Software Testing - Boundary Value Analysis, Equivalent Class Partition, Decision Table

These slides will be useful for understanding BVA, ECP, DT concepts in Software Testing

1. SOFTWARE TESTING
BLACK BOX TESTING
Prepared by
Ms. S. Shanmuga Priya
Senior Assistant Professor
Department of Computer Science and Engineering
New Horizon College of Engineering
Bangalore
Karnataka
India

2. BLACK BOX TESTING
BOUNDARY VALUE TESTING
EQUIVALENCE CLASS TESTING
DECISION –TABLE BASED TESTING

3. CONCEPTS DISCUSSED

4. UNIT TESTING
•Inaproceduralprogramminglanguage,aunitcanbe
–Asingleprocedure
–Afunction
–Abodyofcodethatimplementsasinglefunction
–Sourcecodethatfitsononepage
–Abodyofcodethatrepresentsworkdonein4to40hours
(asinaworkbreakdownstructure)
–Thesmallestbodyofcodethatcanbecompiledand
executedbyitself
•InObjectOrientedProgramming,aunitcanbe
-Methodsofaclassmightbelimitedbyanyofthe
“definitions”ofaunit
•Bottomlineisthat“unit”isprobablybestdefinedby
organizationsimplementingcode

5. Black Box Test Design Techniques
Types of Black Box Testing Techniques
1)Boundary Value Analysis
2) Equivalence Classes Partitioning
3)Decision Table Testing

6. BOUNDARY VALUE ANALYSIS

7. BOUNDARY VALUE ANALYSIS (BVA)
Definition
•BoundaryValueAnalysisisablackboxtest
designtechniquewheretestcasearedesignedby
usingboundaryvalues
•Boundaryvalueanalysis(BVA)isbasedon
testingattheboundariesbetweenpartitions
•Mosterrorsoccuratextremes(<insteadof<=,
counteroffbyone)

8. •Herewehaveboth:
–validboundaries(inthevalidpartitions)and
–invalidboundaries(intheinvalidpartitions)
•BVAisusedinrangechecking
•Mainlyusefulforlooping

9. •WhenafunctionFisimplementedasa
program,theinputvariablesx
1andx
2will
havesomeboundaries:
a<=x
1<=b
c<=x
2<=d
where
-[a,b]and[c,d]aretheintervals
BVAfocusesontheboundaryoftheinput
spacetoidentifytestcases

10. EXAMPLE 1 FOR BVA
•SupposeyouhavetochecktheUserNameandPasswordfieldthataccepts
–minimum8charactersand
–maximum12characters
–Validrange8-12
–Invalidrange7orlessthan7
–Invalidrange13ormorethan13
•TestCasesforValidpartitionvalue,Invalidpartitionvalueandexact
boundaryvalue
•TestCases1:Considerpasswordlengthlessthan8.
•TestCases2:Considerpasswordoflengthexactly8.
•TestCases3:Considerpasswordoflengthbetween9and11.
•TestCases4:Considerpasswordoflengthexactly12.
•TestCases5:Considerpasswordoflengthmorethan12.

11. EXAMPLE 2 FOR BVA
•Testcasesfortheapplicationwhoseinputboxacceptsnumbers
between1-1000
-Validrange1-1000
-Invalidrange0and
-Invalidrange1001ormore
•TestCasesforValidpartitionvalue,Invalidpartitionvalueand
exactboundaryvalue.
•TestCases1:Considertestdataexactlyastheinputboundariesof
inputdomaini.e.values1and1000
•TestCases2:Considertestdatawithvaluesjustbelowtheextreme
edgesofinputdomainsi.e.values0and999
•TestCases3:Considertestdatawithvaluesjustabovetheextreme
edgesofinputdomaini.e.values2and1001

12. •SINGLEFAULTASSUMPTION
Assumesthe‘singlefault’or“independenceof
inputvariables.”
–Failuresarerarelytheresultofthesimultaneous
occurrenceoftwo(ormore)faults
–e.g.Ifthereare2inputvariables,theseinput
variablesareindependentofeachother.
•MULTIPLEFAULTASSUMPTION
“dependenceamongtheinputs”

