FaMa Tes Sui e 1.2
Se gio Segu a, Da id Bena ides and An onio Ruiz-Co ´es
{se giosegu a,bena ides,a uiz}@us.es
Applied So wa e Enginee ing Resea ch G oup
Uni e si y o Se ille, Spain
Ma ch 2010
Technical Repo ISA-10-TR-01
This epo was p epa ed by he
Applied So wa e Enginee ing Resea ch G oup (ISA)
Depa men o compu e languages and sys ems
A / Reina Me cedes S/N, 41012 Se ille, Spain
h p://www.isa.us.es/
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2010 by ISA Resea ch G oup.
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Lis o changes
Ve sion Da e Desc ip ion
1.0 Feb ua y 2009 Fi s elease
1.1 Decembe 2009 Re ined e alua ion wi h mu an s. New es cases added
1.2 Ma ch 2010 Tes cases o he ope a ion ValidP oduc upda ed.
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FaMa Tes Sui e 1.2
Se gio Segu a, Da id Bena ides and An onio Ruiz-Co ´es
Depa men o Compu e Languages and Sys ems
A Reina Me cedes S/N, 41012 Se ille, Spain
{se giosegu a, bena ides, a uiz}@us.es
Abs ac : A Fea u e Model (FM) is a compac ep esen a ion o all he p oduc s o a so wa e p oduc
line. Au oma ed analysis o FMs is apidly gaining impo ance: new ope a ions o analysis ha e been
p oposed, new ools ha e been de eloped o suppo hose ope a ions and di e en logical pa adigms and
algo i hms ha e been p oposed o pe o m hem. Implemen ing ope a ions is a complex ask ha easily
leads o e o s in analysis solu ions. In his con ex , he lack o speci ic es ing mechanisms is becoming a
majo obs acle hinde ing he de elopmen o ools and a ec ing hei quali y and eliabili y. In his pape ,
we p esen FaMa Tes Sui e, a se o implemen a ion–independen es cases o alida e he unc ionali y o
FM analysis ools. This is an e icien and handy mechanism o assis in he de elopmen o ools, de ec ing
aul s and imp o ing hei quali y. In o de o show he e ec i eness o ou p oposal, we e alua ed he sui e
using mu a ion es ing as well as eal aul s and ools. Ou esul s a e p omising and di ec ly applicable
in he es ing o analysis solu ions. We in end his pape o be a i s s ep owa d he de elopmen o a
widely accep ed es sui e o suppo unc ional es ing in he communi y o au oma ed analysis o ea u e
models.
Key Wo ds: Tes sui e, unc ional es ing, ea u e models, au oma ed analysis, so wa e p oduc lines
1 In oduc ion
So wa e P oduc Line (SPL) enginee ing is an app oach o de elop amilies o ela ed sys ems
based on he usage o eusable asse s as a means o imp o e so wa e quali y while educing
p oduc ion cos s and ime– o–ma ke [1]. P oduc s in so wa e p oduc lines a e speci ied in e ms
o ea u es. A ea u e is de ined as an inc emen in p oduc unc ionali y [2]. Key o SPLs is o
cap u e commonali ies (i.e. common ea u es) and a iabili ies (i.e. a ian ea u es) o he sys ems
ha belong o he p oduc line. To his aim, ea u e models a e commonly used. A ea u e model
[3, 4] is a compac ep esen a ion o all he p oduc s o a p oduc line in e ms o ea u es and
ela ionships among hem (see Figu e 1).
The au oma ed analysis o ea u e models deals wi h he au oma ed ex ac ion o in o ma ion
om ea u e models. Typical ope a ions o analysis allow de e mining whe he a ea u e model is
oid (i.e. i ep esen s no p oduc s), whe he i con ains e o s (e.g. ea u es ha canno be pa o
any p oduc ) o wha is he numbe o p oduc s o he SPL ep esen ed by he model. Ca alogues
wi h up o 30 di e en analysis ope a ions on ea u e models ha e been epo ed in he li e a u e
[5, 6]. Analysis solu ions can be ca ego ized in hose using p oposi ional logic [7, 8, 9, 10, 11, 12, 13],
cons ain p og amming [14, 15, 16], desc ip ion logic [17, 18] and ad-hoc algo i hms and echniques
[19, 20, 21]. The e a e also a numbe o ools suppo ing hese analysis capabili ies such as AHEAD
Tool Sui e [22], FaMa F amewo k [23], Fea u e Model Plug-in [24] and pu e:: a ian s [25].
The implemen a ion o analysis ope a ions is a ha d ask in ol ing complex da a s uc u es and
algo i hms. This makes he de elopmen o analysis ools a om i ial and easily leads o e o s
inc easing de elopmen ime and educing hei eliabili y. Gaining con idence in he absence o
de ec s in hese ools is essen ial since he in o ma ion ex ac ed om ea u e models is used o
suppo decisions all along he SPL de elopmen p ocess [2]. Howe e , he lack o speci ic es ing
mechanisms in his con ex appea s as a majo obs acle o enginee s when ying o assess he
unc ionali y and quali y o hei p og ams. Hence, i is known ha so wa e es ing accoun s o
abou 50% o he o al cos o so wa e de elopmen [26].
So wa e es ing in ends o e eal aul s in he so wa e unde es [27, 28]. This is mainly done
by checking he beha iou o he p og am wi h se o inpu -ou pu combina ions (i.e. es cases).
The main challenge when es ing is o ind a balance be ween he numbe o es cases and hei
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e ec i eness [28]. Hence, ying o be exhaus i e when es ing so wa e ools may easily inc ease he
numbe o es cases o an unmanageable le el. Simila ly, using a educed numbe o inpu -ou pu
combina ions may esul in a weak e ec i eness.
In his pape , we p esen a se o implemen a ion–independen es cases o alida e he unc-
ionali y o ea u e model analysis ools. Th ough he implemen a ion o ou es cases, aul s can
be apidly de ec ed assis ing in he de elopmen o ea u e model analysis ools and imp o ing
hei eliabili y and quali y. Fo i s design and e alua ion, we used popula echniques om he
so wa e es ing communi y o assis us on he c ea ion o a ep esen a i e se o inpu -ou pu com-
bina ions. These es cases can be used ei he in isola ion o as a sui able complemen o u he
es ing me hods such as whi e–box es ing echniques [27, 29] o au oma ed es da a gene a o s
[30]. As sugges ed by he es ing li e a u e, each es case was designed o e eal a single ype o
aul . This allows use s o iden i y clea ly he sou ce o a aul once i has been de ec ed. B ie ly,
we nex desc ibe he main cha ac e is ics o ou sui e:
– Ope a ions es ed. Cu en e sion o ou sui e, called FaMa Tes Sui e, add esses 7 ou o
30 analysis ope a ions on ea u e models iden i ied in he li e a u e [5]. These we e selec ed o
hei ex ended use in he communi y o au oma ed analysis and hei he e ogeneous na u e.
– Tes ing echniques. Fou black-box es ing echniques we e used o design es cases, namely:
equi alence pa i ioning, bounda y- alue analysis, pai wise es ing and e o guessing. A p e-
limina y e alua ion o he es ing echniques o be used in ou app oach was p esen ed in
[31].
– Tes cases. The sui e is composed by 192 es cases. Each es case is designed in e ms o he
inpu s (i.e. ea u e models and some o he pa ame e s) and expec ed ou pu s o he analysis
ope a ions unde es .
– Adequacy. We e alua ed he e ec i eness o ou sui e using mu a ion es ing and eal aul s
as ollows. Fi s ly, we gene a ed hund eds o aul y e sions (so-called mu an s) o h ee open
sou ce analysis ools in eg a ed in o he FaMa amewo k. Then, we execu ed ou sui e agains
hose aul y ools and check how many aul s we e de ec ed by ou es cases. As a esul , he
sui e iden i ied 96.1% o he aul s showing he easibili y o ou p oposal. We hen e ined ou
sui e un il ob aining a sco e o 100%. Ou e ined sui e also showed o be e ec i e in de ec ing
mo i a ing aul s ound in he li e a u e and in a ecen elease o he FaMa amewo k.
