What I would like to see is a breakdown of how many failures fall into which category.
And of the above categories, while useful for root cause analysis and eventual fix, for sake of triaging results, and disposition of what to do if hitting such a failure, it seems that whether or not the failure is a true product failure is a HUGE difference from the other three.
For the other three, the major risk is cost in time and compute. For product failure, the major risk is that the bug might escape if it is ignored, misunderstood, or dismissed.
This suggests that if we could be very good at determining if a failure is in product versus one of the other three categories we can respond differently to the failure when we see it. For non-product category, run the test again and if it passes again consider the result passing. Capture the issue for sake of engineering system cost and capacity - but at least you have saved an engineer some confusion and time. For product category, running the test again does not give us any assurance other than understanding its intermittent nature.
The trick, I believe, is getting very good at detecting the difference between product failure and non-product failure. If we could do that with high confidence, we would have a way of saving considerable engineer time.
> This article has both outlined the areas and the types of flakiness that can occur in those areas, so it can serve as a cheat sheet when triaging flaky tests.
Speaking of triaging, as the number of flaky tests in the code base grows, I've often found it becomes laborious to reliably keep track of them. And even if you can keep track of them, you then need to determine which ones are causing the biggest problems so you can focus on them first. Teams frequently start by trying to track this info in issues or a spreadsheet, but nobody really _wants_ to do that, so (in my experience) everyone eventually stops doing it, and now you're back to square one. ��
After experiencing this too many times, I set out to offer a way for teams to automatically detect, track, and rank flaky tests: https://buildpulse.io
Sharing here in case any other readers are in the same boat that I've found myself in so many times in the past. ��
Thank you for sharing your article George. From my experience, Flaky Tests originate from three fundamental problems.
1) Synchronization issues - A synchronization issue comes from not having a precise understanding of the environment's state. Most Automation Engineers would reduce most of their flaky tests by mastering the following four synchronizations (translated into automation code). 1) Does an object exist at this exact moment in time? 2) Does an object not-exist at this exact moment in time? 3) Does an object exist in this maximum amount of time, rechecking on this interval of time? 4) Does an object not-exist in this maximum amount of time, rechecking on this interval of time?
These four fundamental synchronization methods will make a significant difference in reducing the flakiness of automation.
2) Object Locator Strategy - There are so many different ways to identify an object. Each test engineer generally has their favorites, and I have mine as well. Regardless of approach, your locator strategy should be testable, without the need to see it work in the running automation. Chrome Developer Tools provides an excellent way to debug locators, allowing the automation engineer to tune and refine them before implementing them within the automation.
3) Automation Evaluation - The automation Engineer should have an approach to evaluate what should and should not be automated. Developing automation can be like playing with Lego's. An undisciplined Automation Engineer can be tempted to automate everything in sight, regardless of it being the right candidate for automation. -
Here's a good article on these topics - https://testguild.com/podcast/automation/a278-greg-max/
In my experience, test flakiness is mostly due to- a) the application under test not able to handle concurrent hits or a series of very frequent hits. And this certainly raises a concern on the application performance.
b) an unstable or weak network connection.(this is true)
If your application is not designed to scale properly, your tests will eventually fail.
> What I would like to see is a breakdown of how many failures fall into which category.
I conducted a small survey, which partially answers to the question. The interesting part is that often the main reason of untenability is env issues. The survey results can be viewed by link - https://docs.google.com/forms/d/1yMedOYcnA8VBuL-ROfcimv9v21wmiRB8xfjN-np4UHM/viewanalytics
This is very nicely written and includes everything that we encounter on a usual test automation day ! I am looking forward to an equally nice article to fix these causes of flakiness or at least some creative workaround to prevent the same. I would also like to share (out of my experiences)that automation tools can also act intrusive and induce flakiness as well.
Thank you George for your detailed description and categorization of reasons causing the flaky tests.
On a seperate note, I do have a request - It would be greatly helpful if you can let us know your thoughts on the shift-left paradigm of software testing, where we are trying to emphasize on Unit and Integration Tests. Particularly, I'm very interested to know your thoughts on the value additions and defect coverage that Unit and Integration brings into the product.
One of the main challenges of automated testing. Dealing with test flakiness is a critical skill in testing because automated tests that do not provide a consistent signal will slow down the entire development process... check here https://www.h2kinfosys.com/blog/quality-assurance-tutorials/
This is very nicely written and includes everything that we encounter on a usual test automation day ! I am looking forward to an equally nice article to fix these causes of flakiness or at least some creative workaround to prevent the same. I would also like to share (out of my experiences)that automation tools can also act intrusive and induce flakiness as well.
What I would like to see is a breakdown of how many failures fall into which category.