13. VARIANTS OF BVA
•Fourvariationsofboundaryvaluetesting:
–Normalboundaryvaluetesting
–Robustboundaryvaluetesting
–Worst-caseboundaryvaluetesting
–Robustworst-caseboundaryvaluetesting

14. NORMAL BOUNDARY VALUE
TESTING

15. •BasicideaofBVAistouseinputvariablevalues
attheir:blueshadedregion–inputdomain
space
-Minimum(min)
-AboveMinimum(min+)
-NominalValue(nom)(AverageValue)
-BelowMaximum(max-)
-Maximum(max)
VALUE SELECTION IN BVA
a<=x
1<=b
c<=x
2<=d

16. X1,X2Variables
DotsRepresentsatestvalueat
whichtheprogramistobetested
INPUT BOUNDARY VALUE TEST CASES
(2 VARIABLES)

17. TEST CASES (BY NUMBER OF VARIABLES)
•<X
1nom,X
2min>,<10,25>
•<X
1nom,X
2min+>,<10,26>
•<X
1nom,X
2nom>,<10,30>
•<X
1nom,X
2max>,<10,35>
•<X
1nom,X
2max->,<10,34>
•<X
1min,X
2nom>,<5,30>
•<X
1min+,X
2nom>,<6,30>
•<X
1nom,X
2nom>,<10,30>
•<X
1max,X
2nom>,<15,30>
•<X
1max+,X
2nom>,<14,30>
INPUTS
5<=X
1<=15
25<=X
2<=35

18. GENERALIZING BVA
•BVA can be generalized in two ways:
–By the number of variables (4n+1) test cases
for n variables
•Where,
–4 Values (min, min+, max, max-)
–n, number of variables
–1, nor (average value)
•By the kinds of ranges of variables
–Programming language dependent
–Logical variables
–Bounded (upper or lower bounds clearly defined)
–Unbounded (no upper or lower bounds clearly defined)

19. EXAMPLES
TRIANGLE PROBLEM BVA TEST CASES
CONDITIONS
c1: 1<=a<=200
c2: 1<=b<=200
c3: 1<=c<=200
Min = 1
Min+ = 2
Nom = 100
Max-= 199
Max = 200
Min
Min+
Nom
Max-
Max

20. TEST CASE FOR NEXT DATE FUNCTION
Min
Min+
Nom
Max-
Max

21. LIMITATIONS OF BVA

22. ROBUSTNESS TESTING
•Robustnesscanalsobedefinedastheabilityofanalgorithmto
continueoperatingdespiteabnormalitiesininput,calculations,etc.
•Robustnesstestingisasimpleextensionofboundaryvalueanalysis
•Inadditiontothe5BVAvaluesofvariablesincludes,addvaluesslightly
greaterthanthemaximum(max+)andavalueslightlylessthanthe
minimum(min-)
–Min–1
–Min
–Min+1
–Nom
–Max-1
–Max
–Max+1
•Mainvalueofrobustnesstestingistoforceattentiononexception
handling
•Insomestronglytypedlanguagesvaluesbeyondthepredefinedrangewill
causearun-timeerror
Number of TC = 6n+1
6 Values
n Number of Variables
1Nom (Average Value)

23. ROBUSTNESS TEST CASES FOR A FUNCTION
OF TWO VARIABLES
Dotsthatareoutsidetherange
[a,b]ofvariablex1.
Similarly,forvariablex2,wehave
crosseditslegitimateboundaryof
[c,d]ofvariablex2.

