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Pairwise Independent Combinatorial Testing tool
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jacekcz@microsoft.com
PICT generates test cases and test configurations. With PICT, you can generate tests that are more effective than manually generated tests and in a fraction of the time required by hands-on test case design.
PICT runs as a command line tool. Prepare a model file detailing the parameters of the interface (or a set of configurations, or data) you want to test and PICT generates a compact set of parameter value choices that represent the test cases you should use to get comprehensive combinatorial coverage of your parameters.
For instance, to create a test suite for disk partition creation, the domain can be described by the following parameters: ```Partition Type```, ```Partition Size```, ```File System```, ```Format Method```, ```Cluster Size```, and ```Compression```. Each parameter consists of a finite number of possible values. For example, ```Compression``` can only be ```On``` or ```Off```, other parameters are made finite with help of equivalence partitioning:
Type: Single, Span, Stripe, Mirror, RAID-5
Size: 10, 100, 500, 1000, 5000, 10000, 40000
Format method: Quick, Slow
File system: FAT, FAT32, NTFS
Cluster size: 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536
Compression: On, Off
For such a model, thousands of possible test cases can be generated. It would be difficult to test all of them in a reasonable amount of time. Instead of attempting to cover all possible combinations, we settle on testing all possible pairs of values. For example, ```{Type:Single, File system:FAT}``` is one such value pair, ```{Size:10, Format method:Slow}``` is another. Consequently, one test case can cover many pairs. Research shows that testing all pairs is an effective alternative to exhaustive testing and is much less costly.
PICT is a command-line tool that accepts a plain-text model file as an input and produces a set of test cases.
Usage: pict model [options]
Options:
/o:N|max - Order of combinations (default: 2)
/d:C - Separator for values (default: ,)
/a:C - Separator for aliases (default: |)
/n:C - Negative value prefix (default: ~)
/e:file - File with seeding rows
/r[:N] - Randomize generation, N - seed
/c - Case-sensitive model evaluation
/s - Show model statistics
A model is a plain-text file consisting of the following sections:
parameter definitions
[sub-model definitions]
[constraint definitions]
The sections should always be specified in the order shown above. All parameters should be defined first, then sub-models, and then constraints. Parameter definitions are required, other parts are optional. Sections do not require any special separators between them. Empty lines can appear anywhere. Comments are permitted and they should be prefixed with # character.
To produce a basic model file, list the parameters—each in a separate line—with the respective values delimited by commas:
<ParamName> : <Value1>, <Value2>, <Value3>, ...
Example:
#
# This is a sample model for testing volume creation
#
Type: Primary, Logical, Single, Span, Stripe, Mirror, RAID-5
Size: 10, 100, 500, 1000, 5000, 10000, 40000
Format method: quick, slow
File system: FAT, FAT32, NTFS
Cluster size: 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536
Compression: on, off
A comma is the default separator but you can specify a different one using ```/d``` option.
By default, PICT generates a pair-wise test suite (all pairs covered), but the order can be set by option ```/o``` to a value larger than two. For example, when ```/o:3``` is specified, the test suite will cover all triplets of values. It will produce a larger number of tests but will potentially make the test suite even more effective. The maximum order for a model is equal to the number of parameters, which will result in an exhaustive test suite. Following the same principle, specifying ```/o:1``` will produce a test suite that merely covers all values (combinations of 1).
The test cases are printed to the standard output stream. The first line of the output contains names of the parameters. Each of the following lines represents one generated test case. Values in each line are separated by a tab. This way redirecting the output to a file creates a tab-separated value format.
If a model and options given to the tool do not change, every run will result in the same output. However, the output can be randomized if ```/r``` option is used. A randomized generation prints out the seed used for that particular execution to the error output stream. Consequently, that seed can be fed into the tool with ```/r:seed``` option to replay a particular generation.
All errors, warning messages, and other auxiliary information is printed to the error stream.