The emainde o he pape is s uc u ed as ollows: Sec ion 2 p esen s ea u e models, hei
analyses and black-box es ing echniques in a nu shell. A de ailed desc ip ion o how we designed
ou es cases is p esen ed in Sec ion 3. Sec ion 4 desc ibes he adequacy e alua ion and e inemen
o he sui e. A summa y o he e ined sui e and a b ie discussion is p esen ed in Sec ion 5. Finally,
we summa ize ou main conclusions and desc ibe ou u u e wo k in Sec ion 6.
2 P elimina ies
This sec ion p o ides an o e iew o ea u e models, hei analysis and he black–box es ing
echniques used in ou app oach.
2.1 Fea u e models
A ea u e model [3, 4] ep esen s all he p oduc s o an SPL in a single model in e ms o ea u es
and ela ionships among hem. I is o ganized hie a chically and is g aphically depic ed as a ea u e
diag am. Figu e 1 shows a simpli ied example o a ea u e model ep esen ing an e-comme ce SPL.
The model illus a es how ea u es a e used o speci y he commonali ies and a iabili ies o he
on-line shopping sys ems ha belong o he p oduc line. The oo ea u e (i.e. E-Shop) iden i ies
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E-Shop
Sea ch
Basic Ad anced
Ca alogue
In o
Image P ice
Desc ip ion
Secu i y
Medium
High
Paymen
PC
Bank D a
O e s Mobile
GUI
C edi Ca d
Visa Ame ican Exp ess
Manda o y
Op ional
Al e na i e
O
Requi es
Excludes
Banne s
Figu e 1: A sample ea u e model
he SPL. The ela ionships be ween a pa en ea u e and i s child ea u es can be mainly di ided
in o:
Manda o y. A child ea u e has a manda o y ela ionship wi h i s pa en when he child is in-
cluded in all p oduc s in which i s pa en ea u e appea s. Fo ins ance, e e y on-line shopping
sys em in ou example mus implemen a ca alogue o p oduc s.
Op ional. A child ea u e has an op ional ela ionship wi h i s pa en when he child can be
op ionally included in all p oduc s in which i s pa en ea u e appea s. Fo ins ance, banne s is
de ined as an op ional ea u e.
Al e na i e. A se o child ea u es ha e an al e na i e ela ionship wi h hei pa en when only
one ea u e o he child en can be selec ed when i s pa en ea u e is pa o he p oduc . In ou
SPL, a shopping sys em may implemen high o medium secu i y policy bu no bo h in he same
p oduc .
O -Rela ion. A se o child ea u es ha e an o - ela ionship wi h hei pa en when one o mo e
o hem can be included in he p oduc s in which i s pa en ea u e appea s. A shopping sys em
can implemen se e al paymen modules: bank d a ,c edi ca d o bo h o hem.
No ice ha a child ea u e can only appea in a p oduc i i s pa en ea u e does. The oo
ea u e is a pa o all he p oduc s wi hin he SPL. In addi ion o he pa en al ela ionships be-
ween ea u es, a ea u e model can also con ain c oss- ee cons ain s be ween ea u es. These a e
ypically o he o m:
Requi es. I a ea u e A equi es a ea u e B, he inclusion o A in a p oduc implies he inclusion
o B in such p oduc . On-line shopping sys ems accep ing paymen s wi h c edi ca d mus imple-
men a high secu i y policy.
Excludes. I a ea u e A excludes a ea u e B, bo h ea u es canno be pa o he same p oduc .
Shopping sys ems implemen ing a mobile GUI canno include suppo o banne s.
Fea u e models we e i s in oduced as a pa o he FODA (Fea u e-O ien ed Domain Analy-
sis) me hod back in 1990 [4]. Since hen, ea u e modelling has been widely adop ed by he so wa e
p oduc line communi y and a numbe o ex ensions ha e been p oposed in a emp s o imp o e
p ope ies such as succinc ness and na u alness. We e e he eade o [6] o a de ailed su ey on
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he di e en ea u e modelling languages.
2.2 Au oma ed analyses o ea u e models
The au oma ed analysis o ea u e models deals wi h he compu e –aided ex ac ion o in o ma-
ion om ea u e models [5]. F om he in o ma ion ob ained, ma ke ing s a egies and echnical
decisions can be de i ed [32]. The analysis o a ea u e model is gene ally pe o med in wo s eps:
i) Fi s , he model is ansla ed in o a speci ic logic ep esen a ion such as a sa is iabili y p oblem
[7, 8, 9, 10, 11, 12, 13], a cons ain sa is ac ion p oblem [14, 15, 16] o a knowledge base using
desc ip ion logic [17, 18], ii) Then, o - he-shel sol e s a e used o au oma ically pe o m a a ie y
o ope a ions on he logic ep esen a ion o he model. Ca alogues wi h up o 30 di e en analysis
ope a ions on ea u e models ha e been epo ed in he li e a u e [5, 6]. Following, we summa ize
some o he analysis ope a ions we will e e h ough he es o he pape .
De e mining i a ea u e model is oid. This ope a ion akes a ea u e model as inpu and
e u ns a alue in o ming whe he such ea u e model is oid o no [6, 7, 8, 9, 10, 11, 12, 13, 14,
17, 18, 19, 20, 33]. A ea u e model is oid i i ep esen s no p oduc s.
Finding ou i a p oduc is alid. This ope a ion checks whe he an inpu p oduc (i.e.
se o ea u es) belongs o he se o p oduc s ep esen ed by a gi en ea u e model o no
[6, 7, 8, 9, 10, 14, 16, 18, 33]. As an example, P={E-Shop, Ca alogue, In o, Desc ip ion, Secu-
i y, Medium, GUI, PC}is no a alid p oduc o he p oduc line ep esen ed by he model in
Figu e 1 because i does no include he manda o y ea u e Paymen .
Ob aining all p oduc s. This ope a ion akes a ea u e model as inpu and e u ns all he p od-
uc s ep esen ed by he model [7, 9, 10, 14, 19, 20, 33].
Calcula ing he numbe o p oduc s. This ope a ion e u ns he numbe o p oduc s ep e-
sen ed by a ea u e model [8, 10, 14, 19, 20, 21, 33]. The model in Figu e 1 ep esen s 2016 di e en
p oduc s.
Calcula ing a iabili y. This ope a ion akes a ea u e model as inpu and e u ns he a io be-
ween he numbe o p oduc s and 2n−1 whe e n is he numbe o ea u es in he model [14, 33].
This ope a ion may be used o measu e he lexibili y o he p oduc line. Fo ins ance, a small
ac o means ha he numbe o combina ions o ea u es is e y limi ed compa ed o he o al
numbe o po en ial p oduc s. In Figu e 1, Va iabili y = 0.00048.
Calcula ing commonali y. This ope a ion akes a ea u e model and a ea u e as inpu s and e-
u ns a alue ep esen ing he p opo ion o alid p oduc s in which he ea u e appea s [14, 21, 33].
This ope a ion may be used o p io i ize he o de in which he ea u es a e o be de eloped and
can also be used o de ec dead ea u es [15]. In Figu e 1, Commonali y(Banne s) = 25%.
Dead ea u es de ec ion. This ope a ion akes a ea u e model as inpu and e u ns a se o
dead ea u es (i any) [2, 8, 11, 15, 33, 20, 13]. A dead ea u e is a ea u e ha ne e appea s in any
o he p oduc s ep esen ed by he ea u e model [15]. These a e caused by c oss- ee cons ain s.
Some common cases o dead ea u e a e p esen ed in Figu e 4 (Sec ion 3.2.1).
Some comme cial and open sou ce ools suppo ing he analysis o ea u e models a e he
AHEAD Tool Sui e [22], FaMa F amewo k [23], Fea u e Model Plug-in [24] and pu e:: a ian s
[25].