24. WORST –CASE TESTING

26. Min
Min+
Nom
Max-
Max

27. Min
Min+
Nom
Max-
Max

28. ROBUST WORST-CASE TEST CASE
Involves 7
n
test cases

29. SPECIAL VALUE TESTING
•Mostwidelypracticedformoffunctionaltesting
•Testeruses(todevisetestcases):
–his/herdomainknowledge,
–experiencewithsimilarprograms,and
–informationabout“softspots”
•Dependentontheabilitiesofthetester!!!
•TestcasesinvolvingFeb28,29andleapyearsaremost
likelytobeidentifiedastestcasesusingspecialvalue
testing
•Robustnesstestingandworstcasetestingdonot
identifyFeb28asatestcase
•AlsoknownasAd-hocTesting

30. TEST CASE FOR COMMISSION PROBLEM

31. EXAMPLE TEST CASE USING OUTPUT
RANGE VALUE

32. LOCK: $45 STOCK: $30 BARREL: $25
CONSTRAINT(s)
1.Sales person should sell at least
one complete rifle per month
2.Production limit/month:
LOCK: 70, STOCK: 80, BARREL: 90

33. TEST CASE 9 IS THE $1000
BORDER POINT

34. 1
2
3
4
5
6
7
8

35. OUTPUT SPECIAL VALUE TEST CASES

36. RANDOM TESTING
•Basicideaisthat,ratherthanalwayschoosethe
min,min+,nom,max-,andmaxvaluesofa
boundedvariable,usearandomnumber
generatortopicktestcasevalues
•InVisualBasicapplication,topickvaluesfora
boundedvariablea<=x<=b,uses
x=Int((b-a+1)*Rnd+a)
where,
IntReturnstheintegerpartoffloating
pointnumber
RndFunctiongeneratesrandomnumbers
intheinterval[0,1]

37. HOW MANY TEST CASES DO WE MAKE?
•Relatedtotheprobabilityofproducingevery
outcomeatleastonce
•Relatedtotheprobabilityofexecutingevery
statement/pathatleastonce

41. EQUIVALENCE CLASS TESTING

42. •BoundaryValueTestingderivestestcaseswith
Seriousgaps
Massiveredundancy
•Motivationsforequivalenceclasstestingare
Completetesting
Avoidredundancy
CONS OF BVA

43. •BlackBoxTechnique
•Utilizesasubsetofdatawhichisrepresentativeofalarger
class
•Equivalencepartitioninginvolvespartitioningoftheinput
domainofasoftwaresystemintoseveralequivalentclasses
inamannerthatthetestingofoneparticularrepresentative
fromaclassisequivalenttothetestingofsomedifferentvalue
fromthesubjectclass
•Applicability
–Programisafunctionfrominputtooutput
–Inputand/oroutputvariableshavewelldefinedintervals

44. AIRLINE RESERVATION SYSTEM

45. Any number
above or
below 1 and
10 is invalid
1...10 are
valid

46. •Hereisthetestcondition
•Number>10enteredinthereservationcolumn(letsay11)isconsidered
invalid
•Number<1thatis0orbelow,thenitisconsideredinvalid
•Numbers1to10areconsideredvalid
•Any3DigitNumbersay-100isinvalid
•Wecannottestallthepossiblevaluesbecauseifdone,numberoftestcases
willbemorethan100
•Toaddressthisproblem,weuseequivalencepartitioninghypothesiswhere
wedividethepossiblevaluesofticketsintogroupsorsetsasshownbelow
wherethesystembehaviorcanbeconsideredthesame

47. The divided sets are called Equivalence Partitions or Equivalence Classes. Then we
pick only one value from each partition for testing. The hypothesis behind this
technique isthat if one condition/value in a partition passes all others will also pass.
Likewise, if one condition in a partition fails, all other conditions in that partition will
fail.

