Test case Generator with Optimized coverage - Orthogonal Array Testing Strategy (OATS)

  1. Are you Confident about your test coverage?
  2. Do you have millions of permutations and combinations to be covered in testing?
  3. is automation the solution to cover million combinations?
  4. why can not my system decide what test cases to be executed to give complete test coverage?
  5. As part of CI/CD , instead of executing same test cases irrespective changes going in, would you like to execute more appropriate test cases ?

I will be presenting quick demo on statistical approach and strategy which can help us to generate appropriate number of test cases automatically based on the impacted areas and business metrics . Let me share an example below.

Let’s say we are testing any eCommerce application Flipcart or amazon. We will have to cover multiple combinations like different categories of items, different payment types, shipping types, Promotions, user types etc.. . I will use little bit of sets theory here.

Let us take below 5 sets as an example. Assume that there is new payment type is getting introduced as “Paypal” as enhancement. If we need to make sure this new payment type is working with all products, all shipments types , all user types etc… we will have to cover all permutations and combinations which is nothing but Cartesian product of all these sets. The Cartesian product of below 5 sets is - 6*3*2*4*3 =432 test cases.

Do we really need to execute 432 test cases just for one new payment type ? we just need to execute 24 test case to give confident test coverage .This can be done with OATS tool which is developed with intelligence with Business user Metrics + Orthogonal array testing approach ( OATS) approach . OATS tool will generate required number of test cases with simple input and one click .

There are 2 key areas of input that we consider before we generate optimized tests.

1. Business user metrics - Example : if you look at the below payment set , we would like to know what % of customers using each payment type in overall successful transactions. let us assume 60% Credit card payments , 20 % paytm and 20% rest of payment types . These metrics will help tool to give more weightage to Credit card test cases over other payment types. This make sense because we need to focus more on the areas our end users are using.

2. Impacted areas: let us take an example that there is new payment type (AmazonPay) is getting introduced as en enhancement. We should give more weightage to payment set and make sure all existing payment types are tested with different combinations.

Set1 - PAYMENT = { Cash, Credit card , Paytm , AmazonPay, Debit card}- # elements - 6

Set2 – SHIPPING – {Shiptoday, Prime Shipping, ship Tomorrow}-# elements - 3

Set3 – USER TYPE - { Guest User , Logged in user }-# elements - 2

Set4 – PRODUCT TYPE – {Electronics, Groceries, Books , Stationary}- # elements - 4

Set5 – PROMO TYPE – { Promo1 , Promo2 , Promo3}-# elements – 3

I will be covering how this can be done in detail during presentation along with Tool Demo which was built in with an intelligence using sets theory . you will get an answer on why only 24 test cases are good enough instead of 432 test cases for the change new payment type got introduced.

Advantages :

1. Optimized coverage with less test cases

2. Tester will be confident about test coverage

3. This tool output can be integrated with Selenium key word driven framework . All this happens during run time w/o manual intervention.

4. This strategy is good fit for automation testing .

5. if any combinations are invalid to test, those combinations will be excluded while generating tests.

6. There are tools in the market to provide statistics about untested code (Ex: JaCoCO ). These statistics can be used as input for OATS tool. We have not done this yet.


1. We have seen great value (reduced bug escape) applying this strategy to automated tests. However there is limitation in applying the same strategy to manual exploratory , Adhoc testing.

2. We have not tried this strategy for performance testing yet.

3. There are corner scenarios where human can only think will not be covered by this tool . These scenarios to be appended to generated test cases .


Outline/Structure of the Case Study

1. Problem statement -5 mins

2. Approach & strategy with examples - 10 mins

3. Exercises to audience from different domains -10 mins

3. Demo on tool including technical walkthrough - 15 mins

4. Q&A - 5 mins

Learning Outcome

1. Participants will understand how mathematical concepts studied in schools/colleges can be applied to our day to day jobs to increase the efficiency. (Sets theory , Permutations & combinations, Probability )

2. Audience will understand OATS approach and Strategy to design testing cases. once they go back to work, they can apply this strategy to test appropriate number of test cases . Also Audience will be able to build similar tool based on their application functionality.

3. This will encourage audience to build more in-house tools in future while applying statistical and mathematical concepts .

Target Audience

Quality assurance professionals

Prerequisites for Attendees

Basis understanding of sets , permutations and combinations.


schedule Submitted 2 years ago

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