versions of a feature or webpage are tested against each other. By creating different versions and exposing them to a subset of users, businesses can evaluate their effectiveness and make informed decisions based on real user feedback.
Incremental Rollouts
Both Canary and A/B testing allow for gradual and controlled rollouts. By exposing changes to a small percentage of users, businesses can mitigate risks and monitor the impact, ensuring a smooth transition or rollback if necessary.
Objective Evaluation
In both testing methodologies, the evaluation of effectiveness is based on predefined metrics and goals. Businesses can set specific key performance indicators (KPIs) and measure the success or failure of different versions based on these objectives.
Iterative Improvement
Both Canary and A/B testing encourage an iterative approach to development and improvement. By analyzing the results and feedback from the tests, businesses can refine their variations and continuously optimize for better performance.
In conclusion, while there are differences between Canary and A/B testing, they also share similarities in their data-driven, incremental, and iterative approach to validating changes. Understanding these similarities can help businesses choose the right testing methodology for their specific needs and goals.
How to Set Up Canary Testing
Setting up canary testing is a straightforward process that involves the following steps:
- Create a separate environment or infrastructure to deploy the canary code.
- Identify a small group of users who will be part of the canary group.
- Deploy the canary code to the designated canary environment or infrastructure.
- Monitor the behavior and performance of the canary group.
- Collect and analyze the data to determine if the canary code is performing as expected.
- If the canary code meets the desired criteria, gradually roll it out to a larger user base.
- If any issues or performance degradation is observed, roll back the canary code and investigate the root cause.
By going through these steps, you can effectively set up canary testing and ensure that your code changes are thoroughly tested before rolling them out to a wider audience.
How to Set Up A/B Testing
Setting up A/B testing is a critical step in improving the performance and effectiveness of your website or application. A/B testing allows you to compare two versions of a webpage or feature, A and B, in order to determine which one performs better. This can help you make data-driven decisions and optimize your content or design.
Here is a step-by-step guide on how to set up A/B testing:
| Step 1: | Identify the goal and hypothesis – Determine what specific metric or goal you want to improve through A/B testing. Establish a hypothesis, such as “Changing the color of the call-to-action button will increase click-through rates.” |
| Step 2: | Create two versions – Develop the original version (A) and the variant version (B) of the webpage or feature you want to test. Ensure that only one element is different between the two versions. |
| Step 3: | Define the sample size – Determine the number of visitors or users you want to include in the A/B test. This will help generate statistically significant results. |
| Step 4: | Split the traffic – Use a randomization process to evenly divide the incoming traffic between the two versions. This can be achieved by implementing a traffic-splitting algorithm. |
| Step 5: | Measure and analyze results – Track the performance of both versions by measuring specific metrics, such as click-through rates, conversion rates, or engagement. Use statistical analysis to determine if there is a significant difference between the two versions. |
| Step 6: | Implement the winning version – If one version outperforms the other, implement the winning version as the new default. If neither version performs significantly better, iterate and test new variations. |
By following these steps, you can effectively set up and execute A/B testing to improve the performance and user experience of your website or application. It is important to remember that A/B testing should be an ongoing process, as continuous optimization is key to staying ahead in the ever-evolving digital landscape.
Best Practices for Canary Testing
When it comes to comparing A/B testing vs Canary testing, there are several best practices to keep in mind for effective and successful canary testing. Below are some key recommendations:
| Test a Small Percentage: | Start by exposing only a small percentage of your user base to the canary version of your application. This allows you to minimize the potential impact of any issues or bugs that may arise during testing. |
| Gradually Increase Exposure: | Once you are confident in the stability and performance of the canary version, gradually increase the exposure to a larger percentage of users. This helps to ensure that any issues that may have been missed during initial testing are caught before a full release. |
| Monitor and Collect Data: | Implement robust monitoring and data collection mechanisms to track the performance and user experience of the canary version. This data can be used to identify any issues or improvements that need to be addressed before a wider release is undertaken. |
| Have a Rollback Plan: | Always have a rollback plan in place in case any major issues arise during canary testing. This allows you to quickly revert back to the stable version of the application to minimize any negative impact on users. |
| Communicate with Users: | Inform your users about the canary testing process and its purpose. This helps to manage expectations and ensure that users are aware that they may encounter some changes or issues during testing. |
By following these best practices, you can effectively leverage canary testing to mitigate risks and ensure a smooth transition to a new version of your software.
Best Practices for A/B Testing
When it comes to testing different variations of a webpage or app, A/B testing is a commonly used method. This approach involves creating two or more versions of a page and distributing the traffic among them to determine which one performs better. Here are some best practices for A/B testing:
Define Clear Goals and Metrics
Before starting an A/B test, it’s important to define clear goals and metrics. What are you trying to achieve with the test? Are you aiming to increase conversion rates, improve user engagement, or something else? By clearly defining your goals and metrics, you can focus on measuring the success of your test accurately.