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2.3 Black-box es ing echniques
So wa e es ing is pe o med by means o es cases. A es case is a se o es inpu s and ex-
pec ed ou pu s de eloped o e i y compliance wi h a speci ic equi emen [29, 27]. Tes cases
may be g ouped in o so-called es sui es. A a ie y o es ing echniques has been epo ed o
assis on he design o e ec i e es cases, i.e. hose ha will ind mo e aul s wi h less e o and
ime [26, 27, 29]. In his pape , we will ocus on he so called black-box es ing echniques. These
echniques ocus on checking whe he a p og am does wha i is supposed o do based on i s spec-
i ica ion [27]. No knowledge abou he in e nal s uc u e o implemen a ion o he so wa e unde
es is assumed. We will e e o ou o hese echniques along he pape . B ie ly, hese a e:
Equi alence pa i ioning. This echnique is used o educe he numbe o es cases o be de-
eloped while s ill main aining a easonable es co e age (i.e. he deg ee o which he es cases
e i ies he es equi emen s) [27, 29]. In his echnique, he inpu domain o he p og am is di-
ided in o pa i ions (also called equi alence classes) in which he p og am is expec ed o p ocess
he se o da a inpu in a simila (i.e. equi alen ) way. Acco ding o his es ing app oach, only
one o a ew es cases o each pa i ion a e needed o e alua e he beha iou o he p og am o
he co esponding pa i ion. Thus, selec ing a subse o es cases om each pa i ion is enough o
es he p og am e ec i ely while keeping a manageable numbe o es cases.
Bounda y alue analysis. This echnique is used o guide he es e when selec ing inpu s om
equi alence classes [27, 29]. Acco ding o his echnique, p og amme s usually make mis akes wi h
he inpu s alues loca ed on he bounda ies o he equi alence classes. This me hod guides he
es e o selec hose inpu s loca ed on he “edges” o he equi alence pa i ions.
Pai wise es ing (also called 2-wise es ing). This is a combina o ial so wa e es ing me hod
ocusing on es ing all possible disc e e combina ions o wo inpu pa ame e s [29, 34]. Acco ding
o he hypo hesis behind his echnique, mos common e o s in ol e one inpu pa ame e . The
nex mos common ca ego y o e o s consis s o hose dependen on in e ac ions be ween pai s
o pa ame e s and so on. Thus, he main goal o his echnique is o add ess he second mos
common cases o e o s ( hose in ol ing wo pa ame e s) while keeping he numbe o es cases
in a manageable le el.
E o guessing. This is a so wa e es ing echnique based on he abili y o he es e o p edic
whe e aul s a e loca ed acco ding o i s expe ience on he domain [29]. Using his echnique, es
cases a e speci ically designed o exe cise ypical e o -p one poin s ela ed o he ype o sys em
unde es .
3 Tes sui e design
In his sec ion, we desc ibe how we designed he es cases ha compose ou sui e. These mainly a e
inpu -ou pu combina ions speci ically c ea ed o e eal ailu es in he implemen a ions o analysis
ope a ions on ea u e models.
C ea ing es cases o e e y possible pe mu a ion o a p og am is imp ac ical and e y o en
impossible; he e a e simply oo many inpu -ou pu combina ions [27]. Thus, as ecalled by P ess-
man [28], he objec i e when es ing is o “design es s ha ha e he highes likelihood o inding
mos e o s wi h a minimum amoun o ime and e o ”. To assis us in he p ocess, we e alua ed
a numbe o echniques epo ed in he li e a u e [27, 29]. We ocused on black-box echniques
since we wan ou es cases o ely on he speci ica ion o he analysis ope a ions a he han on
speci ic implemen a ions. In pa icula , we ound he ou echniques desc ibed in Sec ion 2.3 o
be e ec i e and gene ic enough o be applicable o ou domain.
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Fo he design o he sui e we ollowed ou s eps, namely: i) iden i ica ion o he inpu s and
ou pu s o he analysis ope a ions, ii) selec ion o ep esen a i e ins ances o each ype o inpu , iii)
combina ion o p e ious ins ances in hose ope a ions ecei ing mo e han one inpu pa ame e ,
and i ) es cases epo . Following, we de ail how we ca ied ou hese s eps.
3.1 Iden i ica ion o inpu s and ou pu s
The cu en e sion o ou sui e add esses 7 ou o 30 analysis ope a ions on ea u e models iden i-
ied in he li e a u e [5] (de ailed in Sec ion 2.2). We selec ed hese ope a ions o i s ex ended use
in he communi y o au oma ed analysis and hei he e ogeneous na u e. In o de o iden i y he
ype o he inpu /ou pu pa ame e s o he ope a ions and a oid misunde s andings when in e -
p e ing hei seman ics, we used he o mal de ini ion o he ope a ions p oposed by Bena ides [32].
Table 1 summa izes he ope a ions in e ms o hei inpu s and ou pu s. Fo he sake o simplici y,
we assign an iden i ica o o each ope a ion o e e hem along he pape . As illus a ed, inpu s
a e composed o ea u e models, p oduc s and ea u es. Ou pu s mainly comp ise collec ions o
p oduc s and ea u es oge he wi h nume ic and boolean alues.
ID ope a ion Ope a ion Inpu s Ou pu
VoidFM Void FM FM Boolean alue
ValidP oduc Valid p oduc FM, P oduc Boolean alue
P oduc s P oduc s FM Collec ion o p oduc s
#P oduc s Numbe o p oduc s FM Nume ic alue
Va iabili y Va iabili y FM Nume ic alue
Commonali y Commonali y FM, Fea u e Nume ic alue
DeadFea u es Dead ea u es FM Collec ion o ea u es
Table 1: Analysis ope a ions add essed in he sui e
The ea u e modelling no a ion used in ou sui e co esponds o one showed in Sec ion 2.1
(he eina e e e ed as basic ea u e models). We selec ed his no a ion o i s simplici y and
ex ended use in cu en ea u e model analysis ools and li e a u e.
3.2 Inpu s selec ion
In his sec ion, we explain how we selec ed he inpu s o be used in ou es cases. Fo each ype o
inpu (i.e. ea u e models, p oduc s and ea u es), we nex desc ibe he echniques used and how
we applied hem.
3.2.1 Fea u e models
We ound wo es ing echniques o be help ul o he selec ion o a sui able se o inpu ea u e
models, namely: equi alence pa i ioning and e o guessing. We applied hem as ollows:
Equi alence pa i ioning. The po en ial numbe o inpu ea u e models is limi less. To selec
a ep esen a i e se o hese, we p opose di iding inpu ea u e models in o equi alence classes
acco ding o he di e en ypes o ela ionships and cons ain s among ea u es, i.e. manda o y,
op ional, o , al e na i e, equi es and excludes. This is a na u al pa i ion in ui i ely used in
mos p oposals when de ining he mapping om a ea u e model o a speci ic logic pa adigm (e.g.
cons ain sa is ac ion p oblem) [7, 9, 10, 13, 14, 15, 18, 32]. The e o e, acco ding o his echnique,
i a ea u e model wi h a single manda o y ela ionship is co ec ly managed by an ope a ion, we
could assume ha hose wi h mo e han one manda o y ela ionship would also be p ocessed
success ully.
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Reasone Mu an s Equi alen Disca ded
Sa 4jReasone 262 27 47
Ja aBDDReasone 302 28 37
JaCoPReasone 185 46 3
To al 749 101 87
Table 4: Mu an s gene a ion esul s
VoidFM and ValidP oduc p oduced he lowes sco es and highe numbe o ali e mu an s. We
ound ha mu an s on hese ope a ions equi ed inpu models o ha e a e y speci ic pa e n in
o de o be killed and he e o e we e ha de o de ec han mu an s in he es o ope a ions.
A e age mu a ion sco es in he h ee easone s anged be ween 94.4% and 100%. In o al, ou es
sui e was able o kill 1390 (96.1%) ou o he 1445 mu an s execu ed showing he e ec i eness o
he sui e.
