Test automation and Continuous Delivery
Software testing and verification needs a careful and diligent process of impersonating an end user, trying various usages and input scenarios, comparing and asserting expected behaviors. Directly, the words "careful and diligent" invoke the idea of letting a computer program do the job. Automating certain programmable aspects of your test suite thus can help software delivery massively. In most of the projects that I have worked on, there were aspects of testing which could be automated, and then there were some that couldn't. Nonetheless, my teams could rely heavily on our automation suite when we had one, and spend our energies testing aspects of the application we could not cover with automated functional tests. Also, automating tests helped us immensely to meet customer demands for quick changes, and subsequently reaching a stage where every build, even ones with very small changes went out tested and verified from our stable. As Jez rightly says in his excellent text about Continuous Delivery, automated tests "take delivery teams beyond basic continuous integration" and on to the path of continuous delivery. In fact, I believe they are of such paramount importance, that to prepare yourself for continuous delivery, you must invest in automation. In this text, I explain why I believe so.
Cycle Time - from your last check-in to deployment
As the complexity of software grows, the amount of effort verifying changes as well as features already built grows, at least linearly. This means that testing time is directly proportional to the number of test cases needed to verify correctness. Thus, adding new features means that testing either increases the time it takes a team to deliver software from the time development is complete, or it adds cost of delivery if the team adds more testers to cover the increased work (assuming all testing tasks are independent of each other). A lot of teams, and I have worked with some, tackle this by keeping a pool of testers working on "regression" suites throughout the length of a release verifying if new changes break already built functionality. This is not only costly, its ineffective, slow and error prone.
Automating test scenarios where you can lets you cut this time/money it takes to verify if a user's interaction with the application works as designed. At this point, let us assume that a reasonable number of your test scenarios can be automated, say 50%, as this is often the least bound in software projects. If your team can and does automate this set to a certain number of repeatable tests, it frees up people to concentrate more on immediate changes. Also, lets suppose that it takes as much as 3 hours to run your tests (it should take as less as possible, less than 20 minutes even). This directly impacts the amount of time it takes to push a build out to customers. Increasing the number of automated tests, and also investing in getting the test-run time down, your agility and ability to respond increases massively, while also reducing the cost. I explain this with some very simple numbers (taking an average case) below:
- Number of scenarios to test - 1000 and growing.
- Number of minutes to setup environment for a build - 10 minutes.
- Number of minutes to test one scenario - 10 minutes.
- Number of testers in your team - 5.
- Assume that there are no blockers.
If you were to have no automated tests, the amount of time it would take to test one single check-in (in minutes):
10 + (1000*10)/5 = 2010 minutes.
This is close to 4 working days (standard 8 hours each). Not only is this costly, it means that if developers get feedback 4 days later. This kind of a setup further encourages mini-waterfalls in your iteration.
Same as Team A, but we've automated 50% (500 test cases) of our suite. Also, assume that running these 500 test cases take a whopping 3 hours to complete.
Now, the amount of time it would take to test one single check-in (in minutes)
task 1 (manual) - 10 + (500*10)/5 = 1010 minutes.
task 2 (automated) - 10 + 180 minutes.
This is close to 2 working days. This is not ideal, but just to prove the fact about reduced cost, we turned around the build one day before. We halved the cost of testing. We also covered 50% of our cases in 3 hours.
Now to a more ideal and (yet) achievable case -
Same as Team B, but we threw in some good hardware to run the tests faster (say 20 minutes), and automated a good 80% of our tests (10% cannot be automated and 10% is new functionality).
Now, the amount of time it would take to test one single check-in (in minutes)-
task 1 (manual) - 10 + (200*10)/5 = 410 minutes.
task 2 (automated) - 10 + 20 minutes = 30 minutes.
So in effect, we cover 80% of our tests in 30 minutes, and overall take 7 hours to turn around a build. Moreover, the probability of finding a blocker earlier, by covering 80% of our cases in 30 minutes, means that we can suspend further manual testing if we need to. Our costs are lower, we get feedback faster. This changes the game a bit, doesn't it?
