# Expression Engine Job by babraagasha

Budjetti $1000 USD

First we define a quality goal&acirc;??we want to assure that our software is 99% bug free. That means that up to 1% of the user sessions would exhibit a bug. To be 100% confident that this statement is true, we would need to test at least 99% of the possible user sessions, or over 990,000 tests.

By adding a level of confidence to our analysis, we can use sampling (selecting a subset of the whole, and extrapolating those results as being characteristic of the whole) to describe the quality of our software. We will leverage the mathematical work that has been developed to determine how to run polls.

We define our goal to be that we have 99% confidence that the software is 99% bug free. The 99% level of confidence means that if we ran our sample repeatedly, 99% of the time, the results would be within the margin of error. Since our goal is 99% bug free code, we will test for 100% passing of tests, with a 1% margin of error.

How many samples do we need, if there are one million combinations, to identify the level of quality with a 99% confidence, and a 1% margin of error? The math for this is readily available, and calculators for determining sample size are online and free. Using this polling approach, we find that the number of samples we require to determine the quality level with a 1% error and 99% confidence is 16,369.

If we test 16,369 user sessions and find 100% success, we have established a 99% confidence that our quality is at least at a 99% level. We only have 99% quality, because we have found 100% quality in our tests, with a 1% margin of error.

This approach scales for very large numbers of combinations. Consider the following table, where our goal is to establish 99% confidence in a 99% quality level. Each row in the following table represents an increasingly complex software application. Complexity is defined as the number of unique combinations of possible inputs).

We can see that the very few additional tests have to be run to achieve the same level of quality for increasingly complex software. When we have a modest quality goal, such as 99/99 (99% confidence in 99% quality), this approach is very effective.

Where this approach doesn't scale well is with increasing levels of quality. Consider the quest for "five nines" ([url removed, login to view]% bug free code). With each increase in the desired level of quality, the number of tests we have to run grows. It quickly becomes an almost exhaustive test suite.

Each row in the following table represents an increasingly stringent quality requirement, with the complexity of the software staying constant at one million possible input combinations.

The random sampling approach does not provide a benefit over exhaustive testing when our quality goals are high.

Pairwise Testing of Input Variables

Studies have shown that bugs in software tend to be the results of the combination of variables, not individual variables. This passes our "gut-check" since we know that conscientious developers will test their code. What slips through the cracks is overlooked combinations of inputs, not individual inputs.

Consider a very simple laptop configuration page, having three selectable controls: CPU, Memory, and Storage. Each control has three possible values as shown in the table below.

We successfully pass tests of each of the different values available in the CPU control. However, we discover that our test fails if the user selects a CPU value of "Consumer" and selects a Storage value of "Huge". This highlights an unknown dependency between the CPI and Storage controls.

Pair-wise testing is designed to get coverage of every possible combination of two variables, without testing every possible combination of all the variables. For this example, there are 27 unique combinations of all of the selections. The following table shows the first 9 combinations. An additional 9 combinations are needed for each of the other CPU selections.

Exhaustive pair-wise testing will make sure that every unique combination of any two variables will be covered. The next table shows the combinations for this example.

With just 9 tests, we are able to exhaustively cover every unique pair of CPU and Memory, CPU and Storage, and Memory and Storage. Pair-wise testing allows us to get full coverage of the combinations of every two variables, with a minimal number of tests.

Pair-wise testing not only gives us full coverage of every pair of values, it also gives us (redundant) coverage of every single value for each control.

If we look back at our previous examples of laptop-configuration, we can calculate the numbers of tests required to get full pair-wise coverage. For the entry level laptop configurator, there are 32,256 possible unique combinations of inputs. We can test every unique combination of two variables with 31 tests. For the high-end laptop configurator, there are 2,322,432 unique combinations of inputs. We can test every unique combination of two variables with 36 tests.

N-wise Testing

The concept of pair-wise testing can be extended to N-wise testing&acirc;??looking at every combination of N possible inputs. This is a simple extension of the idea behind pairwise testing. Good developers will catch the bugs caused by the combination of two variables. Even the best developers will overlook the three-variable (or four or more variable) combinations.

The following table shows how many tests are required to get full coverage of each N-wise combination of inputs for both the low-end and high-end laptops configurators.

This is a much more manageable situation. Exhaustive coverage required us to use 2.3 million tests, where using N-wise testing with N=3, yields only 179 tests! Existing studies have consistently shown that N=3 creates on the order of 90% code coverage with test suites, although the number will vary from application to application. We will use N=3, based on practical experience that N=4 tests rarely uncover bugs that were missed with N=3.

This approach only works when users are forced to enter values in a proscribed sequence and in cases where the sequence of entry is irrelevant. This set of tests won't give us representative coverage of what the users will do when they are allowed to make selections in arbitrary but relevant order. If order doesn't matter for us (for example, most API signatures have a fixed order, and many websites will process multiple inputs in a batch), then we have our desired methodology.

Order Relevance and Statistical Testing

There's been an assumption implicit in all of our calculations so far: that the order of selection in the controls is irrelevant. The available N-wise test calculation tools do not incorporate order of selection in their permutations&acirc;??explicitly, they assume a fixed order of operations. When we test an API we have control over the order of processing&acirc;??there are a fixed number of arguments, in a fixed order. People, however, do not always interact with the controls in a fixed order. And web service architectures may not be able to depend upon a predetermined sequence of events.