Ope a ion Sa 4jReasone Ja aBDDReasone JaCoPReasone
Mu an s Ali e Sco e Mu an s Ali e Sco e Mu an s Ali e Sco e
VoidFM 55 20 63.6 75 12 84.0 8 0 100
ValidP oduc 109 4 96.3 129 7 94.6 61 0 100
P oduc s 86 1 98.8 130 2 98.5 37 0 100
#P oduc s 57 1 98.2 77 2 97.4 13 0 100
Va iabili y 82 1 98.8 104 2 98.1 36 0 100
Commonali y 109 1 99.1 131 2 98.5 66 0 100
DeadFea u es - - - - - - 80 0 100
To al 498 28 94.4 646 27 95.8 301 0 100
Table 5: Mu an s execu ion esul s
4.1.3 Re inemen
As shown in p e ious sec ions, mu a ion es ing is an e ec i e means o measu e he e ec i eness
o a es sui e. Howe e , in o ma ion p o ided by mu a ion es ing can also be used o guide he
c ea ion o new es cases ha kill he emaining ali e mu an s and s eng hen he inal es sui e
[41]. Following his app oach, we designed a numbe o es cases o kill emaining unde ec ed
mu an s un il ob aining a sco e o 100% in he h ee FaMa easone s. A o al o 27 new es cases
we e c ea ed and execu ed. Fo ins ance, ali e mu an s guided us o he c ea ion o a couple es
cases o ensu e ha al e na i e ela ionships a e no p ocessed as o - ela ionships and ice– e sa
(e.g. es cases VM-21 and VM-22 in Appendix A). Ou o he 27 es cases c ea ed, we selec ed
hose es cases ha showed o be e ec i e in killing mu an s in a leas wo o he h ee subjec
easone s and added hem o ou sui e. As a esul , 10 es cases we e added o he ini ial es
sui e inc easing he numbe o hese un il 190.
4.2 E alua ion using eal aul s
Fo a u he e alua ion o ou app oach, we checked he e ec i eness o ou ool in de ec ing eal
aul s. In pa icula , we i s s udied a mo i a ing aul ound in he li e a u e. Then, we used ou
es sui e o es he elease 1.0 alpha o he FaMa amewo k, de ec ing one de ec . These esul s
a e nex epo ed.
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4.2.1 Mo i a ing aul ound in he li e a u e
Conside he wo k o Ba o y in SPLC’05 [7], one o he seminal pape s in he communi y o
au oma ed analysis o ea u e models. The pape included a bug (la e ixed3) in he mapping
o a ea u e model o a p oposi ional o mula. We implemen ed his w ong mapping in o a mock
easone o FaMa and checked he e ec i eness o ou app oach in de ec ing he aul .
Figu e 7 illus a es an example o he w ong ou pu caused by he aul . This mani es s i sel
in al e na i e ela ionships whose pa en ea u e is no manda o y making easone s o conside
as alid p oduc hose including mul iple al e na i e sub ea u es and excluding he pa en ea u e
(P3). As a esul , he se o p oduc s e u ned by he ool is e oneously la ge han he ac ual
one. Fo ins ance, he numbe o p oduc s e u ned by ou aul y ool when using he model in
Figu e 1 as inpu is 3,584 (ins ead o he ac ual 2,016). No e ha his is a mo i a ing aul since
i can easily emain unde ec ed e en when using an inpu wi h he p oblema ic pa e n. Hence,
in he p e ious example (ei he wi h “secu i y” ea u e as manda o y o op ional), he mock ool
co ec ly iden i ies he model as non oid (i.e. i ep esen s a leas one p oduc ), and so he aul
emains la en .
Secu i y
Medium
High
P1={Secu i y,High}
P2={Secu i y,Medium}
P3={High,Medium}
Figu e 7: W ong se o p oduc s ob ained wi h he aul y easone
We implemen ed ou es cases using JUni and es ed ou aul y ool. The aul was de ec ed
by ou es sui e in he ope a ions VoidFM,P oduc ,#P oduc s,Va iabili y and Commonali y
emaining la en in he ope a ions ValidP oduc and DeadFea u es. These wo ope a ions equi ed
a e y speci ic pa e n o e eal he aul no included in he inpu s o ou es sui e. This gi es
an idea o he complexi y o es ing in his domain. We ound his aul su icien ly mo i a ing o
ex end ou sui e wi h es cases ha de ec i in all he ope a ions. Thus, we designed wo new
es cases o de ec he aul in he ope a ion ValidP oduc and DeadFea u es and added hem o
ou es sui e esul ing in a o al o 192 es cases.
4.2.2 FaMa 1.0 alpha
Finally, we e alua ed ou ool by ying o de ec aul s in a ecen elease o he FaMa F amewo k,
FaMa 1.0 alpha. We execu ed he 192 es cases o ou e ined es sui e. Tes s e ealed one de ec .
The aul a ec ed he ope a ions ValidP oduc and Commonali y in Sa 4jReasone . The sou ce o
he p oblem was a bug in he c ea ion o p oposi ional clauses in he so-called s aged con igu a ions,
a new ea u e o he ool.
5 Tes sui e summa y and discussion
Table 6 summa izes he gene al aspec s o he e ined es sui e using he common e ms o he
IEEE S anda d o So wa e Tes ing Documen a ion [35]. Fo each ope a ion, he numbe o es
cases and he es ing echniques used a e p esen ed. Global inpu s cons ain s speci y cons ain s
ha mus be ue o e e y inpu in he se o associa ed es cases. Fo he sake o simplici y,
3 p:// p.cs.u exas.edu/pub/p eda o /splc05.pd
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wo main inpu cons ain s we e imposed, namely: i) inpu ea u e models mus be syn ac ically
co ec (e.g. checking models o con o mance o a me amodel), and ii) all inpu pa ame e s o he
analysis ope a ions mus be p o ided. An ope a ion is said o pass he es (so-called pass c i e ia)
when all he es cases associa ed o ha ope a ion a e success ul.
T ying o be exhaus i e when es ing ea u e model analyses ools can easily inc ease he
numbe o es cases o an unmanageable le el. To keep a easonable balance be ween numbe
o es cases and es co e age, we kep in mind a numbe o gene ic ecommenda ions om he
es ing li e a u e, namely: i) we ga e p io i y o hose decisions educing he numbe o es cases,
ii) we a oided edundancies by designing each es case o e eal a single ype o aul , and iii) we
designed simple es cases whose ou pu could be wo ked ou manually o a oid es hemsel es o
become e o -p one.
We ema k ha he po en ial use s o he sui e a e e e y ool suppo ing he analysis o ea u e
model. This can be au oma ed by simply implemen ing he es cases in he desi ed pla o m and
execu ing hem. A comple e lis o he es cases ha compose he sui e is epo ed in Appendix
A. To acili a e i s implemen a ion, inpu models used in he es cases a e also a ailable in XML
o ma in he FaMa Tool Sui e Web si e4.
Tes sui e iden i ie : FaMa Tes Sui e ( 1.2)
Ope a ions es ed Tes cases Techniques used
Void FM 24 EP, PT
Valid p oduc 63 EP, PT, BVA
P oduc s 21 EP, PT
Numbe o p oduc s 21 EP, PT
Va iabili y 21 EP, PT
Commonali y 33 EP, PT, BVA
Dead ea u es 9 EG
To al 192
Global inpu cons ain s:
- Inpu FMs mus be syn ac ically co ec
- All inpu pa ame e s a e equi ed
Ope a ion pass c i e ia:
- Pass 100% o associa ed es cases
Table 6: Gene al o e iew o he FaMa Tes Sui e (EP: Equi alence Pa i ioning, PT: Pai wise
Tes ing, BVA: Bounda y-Value Analysis, EG: E o Guessing)
6 Conclusions and u u e wo k
In his pape , we p esen a se o implemen a ion–independen es cases o alida e he unc ion-
ali y o ools suppo ing he analysis o ea u e models. Th ough he implemen a ion o ou es
cases, aul s can be apidly de ec ed assis ing in he de elopmen o ea u e model analysis ools
and imp o ing hei eliabili y and quali y. These can be used ei he in isola ion o as a sui able
complemen o u he es ing me hods such as whi e–box es ing echniques o au oma ed es
da a gene a o s. Fo i s design, we used popula echniques om he so wa e es ing communi y
o assis us on he c ea ion o a ep esen a i e se o inpu –ou pu combina ions. To e alua e i s
e ec i eness, we applied mu a ion es ing on h ee open sou ce ea u e model analysis ools in e-
g a ed in o he FaMa amewo k. These ools use di e en unde lying pa adigms and we e coded
by di e en de elope s wha p o ides he necessa y he e ogenei y o he e alua ion. We ini ially
ob ained an a e age mu a ion sco e o 96.1% and e ined ou sui e p og essi ely un il ge ing 100%.