Constraints express inherent limitations of the modelled domain. In the example above, one of the pairs that will appear in at least one test case is ```{File system:FAT, Size:5000}```. In practice, the FAT file system cannot be applied on volumes larger than 4,096 MB. Note that you cannot simply remove those violating test cases from the result because an offending test case may cover other, possibly valid, pairs that would not otherwise be tested. Instead of losing valid pairs, it is better to eliminate disallowed combinations during the generation process. In PICT, this can be done by specifying constraints, for example:
Type: Primary, Logical, Single, Span, Stripe, Mirror, RAID-5
Size: 10, 100, 500, 1000, 5000, 10000, 40000
Format method: quick, slow
File system: FAT, FAT32, NTFS
Cluster size: 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536
Compression: on, off
IF [File system] = "FAT" THEN [Size] <= 4096;
IF [File system] = "FAT32" THEN [Size] <= 32000;
A term ```[parameter] relation value``` is an atomic part of a constraint expression. The following relations can be used: ```=```, ```<>```, ```>```, ```>=```, ```<```, ```<=```, and ```LIKE```. ```LIKE``` is a wildcard-matching string operator (```*``` - any character, ```?``` – one character).
[Size] < 10000
[Compression] = "OFF"
Operator IN allows specifying a set of values:
IF [Cluster size] IN {512, 1024, 2048} THEN [Compression] = "Off";
IF [File system] IN {"FAT", "FAT32"} THEN [Compression] = "Off";
The ```IF```, ```THEN```, and ```ELSE``` parts of an expression may contain multiple terms joined by logical operators: ```NOT```, ```AND```, and ```OR```. Parentheses are allowed in order to change the default operator priority:
IF [File system] <> "NTFS" OR
( [File system] = "NTFS" AND [Cluster size] > 4096 )
THEN [Compression] = "Off";
IF NOT ( [File system] = "NTFS" OR
( [File system] = "NTFS" AND NOT [Cluster size] <= 4096 ))
THEN [Compression] = "Off";
Parameters can be compared to other parameters, like in this example:
OS_1: Win7, Win8, Win10
SKU_1: Home, Pro
LANG_1: English, Spanish, Chinese
OS_2: Win7, Win8, Win10
SKU_2: Home, Pro
LANG_2: English, Spanish, Chinese, Hindi
IF [LANG_1] = [LANG_2]
THEN [OS_1] <> [OS_2] AND [SKU_1] <> [SKU_2];
An invariant declares an always valid limitation of a domain:
#
# At least one parameter must be different to be a meaningful test case
#
[OS_1] <> [OS_2] OR [SKU_1] <> [SKU_2] OR [LANG_1] <> [LANG_2];
#
# All parameters must be different (we use AND operator)
#
[OS_1] <> [OS_2] AND [SKU_1] <> [SKU_2] AND [LANG_1] <> [LANG_2];
PICT has a simple type system. There are two types of parameters: a string and a numeric type. Types do not have to be expicitely declared. A parameter is considered numeric when all of its values can be converted to a number (an integer or a float).
Types are only important when evaluating constraints. A numeric parameter is only comparable to a number, and a string parameter is only comparable to another string. For example:
Size: 1, 2, 3, 4, 5
Value: a, b, c, d
IF [Size] > 3 THEN [Value] > "b";
If a value has multiple names, only the first is considered when PICT detects the type.
By default, PICT does all its comparisons and checks case-insensitively. For instance, if there are two parameters defined: ```OS``` and ```os```, a duplication of names will be detected (parameter names must be unique). Constraints are also resolved case-insensitively by default:
IF [OS] = "Win10" THEN ...
will match both ```Win10``` and ```win10``` values (values of a parameter are not required to be unique). Option ```/c``` however, makes the model evaluation fully case-sensitive.
Once a parameter is defined, it can help in defining other parameters.
#
# Machine 1
#
OS_1: Win7, Win8, Win10
SKU_1: Home, Pro
LANG_1: English, Spanish, Chinese
#
# Machine 2
#
OS_2: <OS_1>
SKU_2: <SKU_1>
LANG_2: <LANG_1>, Hindi
Less typing and better for maintainability.
Sub-models allow the bundling of certain parameters into groups that get their own combinatory orders. This can be useful if combinations of certain parameters need to be tested more thoroughly or must be combined in separation from the other parameters in the model. The sub-model definition has the following format:
{ <ParamName1>, <ParamName2>, <ParamName3>, ... } @ <Order>
For example, sub-modeling is useful when hardware and software parameters are combined together. Without sub-models, each test case would produce a new, unique hardware configuration. Placing all hardware parameters into one sub-model produces fewer distinct hardware configurations and potentially lowers the cost of testing. The order of combinations that can be assigned to each sub-model allows for additional flexibility.