Cost of testing per deployment
Automation not only reduces cycle times, it also improves the cost of each deployment. This cost reduction happens in two ways -
- Earlier feedback to developer through automated tests improves the quality of builds that the testers work on, provided they pick up green builds
- Direct reduction in testing cost, as lesser number of people are needed to run the same set of tests over and over.
The direct cost reduction is easily explained. Using the same teams we saw above, the following chart shows the costs incurred by the team when they run a full round of tests; I am assuming a modest $50 an hour per tester here.
As you can see, the cost that Team A (a whopping $100k) incurs is almost 5 times that Team C does, in order to turn around one deployment candidate build.
There is also a dramatic decrease in testing costs over a long(-ish) period of time, lets say 2 years. The goal is to turn around every deployable build within a day - a modest goal, something that Team C does as of now. Team A needs 25 testers to do this, Team B needs 15. I will also add the cost of hardware and authoring here, to illustrate the implications over a period of 2 years, we should add
- Hardware cost - $50k for machines and more.
- Authoring cost - initial investment of 2 developers for 2 months on authoring, and the rest of the time maintaining the test suite, Team B and C size is 7 now.
With these numbers, Team A costs a whopping $4.8 million over a period of 2 years to meet the goal of turning around a build within a day. Team C in comparison costs close to $1.4 million, a 70% reduction in cost. The following chart illustrates this visually:
(Click on the image to enlarge it)
Apart from cycle time and incurred costs, automated tests add value in a lot of indirect ways, some of which are:
Verification on time
Team A that I mentioned above would need 50 testers to certify a build in less than 2 hours. That cost is not surprisingly unattractive to customers. In most cases, it is almost impossible to turn around a build from development to delivery within a day, without automation. I say almost impossible, as this would prove to be extremely costly in cases where it is. So, if assuming that my team doesn't automate and hasn't got an infinite amount of money, every time a developer on the team checks-in one line of code, our time to verify a build completely increases by hours and days. This discourages a manager to schedule running these tests every time on every build, which consequently decreases the quality coverage for builds, and the amount of time bugs stay in the system. It also, in some cases I have experienced, dis-incentivizes frequent checking in of code, which is not healthy.
Early and often feedback
One of the most important aspects of automation is the quick feedback that a team gets from a build process. Every check-in is tested without prejudice, and the team gets a report card as soon as it can. Getting quicker feedback means that less code gets built on top of buggy code, which in turn increases the credibility of software. To extend the example of teams A, B and C above:
- For Team A - the probability of finding a blocker on day one is 1/2. Which basically means that there is a good risk of finding a bug on the second day of testing, which completely lays the first days of work to waste. That blocker would need to be fixed, and all the tests need to be re-verified. The worst case is that a bug is found after 2 days of an inclement line of code getting checked-in.
- For Team B - the worst case is that you find a blocker during the last few hours of the day. This is still much better than for Team A. Better still, as 50% of test cases are automated, the chance of finding a blocker within 3 hours is very high (50%). This quick feedback lets you find and fix issues faster, and therefore respond to customer requests very quickly.
- For Team C - the best case of all 3. The worst scenario is that Team C will know after 3 hours if they checked-in a blocker. As 80% of test cases are automated, by 20 minutes, they would know that they made a mistake. They have come a long way from where Team A is, 20 minutes is way better than 2 days.
Economists use an apt term - opportunity cost to define what is lost if one choice amongst many is taken. The opportunity cost of re-verifying tedious test cases build after build is the loss of time spent on exploratory testing. More often than not a bug leads to many, but by concentrating on manual scenarios, and while catching up to do so, testers hardly find any time to create new scenarios and follow up on issues. Not only this, it is imperative that by concentrating on regression tests all the time, testers spend proportionately less time on newer features, where there is a higher probability of bugs to be found. By automating as much as possible, a team can free up testers to be more creative and explore an application from the "human angle" and thus increase the depth of coverage and quality. On projects I have worked on, whenever we have had automated tests aiding manual testing I have noticed better and in-depth testing which has results in better quality.