4h p://www.isa.us.es/ ama/?FaMa Tes Sui e
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Once e ined, ou sui e also showed o be e ec i e in de ec ing eal aul s ound in he li e a u e
and in a ecen elease o FaMa. Bo h, he es sui e documen a ion and he inpu s models used in
he es cases a e eady– o–use and a ailable a he Web Si e o he FaMa Tool Sui e. We in end
his pape o be a i s e o owa d he de elopmen o a widely accep ed es sui e o suppo
unc ional es ing in he communi y o au oma ed analysis o ea u e models.
Se e al challenges emain o ou u u e wo k in wo main di ec ions, namely:
–We in end o ex end ou sui e wi h new ope a ions and es ing echniques. We also plan o
e alua e ou sui e wi h o he ools and in de elopmen scena ios using FaMa and epo ou
expe iences o he communi y.
–We also plan o explo e he bene i s ob ained when combining ou sui e wi h o he au oma ed
es ing me hods. In a p e ious wo k [30], we p esen ed an au oma ed es da a gene a o o
he analysis o ea u e models wi h p omising esul s. I gene a es andom ollow–up es cases
based on he ela ions be ween inpu s ea u e models and hei s expec ed ou pu s (so-called
me amo phic es ing). Howe e , i is known ha me amo phic es ing p oduce be e esul s
when combined wi h o he es case selec ion s a egies ha gene a e he ini ial se o es
cases. We in end o use ou sui e o guide he gene a ion o es cases in ou au oma ed es
da a gene a o and s udy he gains in e iciency and e icacy.
Ma e ial
The ools, mu an s, and es cases used in ou e alua ion a e a ailable a h p://www.lsi.us.
es/~segu a/ iles/ma e ial/ s_1_2/
Acknowledgmen s
We would like o hank he e iewe s o he pape as well as Don Ba o y and Robe M. Hie ons
whose commen s and sugges ions helped us o imp o e he pape subs an ially. We also hank Jose
Galindo o his echnical suppo du ing he e alua ion o ou app oach.
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A Tes cases
In his appendix, we p esen he es cases included in he cu en e sion o he FAMA Tes Sui e.
Fo each ope a ion, associa ed es cases a e de ailed.
A.1 Ope a ion Void Fea u e Model
ID Desc ip ion Inpu Exp. Ou pu Deps.
VM-1 Check whe he manda o y ela ionships a e
co ec ly managed by he ope a ion.
A
B
Non oid
VM-2 Check whe he op ional ela ionships a e co -
ec ly managed by he ope a ion.
A
B
Non oid
VM-3 Check whe he o – ela ionships a e co ec ly
managed by he ope a ion.
A
B C
Non oid
VM-4 Check whe he al e na i e ela ionships a e
co ec ly managed by he ope a ion.
A
B C
Non oid
VM-5 Check whe he ‘ equi es’ cons ain s a e co -
ec ly managed by he ope a ion.
A
B C
Non oid VM-2
VM-6 Check whe he ‘excludes’ cons ain s a e co -
ec ly managed by he ope a ion.
A
B C
Non oid VM-2
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ID Desc ip ion Inpu Exp. Ou pu Deps.
VM-7
Check whe he he in e ac ion be ween
manda o y and op ional ela ionships is co -
ec ly p ocessed.
A
B
D
C
E
Non oid VM-1
VM-2
VM-8
Check whe he he in e ac ion be ween
manda o y and o - ela ionships is co ec ly
p ocessed.
A
B
E F
C D
G
Non oid VM-1
VM-3
VM-9
Check whe he he in e ac ion be ween
manda o y and al e na i e ela ionships is
co ec ly p ocessed.
A
B
E F
C D
G
Non oid VM-1
VM-4
VM-10
Check whe he he in e ac ion be ween
manda o y ela ionships and ‘ equi es’ con-
s ain s is co ec ly p ocessed.
A
BC
Non oid VM-1
VM-5
VM-11
Check whe he he in e ac ion be ween
manda o y ela ionships and ‘excludes’ con-
s ain s is co ec ly p ocessed.
A
BC
Void VM-1
VM-6
VM-12
Check whe he he in e ac ion be ween op-
ional and o - ela ionships is co ec ly p o-
cessed.
A
B
E F
C D
G
Non oid VM-2
VM-3
VM-13
Check whe he he in e ac ion be ween op-
ional and al e na i e ela ionships is co ec ly
p ocessed.
A
B
E F
C D
G
Non oid VM-2
VM-4
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ID Desc ip ion Inpu Exp. Ou pu Deps.
VM-14
Check whe he he in e ac ion be ween o -
and al e na i e ela ionships is co ec ly p o-
cessed.
A
D E
H I
B C
F G
Non oid VM-3
VM-4
VM-15
Check whe he he in e ac ion be ween o -
ela ionships and ‘ equi es’ cons ain s is co -
ec ly p ocessed.
A
B C
Non oid VM-3
VM-5
VM-16
Check whe he he in e ac ion be ween o -
ela ionships and ‘excludes’ cons ain s is co -
ec ly p ocessed.
A
B C
Non oid VM-3
VM-6
VM-17
Check whe he he in e ac ion be ween al e -
na i e ela ionships and ‘ equi es’ cons ain s
is co ec ly p ocessed.
A
B C
Non oid VM-4
VM-5
VM-18
Check whe he he in e ac ion be ween al e -
na i e ela ionships and ‘excludes’ cons ain s
is co ec ly p ocessed.
A
B C
Non oid VM-4
VM-6
VM-19
Check whe he he in e ac ion be ween ‘ e-
qui es’ and ‘excludes’ cons ain s is co ec ly
p ocessed.
A
BC
Non oid
VM-2
VM-5
VM-6
VM-20
Check whe he he in e ac ion among h ee o
mo e di e en ypes o ela ionships and con-
s ain s is co ec ly p ocessed.
A
B
D E
C
F G
Non oid
VM-1,
...,
VM-19
20
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01
ID Desc ip ion Inpu Exp. Ou pu Deps.
VM-21 Check whe he al e na i e ela ionships a e
e oneously implemen ed as o - ela ionships
B
C D
A
E
Void
VM-1
VM-4
VM-9
VM-10
VM-17
VM-22
Check whe he o - ela ionships a e e o-
neously implemen ed as al e na i e ela ion-
ships
B
C D
A
E
Non oid
VM-1
VM-3
VM-8
VM-10
VM-15
VM-23
Check whe he al e na i e sub ea u es can be
pa o a p oduc wi hou including i s pa en
ea u e
B
C D
A
E
Void
VM-1
VM-2
VM-4
VM-5
VM-6
VM-7
VM-9
VM-10
VM-11
VM-13
VM-17
VM-18
VM-24 Check whe he a p oduc can e oneously in-
clude mo e han one al e na i e ea u es
B
D
G
A
H
C
F
E
Void
VM-1
VM-4
VM-9
VM-10
VM-17
Table 7: Ope a ion oidFM. Tes cases
A.2 Ope a ion Valid P oduc
21
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01
ID Desc ip ion Inpu Exp. Ou pu Deps.
VP-1
Check whe he alid p oduc s a e co ec ly
iden i ied in ea u e models wi h manda o y
ela ionships.
A
B
P={A,B}
Valid
VP-2
Check whe he non- alid p oduc s a e co -
ec ly iden i ied in ea u e models wi h
manda o y ela ionships.
A
B
P={A}
Non- alid
VP-3
Check whe he alid p oduc s (wi h a mini-
mum se o ea u es) a e co ec ly iden i ied
in ea u e models wi h op ional ela ionships.
A
B
P={A}
Valid
VP-4
Check whe he alid p oduc s (wi h a maxi-
mum se o ea u es) a e co ec ly iden i ied
in ea u e models wi h op ional ela ionships.
A
B
P={A,B}
Valid
VP-5
Check whe he alid p oduc s (wi h a mini-
mum se o ea u es) a e co ec ly iden i ied
in ea u e models wi h o - ela ionships.