Test One Element at a Time
To get meaningful results, it’s crucial to test one element at a time. Whether it’s a headline, button color, or layout, changing multiple elements simultaneously can make it difficult to understand the impact of each change. By isolating the variables, you can identify which specific element contributes to the performance difference.
Ensure Sufficient Sample Size
A/B testing requires a sufficient sample size to obtain valid and statistically significant results. If the sample size is too small, the results may not be reliable or applicable to the broader population. It’s essential to determine the appropriate sample size based on your goals, expected effect sizes, and statistical power calculations.
Randomize and Balance Traffic Allocation
To ensure unbiased results, it’s important to randomly assign visitors to different versions of your page. Randomization helps to eliminate any potential selection biases and ensures that each version has an equal chance of being shown to visitors. Additionally, it’s important to balance traffic allocation evenly between different versions to avoid confounding factors.
Monitor Results and Iterate
A/B testing is an iterative process, and it’s crucial to monitor the results carefully. Analyze the data and determine if one version performs significantly better than the others. If one version shows promise, consider implementing the winning variation and continue testing other elements to further optimize your page or app.
In conclusion, A/B testing is a powerful technique for improving the performance of your webpage or app. By following these best practices, you can maximize the effectiveness of your A/B tests and make data-driven decisions to enhance user experience and achieve your goals.
Tools for Implementing Canary Testing
When it comes to implementing canary testing, there are several tools available that can help you streamline the process and ensure accurate results. Whether you’re deciding between A/B testing and canary testing, or simply want to add canary testing to your existing testing strategy, these tools can make the process easier and more efficient:
1. Feature Flags
Feature flags are a powerful tool that allows you to enable or disable specific features in your application for different users or groups of users. This can be particularly useful when implementing canary testing, as you can gradually roll out a new feature to a small percentage of users to gather feedback and ensure stability before releasing it to a wider audience.
2. Deployment Pipelines
Deploying changes to production can be a risky endeavor, especially when it comes to introducing new features or making significant updates. Using deployment pipelines can help mitigate these risks by automating the deployment process and allowing you to test changes in a controlled environment before releasing them to your users. With canary testing, deployment pipelines can be used to deploy changes to a small subset of users and monitor their behavior, ensuring that any issues are caught before impacting a larger audience.
By utilizing these tools, you can effectively implement canary testing and gain valuable insights into the impact of your changes on your user base. Whether you choose canary testing over A/B testing or use both in combination, these tools will help you make informed decisions and improve the quality of your software.
Tools for Implementing A/B Testing
Implementing A/B testing is crucial for businesses looking to optimize their website or application’s performance. It allows you to compare two or more versions of a page or feature to determine which one performs better in terms of user behavior, conversions, and overall success. Here are some popular tools for implementing A/B testing:
1. Canary
Canary is a powerful A/B testing tool that offers a wide range of features to optimize your testing process. With Canary, you can easily create and manage multiple experiments, define your test goals, and track user interactions. It provides detailed reports and analytics to help you make data-driven decisions.
2. A/B
A/B is another popular tool for implementing A/B testing. It allows you to easily create and run experiments, set up test variations, and monitor the performance of each variant. A/B provides statistical analysis to help you determine the significance of your results and make informed decisions on which variant performs better.
Both Canary and A/B are great options for implementing A/B testing. The choice between them depends on your specific testing needs, budget, and technical requirements. Consider factors like ease of use, integration capabilities, and customer support when making your decision.
In conclusion, A/B testing is essential for optimizing your website or application. By using the right tools, such as Canary or A/B, you can effectively run experiments, analyze results, and make data-driven decisions to improve website performance and user experience.
Customer Feedback on Canary Testing
Customer feedback is crucial when it comes to making decisions about which testing method to choose, whether it’s A/B testing or canary testing. By taking into account the thoughts and experiences of real users, businesses can better understand how their products or services are being received and make improvements accordingly.
When it comes to canary testing, customers have expressed positive opinions about its effectiveness in catching potential issues before they become widespread. The ability to test a new feature or update on a small subset of users allows for quicker feedback and the opportunity to make adjustments before rolling out to a larger audience.
Customers appreciate the proactive approach of canary testing, as it allows businesses to uncover any bugs or performance issues early on, minimizing the impact on the overall user experience. This helps to build trust and confidence in the brand, as customers know that their feedback is being taken seriously and acted upon.
Furthermore, canary testing allows for more in-depth monitoring and analysis of user behavior and preferences. By closely observing the responses of the canary group, businesses can gain valuable insights into how users are interacting with the new feature or update, enabling them to make data-driven decisions for future enhancements.
Overall, customer feedback on canary testing has been positive, with users appreciating the benefits it brings in terms of early issue detection, improved user experience, and data-driven decision making. While A/B testing is also a valuable method, canary testing offers a unique approach that gives businesses a competitive edge in delivering high-quality products and services to their users.