Another disadvantage is that manual testing involved tedious re-verification of the same cases day after day. Even if they were creative to distribute tests to different people every day, the cycle would inadvertently repeat after a short period of time. Testers have less time to be creative, and therefore their jobs less gratifying. Testers are creative beings and their forte is to act as end-users and find new ways to test and break an application, not in repeating a set process time after time. The opportunity cost in terms of keeping and satisfying the best testers around is enormous without automation.
Believe it or not, even the best of us are prone to making mistakes doing our day-to-day jobs. Given how good or bad we are it, the probability of making a mistake while working is higher or lower, but mostly a number greater than 0. It is important to keep this risk in mind while ascertaining the quality of a build. Indeed, human errors are generally behind most bugs that we see in software applications that we see, error during development and testing. Computers are extremely efficient doing repetitive tasks - they are diligent and careful, which makes automation a risk mitigation strategy.
Tests as executable documentation
Test scenarios provide an excellent source of knowledge about the state of an application. Manual test results provide a good view of what an application can do for an end user, and also tell the development team about quirky components in their code. There are two components to documenting test results - showing what an application can do, and upon failures, documenting what fails and how, so its easy to manage application abnormalities. If testers are diligent and make sure they keep their documentation up to date (another overhead for them), it is possible to know the state of play through a glance at test results. The amount of work increases drastically with failures, as testers then need to document each step, take screenshots, maybe even videos of crash situations. Adding the time spent on these increase the cost of making changes, in fact in a way the added cost dis-incentivizes documenting the state with every release.
With automated tests, and by choosing the right tools, the process of documenting the state of an application becomes a very low cost affair. Automation testing tools provide a very good way of executing tests, collating results in categories, and publishing results to a web page, and also let you visualize test result data to monitor progress and get relevant feedback from test result data. With tools like Twist, Concordian, Cucumber and the lot, it becomes really easy to show your test results to your customers and even let them author test; this reduces the losses in translation with the added benefit of customer getting more involved in application development. Upon failures, a multitude of testing tools automate the process of taking screenshots and videos to help document failures and errors in a more meaningful way. Results could be mailed to people, or much better, served as RSS feeds per build to people who are interested.
Technology facing tests
Testing non-functional aspects of an application - like testing application performance upon a user action, testing latency over a network and its effect on an end-users interaction with the application etc. have traditionally been partially automated (although very early during my work life I have sat with a stop watch in my hand to test performance, low-fi but effective). It is easy to take advantage of automated tests and reuse them to tests such non-functional tests. For example, running an automated functional test over a number of times can tell you average performance of an action on your web-page. The model is easy to set up, put a number of your automated functional tests inside a chosen framework that lets you setup and probe non-functional properties while the tests are run. Testing and monitoring aspects like role-based security, effects of latency, query performance etc. can all be automated by re-using an existing set of automated tests, an added benefit.
On your journey to Continuous Delivery, small and big, you would have to take many steps. The most difficult hurdle would be to reduce your cycle times and keeping costs low at the same time. Automated tests help you achieve these, and well as help you build far more effective and happy teams that test while they develop. My understanding and suggestion would be to start small with a good investment on a robust automation suite, give it your best people, cultivate habits in your team that respect tests and results, build this backbone first, and then off you would be. Have a smooth ride.
About the Author
Ranjan D Sakalley works with ThoughtWorks Studios as a Lead Consultant. In past he has worked as a developer, Project Manager and Agile Coach at ThoughtWorks and prior employers. He is passionate about automation, continuous delivery, writing good code and working with people in general. His favorite pastimes are reading comic books and watching Manchester United keep rolling on. You can follow him on twitter at rnjn or through his blog.
Dealing with Projects with No Automated Tests
Re: Dealing with Projects with No Automated Tests
Great to know that you want to initiate this on your team. A very strong actionable technique that I have seen work well is that you create a small/minimal smoke test suite for you larger app. You can decide on what comprises a smoke test -
1. Basic user actions - some 4-5 use cases without which a user cannot use your application. (for eg. on an ecommerce site, my smoke tests would comprise of login, user registration, search, end-to-end purchase and payment).