ReplyDeleteAnd of the above categories, while useful for root cause analysis and eventual fix, for sake of triaging results, and disposition of what to do if hitting such a failure, it seems that whether or not the failure is a true product failure is a HUGE difference from the other three.
For the other three, the major risk is cost in time and compute.
For product failure, the major risk is that the bug might escape if it is ignored, misunderstood, or dismissed.
This suggests that if we could be very good at determining if a failure is in product versus one of the other three categories we can respond differently to the failure when we see it. For non-product category, run the test again and if it passes again consider the result passing. Capture the issue for sake of engineering system cost and capacity - but at least you have saved an engineer some confusion and time. For product category, running the test again does not give us any assurance other than understanding its intermittent nature.
The trick, I believe, is getting very good at detecting the difference between product failure and non-product failure. If we could do that with high confidence, we would have a way of saving considerable engineer time.
> This article has both outlined the areas and the types of flakiness that can occur in those areas, so it can serve as a cheat sheet when triaging flaky tests.
ReplyDeleteSpeaking of triaging, as the number of flaky tests in the code base grows, I've often found it becomes laborious to reliably keep track of them. And even if you can keep track of them, you then need to determine which ones are causing the biggest problems so you can focus on them first. Teams frequently start by trying to track this info in issues or a spreadsheet, but nobody really _wants_ to do that, so (in my experience) everyone eventually stops doing it, and now you're back to square one. ��
After experiencing this too many times, I set out to offer a way for teams to automatically detect, track, and rank flaky tests: https://buildpulse.io
Sharing here in case any other readers are in the same boat that I've found myself in so many times in the past. ��
Thank you for sharing your article George. From my experience, Flaky Tests originate from three fundamental problems.
ReplyDelete1) Synchronization issues - A synchronization issue comes from not having a precise understanding of the environment's state. Most Automation Engineers would reduce most of their flaky tests by mastering the following four synchronizations (translated into automation code).
1) Does an object exist at this exact moment in time?
2) Does an object not-exist at this exact moment in time?
3) Does an object exist in this maximum amount of time, rechecking on this interval of time?
4) Does an object not-exist in this maximum amount of time, rechecking on this interval of time?
These four fundamental synchronization methods will make a significant difference in reducing the flakiness of automation.
2) Object Locator Strategy - There are so many different ways to identify an object. Each test engineer generally has their favorites, and I have mine as well. Regardless of approach, your locator strategy should be testable, without the need to see it work in the running automation. Chrome Developer Tools provides an excellent way to debug locators, allowing the automation engineer to tune and refine them before implementing them within the automation.
3) Automation Evaluation - The automation Engineer should have an approach to evaluate what should and should not be automated. Developing automation can be like playing with Lego's. An undisciplined Automation Engineer can be tempted to automate everything in sight, regardless of it being the right candidate for automation. -
Here's a good article on these topics - https://testguild.com/podcast/automation/a278-greg-max/
Great Points there Greg!
DeleteIn my experience, test flakiness is mostly due to-
a) the application under test not able to handle concurrent hits or a series of very frequent hits.
And this certainly raises a concern on the application performance.
b) an unstable or weak network connection.(this is true)
If your application is not designed to scale properly, your tests will eventually fail.
> What I would like to see is a breakdown of how many failures fall into which category.
ReplyDeleteI conducted a small survey, which partially answers to the question. The interesting part is that often the main reason of untenability is env issues. The survey results can be viewed by link - https://docs.google.com/forms/d/1yMedOYcnA8VBuL-ROfcimv9v21wmiRB8xfjN-np4UHM/viewanalytics
This is very nicely written and includes everything that we encounter on a usual test automation day ! I am looking forward to an equally nice article to fix these causes of flakiness or at least some creative workaround to prevent the same. I would also like to share (out of my experiences)that automation tools can also act intrusive and induce flakiness as well.
ReplyDeleteThank you George for your detailed description and categorization of reasons causing the flaky tests.
ReplyDeleteOn a seperate note, I do have a request - It would be greatly helpful if you can let us know your thoughts on the shift-left paradigm of software testing, where we are trying to emphasize on Unit and Integration Tests. Particularly, I'm very interested to know your thoughts on the value additions and defect coverage that Unit and Integration brings into the product.
One of the main challenges of automated testing. Dealing with test flakiness is a critical skill in testing because automated tests that do not provide a consistent signal will slow down the entire development process... check here https://www.h2kinfosys.com/blog/quality-assurance-tutorials/
ReplyDeleteThis is very nicely written and includes everything that we encounter on a usual test automation day ! I am looking forward to an equally nice article to fix these causes of flakiness or at least some creative workaround to prevent the same. I would also like to share (out of my experiences)that automation tools can also act intrusive and induce flakiness as well.
ReplyDelete