Customer Feedback on A/B Testing
Customer feedback is a crucial component of any A/B testing strategy. It helps in gathering valuable insights about user preferences, behavior, and overall satisfaction with the tested variations. By leveraging customer feedback, companies can make data-driven decisions and optimize their website or app based on the insights gained from A/B testing.
Benefits of Customer Feedback in A/B Testing
There are several benefits of collecting customer feedback during the A/B testing process:
- Insight into User Preferences: Customer feedback provides a deeper understanding of what users like or dislike about a particular variation. This information can help in optimizing the design, layout, and content of a website or app.
- Identification of Pain Points: Customers often highlight pain points or areas where they face difficulties while navigating a website or using an app. By addressing these pain points, businesses can improve the overall user experience and increase customer satisfaction.
- Validation of A/B Test Results: Customer feedback can help validate the results of an A/B test. If a particular variation performs exceptionally well or poorly, customer feedback can provide additional insights into why users might prefer or dislike that variation.
- Enhanced Decision Making: By combining quantitative data from A/B tests with qualitative customer feedback, businesses can make more informed decisions. This approach ensures that decision-making is based on a holistic understanding of user preferences.
Methods for Collecting Customer Feedback
There are various methods businesses can use to collect customer feedback during A/B testing:
- Surveys: Online surveys can be conducted to gather feedback from users. These surveys can include both closed-ended and open-ended questions to capture specific preferences and general opinions.
- User Interviews: Conducting one-on-one interviews with users can provide in-depth insights into their experiences and preferences. These interviews can be done in person or remotely through video calls.
- Heatmaps and Click Tracking: Heatmaps and click tracking tools can help analyze user behavior, navigation patterns, and interactions with different variations. This data can be valuable in uncovering areas of interest or user engagement.
- User Reviews and Ratings: Monitoring user reviews and ratings on app stores, review websites, or social media platforms can provide feedback from a broader user base. This feedback may include both positive and negative aspects of a particular variation.
By incorporating customer feedback into the A/B testing process, businesses can gain a comprehensive understanding of user preferences and make data-driven improvements to their website or app. Combining A/B testing with customer feedback allows companies to optimize their digital assets and ultimately deliver a better user experience.
Question-answer:
What is the difference between canary testing and A/B testing?
Canary testing involves releasing a new feature or change to a small subset of users to gather feedback and identify any potential issues before rolling it out to a larger audience. A/B testing, on the other hand, involves comparing two or more versions of a feature or webpage by randomly dividing the audience and measuring their responses to determine which version performs better.
When should I use canary testing?
Canary testing is particularly useful when introducing major changes or new features to an application, as it allows you to validate the changes with a small audience before releasing them to the wider user base. It helps to identify any issues or potential problems and make adjustments based on user feedback before a full rollout.
What are the benefits of A/B testing?
A/B testing provides valuable insights into user behavior and preferences. It allows you to make data-driven decisions by comparing different versions of a feature or webpage and measuring their impact on user engagement, conversion rates, and other important metrics. By identifying the version that performs better, you can optimize your website or application to enhance user experience and achieve your business goals.
Which testing approach is more suitable for continuous integration and deployment?
Canary testing is often preferred for continuous integration and deployment as it allows you to gradually release changes or new features to a small group of users. By monitoring and gathering feedback from this subset of users, you can quickly detect any issues or performance problems and make necessary improvements before rolling out the changes to a larger audience. This helps to ensure a smoother and more reliable deployment process.
Is one testing approach inherently better than the other?
There is no definitive answer to this question. The choice between canary testing and A/B testing depends on your specific testing needs and goals. Canary testing is ideal for validating major changes or new features with a small audience, while A/B testing is valuable for optimizing user experience and making data-driven decisions. It’s best to consider the context and objectives of your testing and choose the approach that aligns with your goals.
What is a canary test?
A canary test is a type of testing where a small group of users are exposed to a new feature or change before it is released to the rest of the users. This allows for early feedback and helps to identify any issues or bugs before they impact a larger user base.
What is A/B testing?
A/B testing is a method of testing where two or more versions of a webpage or feature are shown to users, and the results are analyzed to determine which version performs better. It is commonly used to test different designs, layouts, or content elements to optimize the user experience and improve conversion rates.
How do canary and A/B testing differ?
Canary testing focuses on testing new features or changes on a small group of users before a wider release, while A/B testing involves testing multiple versions of a feature or webpage and comparing their performance to determine the most effective version.
Which type of testing is better for finding bugs?
When it comes to finding bugs, a canary test can be more effective as it exposes a new feature or change to a small group of users who can provide early feedback. This allows bugs to be identified and addressed before a wider release. A/B testing is more focused on comparing the performance of different versions, so it may not be as effective for finding bugs.