A
B C
P={A,B}
Valid
VP-6
Check whe he alid p oduc s (wi h a maxi-
mum se o ea u es) a e co ec ly iden i ied
in ea u e models wi h o - ela ionships.
A
B C
P={A,B,C}
Valid
22
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01
ID Desc ip ion Inpu Exp. Ou pu Deps.
VP-40
Check whe he alid p oduc s (wi h a mini-
mum se o ea u es) a e co ec ly iden i ied
in ea u e models con aining o - ela ionships
and ‘ equi es’ cons ain s.
A
B C
P={A,C}
Valid
VP-5
VP-6
VP-7
VP-11
VP-12
VP-41
Check whe he alid p oduc s (wi h a maxi-
mum se o ea u es) a e co ec ly iden i ied
in ea u e models con aining o - ela ionships
and ‘ equi es’ cons ain s.
A
B C
P={A,B,C}
Valid
VP-5
VP-6
VP-7
VP-11
VP-12
VP-42
Check whe he non- alid p oduc s (wi h a low
numbe o ea u es) a e co ec ly iden i ied in
ea u e models con aining o - ela ionships and
‘ equi es’ cons ain s.
A
B C
P={A}
Non- alid
VP-5
VP-6
VP-7
VP-11
VP-12
VP-43
Check whe he alid p oduc s a e co ec ly
iden i ied in ea u e models con aining o -
ela ionships and ‘excludes’ cons ain s.
A
B C
P={A,B}
Valid
VP-5
VP-6
VP-7
VP-13
VP-14
VP-15
VP-44
Check whe he non- alid p oduc s (wi h a low
numbe o ea u es) a e co ec ly iden i ied in
ea u e models con aining o - ela ionships and
‘excludes’ cons ain s.
A
B C
P={A}
Non- alid
VP-5
VP-6
VP-7
VP-13
VP-14
VP-15
29
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01

ID Desc ip ion Inpu Exp. Ou pu Deps.
VP-45
Check whe he non- alid p oduc s (wi h
a high numbe o ea u es) a e co ec ly
iden i ied in ea u e models con aining o -
ela ionships and ‘excludes’ cons ain s.
A
B C
P={A,B,C}
Non- alid
VP-5
VP-6
VP-7
VP-13
VP-14
VP-15
VP-46
Check whe he alid p oduc s a e co ec ly
iden i ied in ea u e models con aining al e -
na i e ela ionships and ‘ equi es’ cons ain s.
A
B C
P={A,C}
Valid
VP-8
VP-9
VP-10
VP-11
VP-12
VP-47
Check whe he non- alid p oduc s (wi h a low
numbe o ea u es) a e co ec ly iden i ied in
ea u e models con aining al e na i e ela ion-
ships and ‘ equi es’ cons ain s.
A
B C
P={A}
Non- alid
VP-8
VP-9
VP-10
VP-11
VP-12
VP-48
Check whe he non- alid p oduc s (wi h a
high numbe o ea u es) a e co ec ly iden-
i ied in ea u e models con aining al e na i e
ela ionships and ‘ equi es’ cons ain s.
A
B C
P={A,B,C}
Non- alid
VP-8
VP-9
VP-10
VP-11
VP-12
VP-49
Check whe he alid p oduc s a e co ec ly
iden i ied in ea u e models con aining al-
e na i e ela ionships and ‘excludes’ con-
s ain s.
A
B C
P={A,B}
Valid
VP-8
VP-9
VP-10
VP-13
VP-14
VP-15
30
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01
ID Desc ip ion Inpu Exp. Ou pu Deps.
VP-50
Check whe he non- alid p oduc s (wi h a low
numbe o ea u es) a e co ec ly iden i ied in
ea u e models con aining al e na i e ela ion-
ships and ‘excludes’ cons ain s.
A
B C
P={A}
Non- alid
VP-8
VP-9
VP-10
VP-13
VP-14
VP-15
VP-51
Check whe he non- alid p oduc s (wi h a
high numbe o ea u es) a e co ec ly iden-
i ied in ea u e models con aining al e na i e
ela ionships and ‘excludes’ cons ain s.
A
B C
P={A,B,C}
Non- alid
VP-8
VP-9
VP-10
VP-13
VP-14
VP-15
VP-52
Check whe he alid p oduc s (wi h a mini-
mum se o ea u es) a e co ec ly iden i ied in
ea u e models con aining ‘ equi es’ and ‘ex-
cludes’ cons ain s.
A
BC
P={A}
Valid
VP-3
VP-4
VP-11
VP-12
VP-13
VP-14
VP-15
VP-53
Check whe he alid p oduc s (wi h a maxi-
mum se o ea u es) a e co ec ly iden i ied in
ea u e models con aining ‘ equi es’ and ‘ex-
cludes’ cons ain s.
A
BC
P={A,C}
Valid
VP-3
VP-4
VP-11
VP-12
VP-13
VP-14
VP-15
VP-54
Check whe he non- alid p oduc s (wi h a
high numbe o ea u es) a e co ec ly iden-
i ied in ea u e models con aining ‘ equi es’
and ‘excludes’ cons ain s.
A
BC
P={A,B,C}
Non- alid
VP-3
VP-4
VP-11
VP-12
VP-13
VP-14
VP-15
31
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01
ID Desc ip ion Inpu Exp. Ou pu Deps.
VP-55
Check whe he alid p oduc s (wi h a mini-
mum se o ea u es) a e co ec ly iden i ied
in ea u e models con aining h ee o mo e di -
e en ypes o ela ionships and cons ain s.
A
B
D E
C
F G
P={A,B,D}
Valid
VP-1,
...,
VP-54
VP-56
Check whe he alid p oduc s (wi h a max-
imum se o ea u es) a e co ec ly iden i ied
in ea u e models con aining h ee o mo e di -
e en ypes o ela ionships and cons ain s.
A
B
D E
C
F G
P={A,B,C,E, F,G}
Valid
VP-1,
...,
VP-54
VP-57
Check whe he non- alid p oduc s (wi h a low
numbe o ea u es) a e co ec ly iden i ied in
ea u e models con aining h ee o mo e di -
e en ypes o ela ionships and cons ain s.
A
B
D E
C
F G
P={A,B}
Non- alid
VP-1,
...,
VP-54
VP-58
Check whe he non- alid p oduc s (wi h a
high numbe o ea u es) a e co ec ly iden-
i ied in ea u e models con aining h ee o
mo e di e en ypes o ela ionships and con-
s ain s.
A
B
D E
C
F G
P={A,B,C,D,
E,F,G}
Non- alid
VP-1,
...,
VP-54
VP-59 Check whe he non- alid p oduc s excluding
he oo ea u e a e co ec ly iden i ied.
A
B
P={B}
Non- alid VP-3
VP-4
32
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01
ID Desc ip ion Inpu Exp. Ou pu Deps.
VP-60
Check whe he non- alid p oduc s including
non-exis en ea u es a e co ec ly p ocessed
by he ope a ion.
A
B
P={A,H}
Non- alid VP-3
VP-4
VP-61
Check whe he child ea u es in an al e na-
i e ela ionships can e oneously be pa o a
p oduc wi hou hei pa en ea u e.
B
C D
A
E
P={A,D,E}
Non Valid
VP-1
...
VP-4
VP-8
...
VP-18
VP-22
...
VP-28
VP-32
...
VP-35
VP-46
...
VP-51
VP-62 Check whe he a p oduc can e oneously in-
clude mo e han one al e na i e ea u es
B
D
G
A
H
C
F
E
P={A,B,E,G,H}
Non Valid
VP-1
VP-2
VP-8
VP-9
VP-10
VP-22
...
VP-27
VP-46
VP-47
VP-48
VP-63
Check whe he mul iple al e na i e ea u es
can be e oneously pa o a p oduc when no
including hei non-manda o y pa en ea u e
A
B
C D
P={A,C,D}
Non Valid
VP-3
VP-4
VP-8
VP-9
VP-10
VP-32
VP-33
VP-34
VP-35
Table 8: Ope a ion ValidP oduc . Tes cases
A.3 Ope a ion All P oduc s
33
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01
ID Desc ip ion Inpu Exp. Ou pu Deps.
P-1 Check whe he manda o y ela ionships a e
co ec ly managed by he ope a ion.