48. EQUIVALENCE CLASS TEST CASE
•Consideranumericalinputvariable,i,whosevalues
mayrangefrom-200through+200.Thenapossible
partitioningoftestinginputvariable,i,by4
partitionsmaybe:
–-200to-100
–-101to0
–1to100
–101to200
•Define“samesign”astheequivalencerelation,R,
definedovertheinputvariable’svalueset,i={-200-
-,0,--,+200}.Thenonepartitioningwillbe:
–-200to-1(negativesign)
–0 (nosign)
–1to200(positivesign)

49. DIFFERENCE BETWEEN BVA AND
EQUIVALENCE PARTITIONING

50. TRADITIONAL EQUIVALENCE CLASS TESTING
•NearlyidenticaltoWeak
Robust Equivalence
ClassTesting
•FocusesonInvaliddata
values

51. •Theideaofequivalenceclasstestingistoidentifytestcasesby
usingoneelementfromeachequivalenceclass
•Iftheequivalenceclassesarechosenwisely,thepotential
redundancyamongtestcasescanbereduced
•[ClosedIntervalIncludesend-points
•)OpenIntervalDoesnotincludeend-points
Example (0,1) means greater than 0 and less than 1
IMPROVED EQUIVALENCE CLASS TESTING

52. •Invalid values of x1 and x2 are
–x1 <a, x1> d, and x2 <e, x2> g.
•The equivalence classes of valid values are
V1 = {x1: a ≤ x1 < b},
V2 = {x1: b ≤ x1 < c},
V3 = {x1: c ≤ x1 ≤ d},
V4 = {x2: e ≤ x2 < f },
V5 = {x2: f ≤ x2 ≤ g}
•The equivalence classes of invalid values are
NV1 = {x1: x1 < a},
NV2 = {x1: d < x1},
NV3 = {x2: x2 < e},
NV4 = {x2: g < x2}

53. TYPES OF EQUIVALENCE CLASS TESTING
1)WeakNormalEquivalenceClassTesting
2)StrongNormalEquivalenceClassTesting
3)WeakRobustEquivalenceClassTesting
4)StrongRobustEquivalenceClassTesting

55. WEAK NORMAL EQUIVALENCE CLASS
TESTING
•WeakSingleFaultAssumption(One
fromeachclass)
•NormalClassesofValidValuesofInput
(IdentifyClasses)
•Choosethetestcasefromeachofthe
equivalenceclassesforeachinputvariable
independentlyoftheotherinputvariable
•Testcaseshaveallvalidvalues
•Detectsfaultsduetocalculationswithvalid
valuesofsinglevariable

56. DOTIndicatesaValid
Value(TestData)
Validvalueonefromeach
class

57. Example
Assume the equivalence partitioning of inputX2 is: 1 to 10; 11 to 20, 21 to 30
and the equivalence partitioning of input X1is: 1 to 5; 6 to 10; 11 to 15; and 16 to 20
NUMBER OF TEST CASES = #classes in the partition with the largest numbering of
subsets.
X2
X1
1
10
20
30
1 5 10 15 20
Wehavecoveredeveryoneof
the3equivalenceclassesfor
inputX.
For ( X2, X1 )
we have:
(24,2)
(15, 8)
( 4,13)
(23,17)
PROBLEM: May miss some equivalence classes

58. STRONG NORMAL EQUIVALENCE CLASS TESTING
•StrongMultipleFaultAssumption(One
fromeachclassinCartesian
Product)
•NormalIdentifyequivalenceclassesofValid
Inputs
•TestcasesfromCartesianProductofvalid
values
•CartesianProductguaranteesnotionof
completeness
•Detectsfaultsduetointeractionwithvalidvalues
ofanynumberofvariables

59. DotsValid
valuesbutwithall
possibilities
COVERS ONE
POSSIBLE
COMBINATION
FROM EACH
PARTITION

60. Example of : Strong Normal Equivalence testing
Assume the equivalence partitioning of input X2is: 1 to 10; 11 to 2; 21 to 30
and the equivalence partitioning of input X1is: 1 to 5; 6 to 10; 11 to 15; and 16 to 20
NUMBER OF TEST CASES: Based on Cartesian product of the partition subsets
X2
X1
1
10
20
30
1 5 10 15 20
Wehavecoveredeveryoneof
the3x4Cartesianproductof
equivalenceclasses
PROBLEM: Doesn’t test values outside of intervals