With 5 controls in an interface, we have 5! (factorial) or 120 possible sequences in which selections can be made by a person. Although the user interface may incorporate dynamic filtering that prevents some subsets of out-of-sequence selection, N-wise testing is blackbox testing, and will not have access to that information.

For an interface with M possible controls, each script created by an N-wise test generator will have to be tested in M! sequences to get exhaustive coverage. If the controls are split across multiple screens, then we can reduce the number of sequences. For example, if there are 5 controls on the first screen, and five controls on the next screen, instead of considering 10! (3.6 million sequences), we can consider all first-screen-sequences in combination with all second-screen sequences (5! * 5! = 120 * 120 = 14,400 script sequences).

In our example laptop configurators, there are 11 and 13 controls (all on the same page) for the low-end and high-end laptops respectively. This would imply 11! and 13! possible sequences (40 million and 6 billion).

We do not need to do exhaustive coverage of the sequencing permutations. An N-wise test is specifically analyzing the interdependence of any combination of N controls. As a lower-bound, we would only need N! sequences for each generated script. So our 179-script suite for the high-end laptop (with N=3) would need 3! (6) * 179 = 1,074 scripts to cover the product.

Here's the table for the lower-bound of scripts required to account for different values of N for both laptop-configurators.

This is a lower bound, because it assumes a perfect efficiency in combining unique sequences of each group of N controls. Existing N-wise testing tools do not (to the author's knowledge) take order of operations into account. For N=2, this is trivial&acirc;??just duplicate the set of tests, in the exact reverse order.

We can take order of operations into account by treating the sequence as an additional input. We use the mathematical formula "X choose Y" which tells us the number of different combinations of Y values from a set of X values. The formula for calculating "X choose Y" is X!/(Y!*(X-Y)!) where X is the number of inputs and Y is the dimension of the desired N-wise test.

Here's the table of the number of combinations for each N, for both the low-end and high-end laptop configuration screens we've been discussing.

Here are the values, generally, for varying numbers of inputs.

We would then calculate the N-wise testing using a value of N+1 as an input to the test-generation tool, and include the number of unique sequences as if it were a control input.

Unfortunately, we don't have a solver capable of handling single dimensions larger than 52. This limits our ability to create a test suite for N=3 to a maximum of 7 controls.

To show the impact of sequencing on the test suite, consider an interface with 7 controls, each having 5 possible values. N=3 would require 236 tests if order is irrelevant. We then include sequence of selection as a parameter (by adding an 8th control with 35 possible values, and testing for N=4), In this case, N=3 (with sequencing) requires 8,442 scripts. Our theoretical lower bound would be 236 * 35 = 8260.

How to Make it Even Better

When we don't know anything, or don't apply any knowledge about our application to our testing strategy, we end up with far too many tests. By applying knowledge of the application to our test design we can greatly reduce the size of our test suite. Tests that incorporate knowledge of the application being tested are known as whitebox tests.

Map out the control dependencies

In our previous examples, we applied no knowledge of the interactions of controls, or the interactions within the program of having made selections in the controls. If we consider a visual map of the controls and their possible relationships, it would look like the following diagram.

There is a possibly-relevant connection between the selections in every pair of controls. We have designed our testing around the lack of knowledge that is clearly visible in the diagram.

It is likely that we can rule out some of the dependencies, but possibly not all of them. Our approach should be conservative; only remove those dependencies that we know don't exist. This knowledge comes from an understanding of the underlying application. Once we remove these links the diagram will look like this:

This clarified mapping allows us to reduce the size of our test suite dramatically, because we've identified the independence of many controls. In an ideal case, the result will be two or more completely disconnected graphs, and we can build a set of tests for our suite around each separate graph. As the diagram above shows, we do not have two completely independent graphs. We can take a testing approach as shown in the following diagram:

We've grouped all of the controls on the left in a blue box. These controls will be used with the N-wise generation tool to create a set of tests. The grouping of controls on the right will also be used to generate a set of tests.

In this example, we reduce the number of tests required by a significant amount when order matters.

Also note that we increase the number of tests required when order doesn't matter, if we have any overlapping controls (if the graphs can't be separated). When the graphs can be separated, this reduces the amount of testing even if order is irrelevant.

The key to separating the graphs is to make sure that all controls only connect to other controls within their region (including the overlapping region).

Eliminate Equivalent Values from the Inputs

When we know how the code is implemented, or have insights into the requirements, we can further reduce the scope of testing by eliminating equivalent values. Consider the following example requirements for an application:

The next table shows two variables that we are evaluating in our testing&acirc;??imagine that they are controls in a user interface (or values imported from an external system).

If we did a pairwise test suite without knowledge of the requirements, we would have 18 tests to evaluate. We get 18 tests by finding all of the unique combinations of the two controls (6 order-quantity values * 3 account status values = 18 combinations). However, with knowledge of the requirements, we can identify that some of the values are equivalent. The highlighted regions represent equivalent values (with respect to the requirements).