PLATFORM: x86, x64, arm
CPUS: 1, 2, 4
RAM: 1GB, 4GB, 64GB
OS: Win7, Win8, Win10
Browser: Edge, Opera, Chrome, Firefox
APP: Word, Excel, Powerpoint
The test generation for the above model would proceed as follows:
$
|
| order = 2 (defined by /o)
|
+------------------------------+-----------------------------+
| | |
| order = 3 | order = 2 |
| | |
{ PLATFORM, CPUS, RAM, HDD } { OS, Browser } APP
1. You can define as many sub-models as you want; any parameter can belong to any number of sub-models. However, the model hierarchy can be just one level deep.
2. The combinatory order of a sub-model cannot exceed the number of its parameters. In the example above, an order of the first sub-model can be any value between one and four.
3. If you do not specify the order for a sub-model, the order specified by ```/o``` option will be used.
Aliasing is a way of specifying multiple names for a single value. Multiple names do not change the combinatorial complexity of the model. No matter how many names a value has, they are treated as one entity. The only difference will be in the output; any test case that would normally have that one value will have one of its names instead. Names are rotated among the test cases.
By default, names should be separated by | character but this can be changed with option ```/a```.
OS_1: Win2008, Win2012, Win2016
SKU_1: Professional, Server | Datacenter
1. When evaluating constraints, only the first name counts. For instance, ```[SKU_1] = "Server"``` will result in a match but ```[SKU_1] = "Datacenter"``` will not. Also, only the first name is used to determine whether a value is "negative" or for the purpose of detecting its type.
In addition to testing valid combinations, referred to as “positive testing,” it is often desirable to test using values outside the allowable range to make sure the program handles errors properly. This “negative testing” should be conducted such that only one out-of-range value is present in any test case. This is because of the way in which typical applications are written: namely, they often error out when the first error is detected.
"Input masking" is a type of problem in test design, in which one out-of-range input prevents another invalid input from being tested.
Consider the following routine, which takes two arguments:
float SumSquareRoots( float a, float b )
{
if ( a < 0 ) throw error; // [1]
if ( b < 0 ) throw error; // [2]
return ( sqrt( a ) + sqrt( b ));
};
Although the routine can be called with any numbers for ```a``` or ```b```, it only makes sense to do the calculation on non-negative numbers. For that reason by the way, the routine does verifications [1] and [2] on the arguments. Now, assume a test ```( a: -1, b: -1 )``` was used to test that negative case. Here, ```a: -1``` "masks" ```b: -1``` because the check [2] never gets executed. If it did not exist at all, that problem in code would go unnoticed.
To prevent input masking issues, it is important that two out-of-range values do not appear in the same test case. Prefixing a value with ```~``` (tilde) marks it invalid or out-of-allowable-range. Option ```/n``` allows for specifying a different prefix.
#
A: ~-1, 0, 1, 2
B: ~-1, 0, 1, 2
PICT guarantees that all possible pairs of valid (or in-range) values will be covered and all possible combinations of any out-of-range / invalid value will be paired only with all valid values.
A B
2 0
2 2
0 0
0 1
1 0
2 1
0 2
1 2
1 1
~-1 2
1 ~-1
0 ~-1
~-1 1
2 ~-1
~-1 0
Notes:
1. The ```~``` prefix is not a part of a value when it comes to type detection or constraint evaluation. A valid constraint for the above example would look like this: ```if [A] = -1 then [B] = 0;```. The ```~``` prefix however will show up in the output.
2. If a value has multiple names, only prefixing the first name will make the value out-of-range.
## Excluding Entire Parameters
Some test suites may require that under centrain conditions entire parameters should be ignored. Negative testing could be such a scenario. There, an error or a failure may stop the application under test and make any other parameter choices meaningless. By design, PICT will try to pack as many combinations into fewest test cases possible, including those that contain values that will trigger failures. In these cases, we will see testable pairs esentially wasted.
PICT does not have any special handling for this scenario however one technique that could be useful is to add a dummy value to the parameters should be ignored.
Consider the following example where a ```P1: -1``` is a value that triggers an application error.