61. WEAK ROBUST EQUIVALENCE CLASS
TESTING
•WeakSingleFaultAssumption(Onefromeach
class)
•RobustClassesofvalidandinvalidvaluesof
input
•Uptonowwehaveonlyconsideredpartitioningthe
validinputspace
•“Weakrobust”issimilarto“weaknormal”
equivalencetestexceptthattheinvalidinputvariables
arenowconsidered

63. Example of : Weak RobustEquivalence testing
Assume the equivalence partitioning of input X2 is 1 to 10; 11 to 20, 21 to 30
and the equivalence partitioning of input X1is 1 to 5; 6 to 10; 11 to 15; and 16 to 20
X2
X1
1
10
20
30
1 5 10 15 20
Wehavecovered
everyoneofthe5
equivalenceclassesfor
inputX.
PROBLEM: Misses some equivalence classes

64. PROBLEMS WITH ROBUST EQUIVALENCE TESTING
a)Veryoftenthespecificationdoesnotdefine
whattheexpectedoutputforaninvalidtest
caseshouldbe
b)Thus,testersspendalotoftimedefining
expectedoutputsforthesecases
c)StronglytypedlanguageslikePascal,Ada,
eliminatetheneedfortheconsiderationof
invalidinputs

65. STRONG ROBUST EQUIVALENCE CLASS TESTING
•StrongMultipleFaultAssumption(One
fromeachclassinCartesian
Product)
•RobustClassesofvalidandinvalidvalues
ofinput
•Obtainthetestcasesfromeachelementofthe
Cartesianproductofalltheequivalence
classes

67. Example of : Strong Robust Equivalence testing
Assume the equivalence partitioning of input X is: 1 to 10; 11 to 20, 21 to 30
and the equivalence partitioning of input Y is: 1 to 5; 6 to 10; 11 to 15; and 16 to 20
X
Y
1
10
20
30
1 5 10 15 20
Wehavecoveredeveryoneof
the5x6Cartesianproductof
equivalenceclasses(including
invalidinputs)
Problem: None, but results in lots of test cases (expensive)

68. SAMPLE QUESTIONS

69. EQUIVALENCE CLASS TEST
CASES FOR THE TRIANGLE
PROBLEM

70. •Fourpossibleoutputs:
–NotATriangle
–ScaleneTriangle
–IsoscelesTriangle
–EquilateralTriangle
•Output(range)equivalenceclasses:
INPUT OUTPUT

71. WEAK NORMAL EQUIVALENCE CLASS TEST CASE
Four weak normal equivalence class test cases,
chosen arbitrarily from each class

72. STRONG NORMAL EQUIVALENCE CLASS TEST CASE
•Becausenovalidsubintervalsofvariablesa,
b,andcexist,thestrongnormalequivalence
classtestcasesareidenticaltotheweak
normalequivalenceclasstestcases.

73. WEAK ROBUST TEST CASES
The invalid
valuescouldbe
-Zero(0)
-AnyNegative
Number(-ve)
-Anynumber
>200
Considering, the invalid values for a, b, and c
yield the above additional
weak robust equivalence class test cases

74. STRONG ROBUST TEST CASE

75. TRIANGLE INPUT EQUIVALENCE CLASSES
Forexample,triplet<1,4,1>hasexactlyonepairof
equalsides,butthosesidesdonotformatriangle

76. Ifwewanttobeevenmorethorough,wecould
separatethe“greaterthanorequalto”totwo
distinctcasesas:
D6willbecome:
D6’={<a,b,c>:a=b+c}
D6”={<a,b,c>:a>b+c}
Similarly,forD7andD8

77. EQUIVALENCE CLASS TEST CASES FOR THE
NEXTDATE FUNCTION
•Intervals of valid values defined as follows:
M1= {month : 1 <= month <=12}
D1 = {day : 1<= day <= 31}
Y1 = {year : 1812 <= year <= 2012}