A
B
{A,B}
P-2 Check whe he op ional ela ionships a e co -
ec ly managed by he ope a ion.
A
B
{A},{A,B}
P-3 Check whe he o – ela ionships a e co ec ly
managed by he ope a ion.
A
B C
{A,B},{A,C},
{A,B.C}
P-4 Check whe he al e na i e ela ionships a e
co ec ly managed by he ope a ion.
A
B C
{A,B},{A,C}
P-5 Check whe he ‘ equi es’ cons ain s a e co -
ec ly managed by he ope a ion.
A
B C
{A},{A,C},
{A,B,C}
P-2
P-6 Check whe he ‘excludes’ cons ain s a e co -
ec ly managed by he ope a ion.
A
B C
{A},{A,B},
{A,C}
P-2
P-7
Check whe he he in e ac ion be ween
manda o y and op ional ela ionships is co -
ec ly p ocessed.
A
B
D
C
E
{A,B},{A,B,D},
{A,B,C,E},
{A,B,C,D,E}
P-1
P-2
34
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01

ID Desc ip ion Inpu Exp. Ou pu Deps.
P-8
Check whe he he in e ac ion be ween
manda o y and o - ela ionships is co ec ly
p ocessed.
A
B
E F
C D
G
{A,B,C,E},
{A,B,C,F},
{A,B,C,E,F},
{A,B,D,E,G},
{A,B,C,D,E,G},
{A,B,D,F,G},
{A,B,C,D,F,G},
{A,B,D,E,F,G},
{A,B,C,D,E,F,G}
P-1
P-3
P-9
Check whe he he in e ac ion be ween
manda o y and al e na i e ela ionships is
co ec ly p ocessed.
A
B
E F
C D
G
{A,B,D,F},
{A,B,D,E},
{A,B,C,F,G},
{A,B,C,E,G}
P-1
P-4
P-10
Check whe he he in e ac ion be ween
manda o y ela ionships and ‘ equi es’ con-
s ain s is co ec ly p ocessed.
A
BC
{A,B,C}
P-1
P-5
P-11
Check whe he he in e ac ion be ween
manda o y ela ionships and ‘excludes’ con-
s ain s is co ec ly p ocessed.
A
BC
None
P-1
P-6
35
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01
ID Desc ip ion Inpu Exp. Ou pu Deps.
P-12
Check whe he he in e ac ion be ween op-
ional and o - ela ionships is co ec ly p o-
cessed.
A
B
E F
C D
G
{A,D},{A,C},
{A,C,D},
{A,C,G},
{A,B,D,F},
{A,B,D,E},
{A,C,D,G},
{A,B,C,F},
{A,B,C,E},
{A,B,D,E,F},
{A,B,C,F,G},
{A,B,C,E,G},
{A,B,C,D,F},
{A,B,C,D,E},
{A,B,C,F,E},
{A,B,C,D,F,G},
{A,B,C,D,E,G},
{A,B,C,E,F,G},
{A,B,C,D,E,F},
{A,B,C,D,E,F,G}
P-2
P-3
P-13
Check whe he he in e ac ion be ween op-
ional and al e na i e ela ionships is co ec ly
p ocessed.
A
B
E F
C D
G
{A,C},{A,D},
{A,D,G},
{A,B,C,E},
{A,B,C,F},
{A,B,D,E},
{A,B,D,F},
{A,B,D,E,G},
{A,B,D,F,G}
P-2
P-4
36
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01
ID Desc ip ion Inpu Exp. Ou pu Deps.
P-14
Check whe he he in e ac ion be ween o -
and al e na i e ela ionships is co ec ly p o-
cessed.
A
D E
H I
B C
F G
{A,D,C},
{A,C,E,H},
{A,C,E,I},
{A,B,D,G},
{A,B,D,F},
{A,C,D,E,H},
{A,C,D,E,I},
{A,B,D,F,G},
{A,B,E,G,H},
{A,B,E,F,H},
{A,B,E,G,I},
{A,B,E,F,I},
{A,B,D,E,G,H},
{A,B,E,F,G,H},
{A,B,D,E,F,H},
{A,B,D,E,G,I},
{A,B,E,F,G,I},
{A,B,D,E,F,I},
{A,B,D,E,F,G,H},
{A,B,D,E,F,G,I}
P-3
P-4
P-15
Check whe he he in e ac ion be ween o -
ela ionships and ‘ equi es’ cons ain s is co -
ec ly p ocessed.
A
B C
{A,C},{A,B,C}
P-3
P-5
P-16
Check whe he he in e ac ion be ween o -
ela ionships and ‘excludes’ cons ain s is co -
ec ly p ocessed.
A
B C
{A,B},{A,C}
P-3
P-6
P-17
Check whe he he in e ac ion be ween al e -
na i e ela ionships and ‘ equi es’ cons ain s
is co ec ly p ocessed.
A
B C
{A,C}
P-4
P-5
P-18
Check whe he he in e ac ion be ween al e -
na i e ela ionships and ‘excludes’ cons ain s
is co ec ly p ocessed.
A
B C
{A,B},{A,C}
P-4
P-6
37
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01
ID Desc ip ion Inpu Exp. Ou pu Deps.
P-19
Check whe he he in e ac ion be ween ‘ e-
qui es’ and ‘excludes’ cons ain s is co ec ly
p ocessed.
A
BC
{A},{A,C}
P-2
P-5
P-6
P-20
Check whe he he in e ac ion among h ee o
mo e di e en ypes o ela ionships and con-
s ain s is co ec ly p ocessed.
A
B
D E
C
F G
{A,B,D},
{A,B,C,D,F},
{A,B,C,E,F},
{A,B,C,E,F,G}
P-1,
...,
P-19
P-21 Check whe he a p oduc can e oneously in-
clude mo e han one al e na i e ea u es.
B
D
G
A
H
C
F
E
None
P-1
P-4
P-9
P-10
P-17
Table 9: Ope a ion P oduc s. Tes cases
A.4 Ope a ion Numbe o P oduc s
ID Desc ip ion Inpu Exp. Ou pu Deps.
NP-1 Check whe he manda o y ela ionships a e
co ec ly managed by he ope a ion.
A
B
1
NP-2 Check whe he op ional ela ionships a e co -
ec ly managed by he ope a ion.
A
B
2
NP-3 Check whe he o – ela ionships a e co ec ly
managed by he ope a ion.
A
B C
3
38
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01
ID Desc ip ion Inpu Exp. Ou pu Deps.
C-2 Check whe he op ional ela ionships a e co -
ec ly managed by he ope a ion.
A
B
Fea u e=B
50%
C-3 Check whe he o – ela ionships a e co ec ly
managed by he ope a ion.
A
B C
Fea u e=B
66%
C-4 Check whe he al e na i e ela ionships a e
co ec ly managed by he ope a ion.
A
B C
Fea u e=B
50%
C-5
Check whe he ‘ equi es’ cons ain s a e co -
ec ly managed by he ope a ion. Inpu ea-
u e has minimum commonali y.
A
B C
Fea u e=B
33% C-2
C-6
Check whe he ‘ equi es’ cons ain s a e co -
ec ly managed by he ope a ion. Inpu ea-
u e has maximum commonali y.
A
B C
Fea u e=C
66% C-2
C-7 Check whe he ‘excludes’ cons ain s a e co -
ec ly managed by he ope a ion.
A
B C
Fea u e=B
33% C-2
45
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01

ID Desc ip ion Inpu Exp. Ou pu Deps.
C-8
Check whe he he in e ac ion be ween
manda o y and op ional ela ionships is co -
ec ly p ocessed. Inpu ea u e has minimum
commonali y.
A
B
D
C
E
Fea u e=E
50% C-1
C-2
C-9
Check whe he he in e ac ion be ween
manda o y and op ional ela ionships is co -
ec ly p ocessed. Inpu ea u e has maximum
commonali y.
A
B
D
C
E
Fea u e=B
100% C-1
C-2
C-10
Check whe he he in e ac ion be ween
manda o y and o - ela ionships is co ec ly
p ocessed. Inpu ea u e has minimum com-
monali y.