P1: -1, 0, 1
P2: A, B, C
P3: X, Y, Z
We want to test this condition independently and not associate any values of ```P2``` or ```P3``` with it. To accomplish that, we add a dummy value ```NA``` to parameters ```P2``` and ```P3```. That dummy value will indicate that the parameter should be ignored. We can then use constraints to make sure tests are generated correctly:
P1: -1, 0, 1
P2: A, B, C, NA
P3: X, Y, Z, NA
IF [P1] = -1
THEN [P2] = "NA" AND [P3] = "NA"
ELSE [P2] <> "NA" AND [P3] <> "NA";
This will result in a single test for ```P1: -1``` with ```NA``` set for ```P2``` and ```P3```. No other combinations values of ```P2``` and ```P3``` are used up for the test containing ```P1: -1```:
P1 P2 P3
0 C Z
1 B Z
1 C Y
0 A X
1 C X
0 B X
0 A Y
-1 NA NA
0 B Y
1 A Z
The downside of this method is that constraints will get progressively more complex. For example, when additional failure inducing values are added to the model e.g. ```P2: Null```:
P1: -1, 0, 1
P2: A, B, C, Null, NA
P3: X, Y, Z, NA
IF [P1] = -1
THEN [P2] = "NA" AND [P3] = "NA"
ELSE [P2] <> "NA" AND
( [P3] <> "NA" OR [P2] = "Null" );
IF [P2] = "Null" OR [P2] = "NA"
THEN [P3] = "NA";
Two additional tests will be added (not all output shown):
P1 P2 P3
0 Null NA
1 Null NA
Weights tell the generator to prefer certain parameter values over others. Weights are positive integers. When not explicitly specified, values have a weight of 1:
#
# Let’s focus on primary partitions formatted with NTFS
#
Type: Primary (10), Logical, Single, Span, Stripe, Mirror, RAID-5
Format method: quick, slow
File system: FAT, FAT32, NTFS (10)
Weights are not guarantees but merely opportunistic hints. You cannot be sure that the weights you specified will be honored at all.
PICT's main objective is to cover all combinations of values in the smallest number of test cases. Only when value choices do not affect that coverage criterion, weights will be used to determine the final result.
For that reason, weight values have no intuitive meaning. For example, when a parameter is defined like this:
It does not mean that NTFS will appear in the output ten times more often than FAT or FAT32. It will merely be 10 times more likely to be chosen when the choice does not affect the coverage.
Seeding makes two scenarios possible:
1. Allows for specifying important e.g. regression-inducing combinations that should end up in any generated test suite. In this case, PICT will initialize the output with combinations you provide and will build up the rest of the suite on top of those.
2. Minimize changes to the output when your model is modified. You can feed PICT the results of a previous test generation and expect the tool to reuse the old test cases as much as possible.
Seeding rows must be defined in a separate file. Use option ```/e``` to specify the seeding file's location:
pict.exe model.txt /e:seedrows.txt
Seeding files have the same format as any PICT output. The first line contains parameter names separated by tab characters and each following line contains one test case with values also separated by tabs. This format can easily be prepared manually or prior output can be directly fed back to PICT:
Win7 Pro EN x86
Win7 FR x86
Win10 Pro EN x64
A seeding row may be complete i.e. have values specified for all parameters. It can also be partial, e.g. the second seeding row in the example above does not have a value for the ```SKU``` parameter. In this case, the generation process will take care of choosing the best value for ```SKU```.
There are a few rules of matching seeding rows with the current model:
1. If a seeding file contains a parameter that is not in the current model, the entire column representing the parameter in the seeding file will be ignored. Parameters are matched by name.
2. If a seeding row contains a value that is not in the current model, the value will be ignored. The rest of the row however, if valid, will still be used. As a result of that, a complete row may become a partial row.
3. If a seeding row violates any of the current constraints, it will be skipped entirely.
PICT will issue warnings if (1) or (2) occurs.
In addition, there are a few things that are normally allowed in PICT models but may lead to ambiguities when seeding is used:
1. Blank parameter and value names.
2. Parameter and value names containing tab characters.
3. Values and all their aliases not unique within a parameter.
These are best to avoid in any case. When seeding is used, you will be warned if any of the above problems are detected in your model.
# Constraints Grammar
Constraints :: =
Constraint
| Constraint Constraints
Constraint :: =
IF Predicate THEN Predicate ELSE Predicate;
| Predicate;
Predicate :: =
Clause
| Clause LogicalOperator Predicate
Clause :: =
Term
| ( Predicate )
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Term :: =
ParameterName Relation Value
| ParameterName LIKE PatternString
| ParameterName IN { ValueSet }
| ParameterName Relation ParameterName
ValueSet :: =
Value
| Value, ValueSet
LogicalOperator ::=
AND
| OR
Relation :: =
=
| <>
| >
| >=
| <
| <=
ParameterName ::= [String]
Value :: =
"String"
| Number
String :: = whatever is typically regarded as a string of characters
Number :: = whatever is typically regarded as a number
PatternString ::= string with embedded special characters (wildcards):
* a series of characters of any length (can be zero)
? any one character