79. WEAK NORMAL EQUIVALENCE CLASS

80. WEAK ROBUST TEST CASES

81. STRONG ROBUST TEST CASES

83. EQUIVALENCE CLASS TEST CASES FOR THE
NEXTDATE FUNCTION
•Variables:
–Month
–Day
–Year
•Equivalence Classes for Months M=M
1UM
2UM
3
–30 days months (M1)
–31 days months (M2)
–February (M3)

84. •Equivalence Classes for YearsY=Y
1UY
2UY
3UY
4
–Non-Century Common Years (Y
1)
–Non-Century Leap Years (Y
2)
–Common Century Years (Y
3)
–Leap Century Years (Y
4)
•Equivalence Classes for DaysD= D
1UD
2UD
3…
–D
1=1…?
–D
2=?
MAY GIVE A SOLUTION, BUT
THE CLASSES MAY BE LARGE

85. 31 --> Jan(1), March(3), May(5), July(7),
August(8), Oct(10), Dec(12)
30 --> April(4), June(6), Sept(9), Nov(11)

87. NUMBEROFTESTCASES=#classesinthe
partitionwiththelargestnumberingof
subsets.
WEAK NORMAL EQUIVALENCE CLASS

89. NUMBEROFTESTCASES:
BasedonCartesian
productofthepartition
subsets
3MONTHCLASSES
4DAYCLASSES
3YEARCLASSES
3*4*3=36

90. EQUIVALENCE CLASS TEST CASE FOR
COMMISSION PROBLEM
•Theinputdomainofthecommissionproblemis
partitionedbythelimitson:
–Locks
–Stocks
–Barrels
•Thefirstclassisthevalidinputandothertwoare
invalid
•Whenavalueof-1isgivenforlocks,thewhile
loopterminates,andthevaluesoftotalLocks,
totalStocks,andtotalBarrelsarecomputedto
computesalesandthencommission

92. End Commission

93. INPUT CLASSES

94. WEAK NORMAL AND STRONG
NORMAL EQUIVALENCE CLASS
Wewillhaveexactlyoneweaknormal
equivalenceclasstestcaseanditsidenticalto
thestrongnormalequivalenceclasstestcase

95. WEAK ROBUST TEST CASE

96. STRONG ROBUST EQUIVALENCE CLASS
TEST CASES

97. OUTPUT RANGE EQUIVALENCE CLASS TEST

98. •Equivalencepartitionsaredesignedsothat
everypossibleinputbelongstooneandonly
oneequivalencepartition
•Itisappropriatewheninputdataisdefinedin
termsofintervals
•Equivalenceclasstestingisstrengthenedbya
hybridapproachwithboundaryvaluetesting
ADVANTAGES OF EQUIVALENCE PARTITIONING
ECT BVA

99. DISADVANTAGES
•Noguidelinesforchoosinginputs
•Doesn’ttesteveryinput
•Severaltriesmaybeneededbeforetheright
equivalencerelationisdiscovered

100. EDGE TESTING
•EdgeTestingHybridofboundary
valueanalysisandequivalenceclass
testing
•NeedforEdgeTesting?
–contiguousrangesofaparticular
variableconstituteequivalenceclasses

101. •Normaltestvaluesforx1:{a,a+,b–,b,b+,c–,c,c+,d–,d}
•Robusttestvaluesforx1:{a–,a,a+,b–,b,b+,c–,c,c+,d–,d,d+}
•Normaltestvaluesforx2:{e,e+,f–,f,f+,g–,g}
•Robusttestvaluesforx2:{e–,e,e+,f–,f,f+,g–,g,g+}
•NOTE:Onesubtledifferenceisthatedgetestvaluesdonotinclude
thenominalvaluesthatwehadwithboundaryvaluetesting.