2. Integration points - choose must-have integrations - like payment gateway (same ecommerce motif), oauth for authentication against facebook or some such. Write smoke tests that touch these integrations to know if every build that you process is verified for these.
3. Attack a small part/feature of the application. You can choose the most important, the most buggy etc. Write tests only for this part.
4. Concentrate on the currently "under-development" work, which will give the team immediate value in terms of coverage. This is counter-intuitive and there would be a lot of changes while you work. But I have seen this work wonders at times, and your team starts worrying about test maintenance from the first step on.
You can choose more ways, better mix and match. The correct way to work with these would be to make these smoke tests as part of your CI process.
I am sure with this smoke suite in, your team will start seeing value in automating regression scenarios. From then on, you can your suite from smoke upwards, adding more complex or more detailed scenarios. Another thing to note on big complex applications - aim for small tests, get quick wins. Then combine to make bigger ones. This will keep your team involved and constantly working towards covering every possible nook. But do make sure everything is part of CI, because if its not, then people lose faith.
Finally, as ever, keep showing value, make sure the stakeholders understand the importance, you can do this by sending them reports, and talk about gains etc. If your team buys in with the idea, there's no stopping.!!
Hope this helps,
Does your project needs include XML testing automation?
This enables both regression content checking - but also validation via rule engines.
Introducing CAMV validation engine and CAM templates (OASIS Content Assembly Mechanism) approach.
Essentially there is an ANT scripting test suite setup - so you can create templates to validate content conforms to your XML transaction templates.
Obviously you can extend this to do a whole ton of creative stuff - right now here's what it will do for you out the box.
o Code lists and SQL table lookups with conditional rules
o Cross-field validation rules
o Dynamic XML structure components inclusion/exclusion
o Error and warning level reporting
o XPath V2.0 rules for extended business logic
o Extensible rule templates with schema-aware validations
o Clear business documentation for policy analyst verification
o Open source with open platform deployment
o Public open standards based from W3C and OASIS
o High performance thread safe execution engine
o Dynamic template driven deployment framework
o Spring API compatible for middleware integration
o Ant scripting for test suite implementations
o Flexible result rendering, routing and reporting
Also everything needed runs cross-platform on Windows, MacOs and Linux and is open source based.
Test suite configuration has been made very simple - you merely put all the XML instances in one folder together - setup a test script for pointing to that and your compiled validation template - and run it.
The complete configuration details are explained in a handy quick guide PDF that you will find linked to here - www.cameditor.org/#CAMV_Testing
Maintenance of the Test Automation scripts themselves?
1) take time and effort to develop in the 1st place
2) may have bugs in them too, which again take time and effort to "test" and fix. Do we not get ourselves into a never ending cycle here?
3) to setup these tests you use development resources rather than testing resources and therefore loose time to include more features to a product
Re: Maintenance of the Test Automation scripts themselves?
I think the way to deal with the complexity and cost associated with test automation is to identify tools that do a solid job of taking care of the Framework part of the problem and lets you concentrate on the Test Scripts part of it.
There are a number of tools that I have used that solves this problem.
1. Twist from ThoughtWorks Studios (www.thoughtworks-studios.com/twist-agile-testing)
2. Cucumber from the Cucumber community (cukes.info/)
Disclosure: I work at ThoughtWorks.
I think your cost per tester is incorrect.
Correct me if I'm wrong, but it looks like when you calculated cost per tester, you calculated them making $50/minute, not $50/hour. That's the only way I can come up with numbers that match yours.
If you break it down into hours then:
Team A = ~$8,375
Team B = ~$4,200
Team C = ~$1,710
While those numbers aren't as impressive as the over $100k, the cost savings is still very apparent on the chart.
If my numbers are wrong, would you mind further breaking down your cost analysis?
Iordanis Giannakakis, Savvas Dalkitsis Aug 28, 2015
Ben Linders Aug 28, 2015