A
B
E F
C D
G
Fea u e=F
66% C-1
C-3
C-11
Check whe he he in e ac ion be ween
manda o y and o - ela ionships is co ec ly
p ocessed. Inpu ea u e has maximum com-
monali y.
A
B
E F
C D
G
Fea u e=B
100% C-1
C-3
C-12
Check whe he he in e ac ion be ween
manda o y and al e na i e ela ionships is
co ec ly p ocessed. Inpu ea u e has mini-
mum commonali y.
A
B
E F
C D
G
Fea u e=G
50% C-1
C-4
46
FaMa Tes Sui e 1.2
ISA Technical Repo ISA-10-TR-01
ID Desc ip ion Inpu Exp. Ou pu Deps.
C-13
Check whe he he in e ac ion be ween
manda o y and al e na i e ela ionships is
co ec ly p ocessed. Inpu ea u e has maxi-
mum commonali y.
A
B
E F
C D
G
Fea u e=B
100% C-1
C-4
C-14
Check whe he he in e ac ion be ween
manda o y ela ionships and ‘ equi es’ con-
s ain s is co ec ly p ocessed.
A
BC
Fea u e=B
100%
C-1
C-5
C-6
C-15
Check whe he he in e ac ion be ween
manda o y ela ionships and ‘excludes’ con-
s ain s is co ec ly p ocessed.
A
BC
Fea u e=B
0% C-1
C-7
C-16
Check whe he he in e ac ion be ween op-
ional and o - ela ionships is co ec ly p o-
cessed. Inpu ea u e has minimum common-
ali y.
A
B
E F
C D
G
Fea u e=G
40% C-2
C-3
C-17
Check whe he he in e ac ion be ween op-
ional and o - ela ionships is co ec ly p o-
cessed. Inpu ea u e has maximum common-
ali y.
A
B
E F
C D
G
Fea u e=C
80% C-2
C-3
47
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ID Desc ip ion Inpu Exp. Ou pu Deps.
C-18
Check whe he he in e ac ion be ween op-
ional and al e na i e ela ionships is co ec ly
p ocessed. Inpu ea u e has minimum com-
monali y.
A
B
E F
C D
G
Fea u e=E
33% C-2
C-4
C-19
Check whe he he in e ac ion be ween op-
ional and al e na i e ela ionships is co ec ly
p ocessed. Inpu ea u e has maximum com-
monali y.
A
B
E F
C D
G
Fea u e=D
66% C-2
C-4
C-20
Check whe he he in e ac ion be ween o -
and al e na i e ela ionships is co ec ly p o-
cessed. Inpu ea u e has minimum common-
ali y.
A
D E
H I
B C
F G
Fea u e=C
25% C-3
C-4
C-21
Check whe he he in e ac ion be ween o -
and al e na i e ela ionships is co ec ly p o-
cessed. Inpu ea u e has maximum common-
ali y.
A
D E
H I
B C
F G
Fea u e=E
80% C-3
C-4
C-22
Check whe he he in e ac ion be ween o -
ela ionships and ‘ equi es’ cons ain s is co -
ec ly p ocessed. Inpu ea u e has minimum
commonali y.
A
B C
Fea u e=B
50%
C-3
C-5
C-6
C-23
Check whe he he in e ac ion be ween o -
ela ionships and ‘ equi es’ cons ain s is co -
ec ly p ocessed. Inpu ea u e has maximum
commonali y.
A
B C
Fea u e=C
100%
C-3
C-5
C-6
48
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ID Desc ip ion Inpu Exp. Ou pu Deps.
C-24
Check whe he he in e ac ion be ween o -
ela ionships and ‘excludes’ cons ain s is co -
ec ly p ocessed.
A
B C
Fea u e=C
50% C-3
C-7
C-25
Check whe he he in e ac ion be ween al e -
na i e ela ionships and ‘ equi es’ cons ain s
is co ec ly p ocessed. Inpu ea u e has min-
imum commonali y.
A
B C
Fea u e=B
0%
C-4
C-5
C-6
C-26
Check whe he he in e ac ion be ween al e -
na i e ela ionships and ‘ equi es’ cons ain s
is co ec ly p ocessed. Inpu ea u e has max-
imum commonali y.
A
B C
Fea u e=C
100%
C-4
C-5
C-6
C-27
Check whe he he in e ac ion be ween al e -
na i e ela ionships and ‘excludes’ cons ain s
is co ec ly p ocessed.
A
B C
Fea u e=B
50% C-4
C-7
C-28
Check whe he he in e ac ion be ween ‘ e-
qui es’ and ‘excludes’ cons ain s is co ec ly
p ocessed. Inpu ea u e has minimum com-
monali y.
A
BC
Fea u e=B
0%
C-2
C-5
C-6
C-7
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ID Desc ip ion Inpu Exp. Ou pu Deps.
C-29
Check whe he he in e ac ion be ween ‘ e-
qui es’ and ‘excludes’ cons ain s is co ec ly
p ocessed. Inpu ea u e has maximum com-
monali y.
A
BC
Fea u e=C
50%
C-2
C-5
C-6
C-7
C-30
Check whe he he in e ac ion among h ee o
mo e di e en ypes o ela ionships and con-
s ain s is co ec ly p ocessed. Inpu ea u e
has minimum commonali y.
A
B
D E
C
F G
Fea u e=G
25%
C-1,
...,
C-29
C-31
Check whe he he in e ac ion among h ee o
mo e di e en ypes o ela ionships and con-
s ain s is co ec ly p ocessed. Inpu ea u e
has maximum commonali y.
A
B
D E
C
F G
Fea u e=B
100%
C-1,
...,
C-29
C-32 Check whe he non-exis en ea u es a e co -
ec ly managed.
A
B
Fea u e=C
0% C-2
C-33 Check whe he a p oduc can e oneously in-
clude mo e han one al e na i e ea u e.
B
D
G
A
H
C
F
E
0%
C-1
C-4
C-12
C-13
C-14
C-25
C-26
Table 12: Ope a ion Commonali y. Tes cases
A.7 Ope a ion Dead Fea u es
50
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ID Desc ip ion Inpu Exp. Ou pu Deps.
DF-1
Check whe he dead ea u es caused by an ‘ex-
cludes’ cons ain s be ween a manda o y ea-
u e and an al e na i e child ea u e a e co -
ec ly de ec ed.
A
BC
D E
D
DF-2
Check whe he dead ea u es caused by he
a ‘ equi es’ cons ain s be ween a manda o y
ea u e and an al e na i e child ea u e a e
co ec ly de ec ed. The pa en ea u e in he
al e na i e ela ionship is manda o y.
A
BC
D E
E
DF-3
Check whe he dead ea u es caused by an ‘ex-
cludes’ cons ain s be ween a manda o y ea-
u e and one o he child ea u es o an o -
ela ionship a e co ec ly de ec ed.
A
BC
D E
D
DF-4
Check whe he dead ea u es caused by an ‘ex-
cludes’ cons ain be ween a manda o y and
an op ional ea u e a e co ec ly de ec ed.
A
BC
C
DF-5
Check whe he dead ea u es caused by an ‘ex-
cludes’ cons ain be ween wo manda o y ea-
u es a e co ec ly de ec ed.
A
BC
A,B,C
DF-6
Check whe he dead ea u es caused by a
‘ equi es’ cons ain be ween wo al e na i e
child ea u es a e co ec ly de ec ed.
A
B C
B
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ID Desc ip ion Inpu Exp. Ou pu Deps.
DF-7
Check whe he dead ea u es caused by an ‘ex-
cludes’ cons ain be ween a pa en and i s
child ea u e a e co ec ly de ec ed.
A
B
A,B
DF-8
Check whe he dead ea u es caused by an ‘ex-
cludes’ and a ‘ equi es’ cons ain s be ween
wo op ional ea u es a e co ec ly de ec ed.
A
BC
B
DF-9
Check whe he dead ea u es caused by he
a ‘ equi es’ cons ain s be ween a manda o y
ea u e and an al e na i e child ea u e a e
co ec ly de ec ed. The pa en ea u e in he
al e na i e ela ionship is op ional.
A
BC
D E
E
Table 13: Ope a ion DeadFea u es. Tes cases
52
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