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Effective Testing using Taguchi's Orthogonal Arrays (OA)

Effective Testing

Objectives of Effective Testing

  • Reduce cycle time of the test phase.
  • Offer maximum coverage with minimum test cases.
  • Find maximum defects with minimal no of test cases.
  • Goal is to obtain reproducible results incurring the least possible expenses.

Dr Genichi Taguchi's Orthogonal Array (OA)

  • Based on Combinatorial and Orthogonality theories
  • Allows the designer to execute only a subset of all possible combinations.
  • Orthogonal Array test cases can be customized based on available time and known problems.
  • Independent of platforms and domains.

Advantages of using OA

  • Results in smaller number of experiments.
  • More efficient in handling a large number of input variables.
  • Helps to determine the contribution of each input that influences quality of the product.
  • Allows easy interpretation of experiments.

How to apply??

  • Step 1: Brainstorming

    • First and most important step in experimental design.
    • Mostly performed by a team rather than an individual.
    • Output of Brainstorming session
      • Define the objective: Reduce the number of Test Cases and choose the one that best suits the problem in hand.
      • Create a problem statement: Convert the engineering situation into an experimental design problem.
      • Identify the factors: A factor is a variable under study –an input that can be controlled.
      • Identify the levels: A level is a value that a factor can assume when used in experiment.
  • Step 2: Designing the experiments

    • Select an appropriate orthogonal array from a standard set of orthogonal array defined by Taguchi.

OA

No. of Experiments

 No. of Factors

Max No. of factors at these levels(Level 2)

L-4

4

3

3

L-8

8

7

7

L-32

32

10

1

  • Assign Factors: Once OA is finalized, the factors are assigned to the columns of the array and the integers are translated to the different levels the factors can take.
    • Each row corresponds to the particular experiment
    • Unassigned columns can be deleted from the array.
  • Step 3: Running the experiments

    • Execute the tests as designed.
  • Step 4: Analyzing the results

    • Uses statistical treatment
    • Analysis of Means (ANOM)-which determines the effects of the various parameters.
    • Analysis of Variance (ANOVA)-the variation contributed by each factor can be determined.
  • Step 5: Running confirmation experiments.

    • Optimize the final product.
    • Running the experiments at the optimum conditions.

Where to apply?

  • Configuration Testing
  • User Interface Testing
  • Performance Tuning
  • Regression Testing.

Verification Suite Development Using OA

  • Defining the Parameters and the respective Levels is the crucial activity in the entire process.
  • Optimize combination of test cases.
  • Help of the tool called Minitab can be used for this purpose.
  • For Minitab the input is the Parameters and levels and the output is the optimized test case combination.
  • Generally the code coverage is >90%, if the parameters and levels selection is effective.
  • Thus this method can give a very high code coverage which would ensure that the testing of all functionality in a minimum time and cost.
  • A good analysis is required while identifying the parameters and levels.
  • If the possibility of identifying parameters and levels is tough –it is better to look for alternatives rather than using OA technique.

The Way Ahead

  • Future of testing lies in statistics!
  • These methods doesn’t address all aspects of system testing
  • Tester’s intuition is still a much needed asset!

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