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Common Patterns in Combinatorial Models
Itai Segall, Rachel Tzoref-Brill, Aviad Zlotnick
IBM Haifa Research Labs
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Agenda
• Introduction & Motivation
• Pitfalls in Combinatorial Models
• Modeling Patterns
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Introduction
• Combinatorial model = attributes, values, restrictions
• Correctly identifying them is of the main obstacles in practicing combinatorial testing
• There are common pitfalls, and common patterns that address them
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Motivation
• Construct a “checklist” for practitioners– Common pitfalls and common solutions
• Raise the topic for community discussion– This is not a complete list– More patterns are welcome
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Common Pitfalls in Combinatorial Modeling
Correctness
Failing to capture the intention correctly
Completeness
Omitting an important part of the test space from the model
Redundancy
Explicitly enumerating different cases that are actually equivalent
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Modeling Patterns
• Recurring patterns we encountered in combinatorial models– i.e., recurring properties of the modeled test
spaces
• Address the pitfalls
• Encountered in many different models, regardless of the domain, current level of testing, etc.
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Overview
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Optional and Conditionally-Excluded Values (completeness, correctness)
• Example I: online form with fields email (mandatory) and home address (optional)
• Naïve model:– Email – valid / invalid– HomeAddr – valid / invalid
• Much better:– Email – valid / invalid– HomeAddr – valid / empty / invalid
Incomplete !
Does not distinguish between empty and
invalid home address
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Optional and Conditionally-Excluded Values – cont.• Example II: online form with email
address, home address, and “is billing by email”
• Home address used only if not billing by email
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• Naïve model: – Email – valid / invalid– HomeAddress – valid / invalid– BillByEmail – true / false
– Not allowed:• HomeAddress == “valid” && BillByEmail• HomeAddress == “invalid” && BillByEmail
Incorrect !
Billing by email was entirely excluded
Optional and Conditionally-Excluded Values – cont.
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Optional and Conditionally-Excluded Values – cont.
• Much Better:– Email – valid / invalid– HomeAddr – valid / invalid / empty / NA– BillByEmail – true / false
– Not allowed:• HomeAddr != “NA” && BillByEmail• HomeAddr == “NA” && ! BillByEmail
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Multi-Selection (correctness, completeness)
• Example: shopping cart system. The shopping cart may contain meat, dairy, fish and drinks
• Naïve model:– Cart Item – meat / dairy / fish / drinks
Incomplete and Incorrect !
Does not allow for the cart to contain more
than one item, and does not test interactions
between items
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Multi-Selection – cont.
• A better solution:– Item 1 – meat / dairy / fish / drinks
…– Item t – meat / dairy / fish / drinks / none
• An even better solution:– Meat – true / false– Dairy – true / false– Fish – true / false– Drinks – true / false
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Ranges and Boundaries (redundancy, completeness)
• Applied when values of a parameter can be divided into ranges, such that values in a range are equivalent with respect to the testing scenario
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Ranges and Boundaries – cont.
• Example I: file processing. File of up to 1 MB is handled by a first approach. File of 1-256 MB by a second, and over 256 by a third
• Model:– FileSize: 0 / moreThan0LessThan1 /
1 / moreThan1LessThan256 / 256 / moreThan256
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Ranges and Boundaries – cont.
• Example II: user updates insurance plan. User chooses benefits for the new plan, some may already exist.
• Model:– Benefits – allBenefitsExisting /
someExistingSomeNew / allBenefitsNew
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Order and Padding (completeness)
• When? When a requirement defines an output that depends on two (or more) inputs.
• Modeler Q: Can bugs surface if inputs are presented in different orders ?
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Order and Padding – cont.
• Example: expense reimbursement. Expenses covered only if both food and lodging expenses are submitted
• Naïve model:– Lodging – true / false– Food – true / false
Incomplete !
The following bug may not be detected
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Order and Padding – cont.
if (record.type == “Food” && pLodgingWasSubmitted){ process(record); }
if (record.type == “Lodgin” && pFoodWasSubmitted){ process(record); }
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Order and Padding – cont.
• Better model:– Lodging – true / false– Food – true / false– FoodBeforeLodging – true / false /
NotApplicable
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Order and Padding – cont.
• Modeler Q II: may the implementation depend on the records being consecutive?
• Modeler Q III: may the implementation depend on either records being first / last?
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Order and Padding – cont.
• Updated model:– Lodging – true / false– Food – true / false– FoodBeforeLodging – true / false /
NotApplicable– PaddingBefore – true / false / NotApplicable– PaddingBetween – true / false / NotApplicable– PaddingAfter – true / false / NotApplicable
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Multiplicity and Symmetry (redundancy)
• Applicable when:– Multiple elements of the same type– Their interactions with other elements are
equivalent
• Example: two hosts (x86 / PowerPC), two storage devices (Enterprise / Mid-Range / Low-End)
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Multiplicity and Symmetry – cont.
• Naïve Model:– Host1 – x86 / PowerPC– Host2 – x86 / PowerPC– Storage1 – Enterprise / MidRange / LowEnd– Storage2 – Enterprise / MidRange / LowEnd
Warning: Possible Redundancy !
For example, (host1=x86, Storage1=LowEnd)
may be equivalent to (host2=x86, Storage1=LowEnd)Warning: Possible Redundancy ! For example, (host1=x86, host2=PowerPC)
may be equivalent to (host2=x86, host1=x86)
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Multiplicity and Symmetry – cont.
• Possible Solution:– numX86Running – 0 / 1 / 2– numPowerPCRunning – 0 / 1 / 2– numEnterpriseRunning – 0 / 1 / 2– numMidRangeRunning – 0 / 1 / 2– numLowEndRunning – 0 / 1 / 2– Not allowed:
• numX86Running + numPowerPCRunning != 2• numEnterpriseRunning + numMidRangeRunning +
numLowEndRunning != 2
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Multiplicity and Symmetry – cont.
• An alternative approach:
• A tool and algorithm that supports designating equivalent parameters– And whether internal interactions are
equivalent
• Requires a tool that supports dynamically changing coverage requirements.
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Summary
• Modeling is typically the most challenging part in applying combinatorial testing
• We listed some pitfalls, and common patterns that address them
• There are probably many more
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Thank You For Listening
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