102. DECISION TABLE –BASED TESTING

103. DECISION-TABLE BASED TESTING

104. DECISION TABLE TERMINOLOGIES
CONDITION
STUB
ACTION
STUB
CONDITION ENTRIES
ACTION ENTRIES

105. •Adecisionconditionisconstructedwithacondition
stubandaconditionentry
•ConditionStub
–isdeclaredasastatementofacondition
•ConditionEntry
–providesavalueassignedtotheconditionstub
•Similarly,anaction(ordecision)composestwoelements:
anactionstubandanactionentry
•ActionStub
–Onestatesanactionwithanactionstub
•ActionEntry
–specifieswhether(orinwhatorder)theactionistobeperformed

106. STRUCTURE OF DECISION TABLE
•Acolumnintheentryportionisknownasa
rule
•Rulesindicatewhichactionsaretakenforthe
conditionalcircumstancesindicatedinthe
conditionportionoftherule
•Decisiontableisusedfor“testcase
identification”
•Decisiontablesaredeclarativethatconditions
andactionsfollowsnoparticularorder

107. TYPES OF DECISION TABLES
•LimitedEntryDecisionTable
–Decisiontablesinwhichallconditionsarebinary
areknownas“Limitedentrydecisiontables”
•ExtendedEntryDecisionTable
–Ifconditionsareallowedtohaveseveralvalues,
theresultingtablesareknownas“Extendedentry
decisiontables”

108. DECISION TABLE INTERPRETATION
•Howareconditionentriesinadecisiontableinterpretedwithrespect
toaprogram?
Conditionsareinterpretedas:
Input
Equivalenceclassesofinputs
•Howareactionentriesinadecisiontableinterpretedwithrespecttoa
program?
Actionsareinterpretedas:
Output
Majorfunctionalprocessingportions
•Whatistherelationshipbetweendecisiontablesandtestcases?
–Rulesareinterpretedastestcases
–Withacompletedecisiontable,haveacompletesetoftestcases

109. •Don’tCareEntries
–Don’tcareentriesindicates,thatthe
conditionisirrelevantorconditiondoes
notapply
–Helpsingivingacompletedecisiontable
–Representedbyusingthesymbol‘__’
•Useofdon’tcareentries
–Reducethenumberofexplicitrulesbyimplying
theexistenceonnon-explicitlystatedrules

110. •InLimitedEntryDecisionTable:
ENTRIES NOTATION USED
CONDITIONENTRIES T (TRUE), F (FALSE)
ACTION ENTRIES X
DON’T CARE ENTRIES __

111. TRIANGLE DECISION TABLE
A5Actionaddedbytester,showingruleimpossible
Actionimpossibleshowswhenaruleislogicallyimpossible
CONDITION ENTRY
ACTION ENTRY
DON’T CARE ENTRY
Conditions Input
Actions Output
REASON : If two pairs of integers are equal, by transitivity,
the third pair must be equal
123456789

112. TIPS TO REMEMBER THE TABLE

113. TRIANGLE DECISION TABLE -REFINED
Number of Rules = 2
Numberof Conditions
So therefore, Number of Rules = 2
6
= 64
Whatarewegoingtoconsiderhere?
Amoredetailedviewofthethree
inequalitiesofthetriangleproperty.
Ifanyoneofthesefails,thethree
integersdonotconstitutesidesofatriangle.

114. TRIANGLE DECISION TABLE -REFINED
Number of Rules = 2
Numberof Conditions
So therefore, Number of Rules = 2
6
= 64
123 4567891011

115. TIPS TO REMEMBER THE TABLE

116. •Forlimitedentrydecisiontable:
–Ifnconditionsexist,theremustbe2
n
rules
–Whendon’tcareentriesreallyindicatethatthe
conditionisirrelevant,wecandeveloparulecountas
follows:
•Rulesinwhichdon’tcareentriesdonotoccurcountas
onerule
•Andeachdon’tcareentryinaruledoublesthecountof
thatrule
•Usefulness of rule count
–Lessrulesthancombinationrulecount,indicates
missingrules
–Morerulesthancombinationrulecount,indicates
redundantrulesorindicateinconsistenttable

117. DECISION TABLE WITH RULE COUNT
Whendon’tcareentriesreallyindicatethattheconditionis
irrelevant,wecandeveloparulecountasfollows:
Rulesinwhichdon’tcareentriesdonotoccur
countasonerule
Andeachdon’tcareentryinaruledoublesthe
countofthatrule
Ifnconditions
exist,theremust
be2
n
rules
Here,n=6
So,rules64
Wecanmakesurethat
thisisacomplete
decisiontableby
countingthetotal
number ofrules:
32+16+8+1+1+1+1+1+1+
1+1=64=2
6

118. TEST CASE FOR TRIANGLE PROBLEM

119. •Usingthedecisiontable,weobtain11
functionaltestcases:
–1waytogetascalenetriangle
–1waytogetanequilateraltriangle
–3impossiblecases
–3waystofailthetriangleproperty
–3waystogetanisoscelestriangle

120. TEST CASE FOR THE NEXTDATE FUNCTION
FIRST TRY
•Suppose we start with a set of equivalence
classes:

121. •IntheNextDateproblem,threeoftheconditionscanbethe
equivalenceclassesofthemonths
c
1
:M130Days a
1:Impossible
c
2
:M231Days a
2:NextDate
c
3
:M3Feb
•Sincetheequivalenceclassesaremutuallyexclusive,thena
monthcanonlyexistinonecondition,i.e.wecannothavea
ruleinwhichtwoofthesethreeconditionsaretrue
DECISION TABLE WITH MUTUALLY
EXCLUSIVE CONDITION
DON’TCAREENTRYMEANS,
MUSTBEFALSEINTHISCASE

122. RULE COUNT

123. HOW TO REMEMBER????
DON’TCAREENTRYMEANS,
MUSTBEFALSEINTHISCASE

124. REDUNDANT OR INCONSISTENT

127. SOLUTION????
EXTENDED ENTRY DECISION TABLE

128. SECOND TRY
To produce next date Actions
involved (manipulations) are:
1.Increment day
2.Reset day
3.Increment month
4.Reset month
5.Increment year
TOTAL
NUMBER OF
RULES:
3*4*3 = 36

129. M130 DAYS
M2 31 DAYS
M3 FEBRUARY (28/29)
D1 1-28
D2 29
D3 30
D4 31

130. SECOND TRY TABLE WITH 36 RULES
M130 DAYS
M2 31 DAYS
M3 FEBRUARY (28/29)
D1 1-28
D2 29
D3 30
D4 31

131. THIRD TRY

132. M130 DAYS
M2 31 DAYS EXCEPT
DECEMBER
M3 MONTH IS DECEMBER
M4 MONTH IS FEBRUARY
D1 1-27
D2 28
D3 29
D4 30
D5 31
Y1 LEAP YEAR
Y2 COMMON YEAR

133. M130 DAYS
M2 31 DAYS EXCEPT DECEMBER
M3 MONTH IS DECEMBER
M4 MONTH IS FEBRUARY
D1 1-27
D2 28
D3 29
D4 30
D5 31
Y1 LEAP YEAR
Y2 COMMON YEAR

137. TEST CASE FOR COMMISSION PROBLEM

140. APPLICATIONS WHERE DECISION TABLES
ARE USED
1.Where prominent if-then-else logic
2.Logical relationships among input variables
3.Calculations involving subsets of the input variables

141. ADVANTAGES OF DECISION TABLE
•Decisiontablesarepreciseandcompactwaytomodel
complicatedlogic
•Testingalsoworksiteratively.Thetablethatisdrawn
inthefirstiteration,actsasasteppingstonetoderive
newdecisiontables(s)
•Thesetablesguaranteethatweconsidereverypossible
combinationofconditionvalues.Thisisknownasits
“completenessproperty”
•Decisiontablesaredeclarative.Thereisnoparticular
orderforconditionsandactionstooccur

142. DISADVANTAGES OF DECISION TABLE
•Decisiontablesdonotscaleupwell.Weneed
to“factor”largetablesintosmalleronesto
removeredundancy

143. REFERENCE
•PaulC.Jorgensen:SoftwareTesting,A
Craftsman’sApproach,4thEdition,Auerbach
Publications,2013.

144. END
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