IMPROVING SOFTWARE PROCESS MATURITY THROUGH
DYNAMIC MODELING AND SIMULATION
Me cedes Ruiz1p, Isabel Ramos2, Miguel To o2
Depa men o Compu e Languages and Sys ems
1 Escuela Supe io de Ingenie ía. Uni e si y o Cádiz (Spain)
2 Escuela Técnica Supe io de Ingenie ía In o má ica. Uni e si y o Se ille (Spain)
Resumen
Los modelos de p ocesos ac uales como CMM, SPICE y o os ecomiendan la
aplicación de con ol es adís ico y de guías de mé icas pa a la de inición,
implemen ación y pos e io e aluación de di e en es mejo as del p oceso. Sin
emba go, p ecisamen e en es e con ex o no se ha conside ado lo su icien e el
modelado cuan i a i o, econocido en o as á eas como un elemen o esencial pa a la
adquisición de conocimien o. En es e abajo se desc ibe la base concep ual y
undamen al u ilizada pa a el desa ollo de un ma co en ocado a la mejo a de
p ocesos so wa e que combina las écnicas de es imación adicionales con la
u ilización ex ensi a de modelos dinámicos de simulación como he amien a pa a
aseso a en el p oceso de e olución en e los di e en es ni eles de madu ez
p opues os po el modelo de e e encia CMM. T as la necesa ia in oducción a los
concep os undamen ales del modelado y simulación del p oceso so wa e y la
jus i icación pa a la c eación de dicho ma co, se abo dan las cues iones
undamen ales pa a su desa ollo, ales como el en oque concep ual y su es uc u a,
p es ando especial a ención al pa adigma de desa ollo de los modelos dinámicos de
simulación que le dan sopo e.
Abs ac
Cu en so wa e p ocess models (CMM, SPICE, e c.) s ongly ecommend he
applica ion o s a is ical con ol and measu e guides o de ine, implemen and
e alua e he e ec s o di e en p ocess imp o emen s. Howe e , whils quan i a i e
modelling has been widely used in o he ields, i has no been conside ed enough in
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he ield o so wa e p ocess imp o emen . Du ing he las decade so wa e p ocess
simula ion has been used o add ess a wide di e si y o managemen p oblems.
Some o hese p oblems a e ela ed o s a egic managemen , echnology adop ion,
unde s anding, aining and lea ning, and isk managemen , among o he s. In his
wo k a dynamic in eg a ed amewo k o so wa e p ocess imp o emen is
p esen ed. This amewo k combines adi ional es ima ion models wi h an in ensi e
u ilisa ion o dynamic simula ion models o so wa e p ocess. The aim o his
amewo k is o suppo a quali a i e and quan i a i e assessmen o so wa e
p ocess imp o emen and decision making o achie e a highe so wa e de elopmen
p ocess capabili y acco ding o he Capabili y Ma u i y Model. The concep s
unde lying his amewo k ha e been implemen ed in a so wa e p ocess
imp o emen ool ha has been used in a local so wa e o ganisa ion. The esul s
ob ained and he lessons lea ned a e also p esen ed in his pape .
1 INTRODUCTION
Dynamic modelling and simula ion as p ocess imp o emen ools ha e been
in ensi ely used in he manu ac u ing a ea. Cu en ly, so wa e p ocess modelling
and simula ion a e gaining an inc easing in e es among esea che s and
p ac i ione s as an app oach o analyse complex business and sol e policy
ques ions.
In his pape an app oach is p oposed ha combines adi ional es ima ion
echniques wi h Sys em Dynamics modelling. The aim o his combina ion is o build
a amewo k o suppo a quali a i e and quan i a i e assessmen o so wa e
p ocess imp o emen and decision making. The pu pose o DIFSPI (Dynamic
In eg a ed F amewo k o So wa e P ocess Imp o emen ) is o help o ganisa ions o
achie e a highe so wa e de elopmen p ocess capabili y acco ding o he Capabili y
Ma u i y Model (Paulk e al. 1993). The dynamic models buil inside his amewo k
p o ide he capabili y o gaining insigh o e he whole li e cycle a di e en le els o
abs ac ion. The le el o abs ac ion used in a ce ain o ganisa ion will depend on i s
ma u i y le el. Fo ins ance, in a le el 1 o ganisa ion he simula o can es ablish a
baseline acco ding o adi ional es ima ion models om an ini ial es ima e o he size
o he p ojec . Wi h his baseline, he so wa e manage can analyse he esul s
ob ained wi h he simula ion o di e en p ocess imp o emen s and s udy he
ou comes o o e o unde es ima e o cos o schedule. Du ing he simula ion me ic
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da a a e sa ed. These da a con o m o SEI co e measu es ecommenda ion
(Ca le on e al. 1992) and a e mainly ela ed o cos , schedule and quali y.
The s uc u e o he pape is as ollows. Sec ion 2 p o ides a b ie o e iew o he
wo k conduc ed in he ield o so wa e p ocess simula ion. In Sec ion 3 he
undamen al basis and s uc u e o his amewo k a e desc ibed. The
implemen a ion and esul s ob ained when applying i inside a local o ganisa ion a e
discussed in Sec ion 4. Finally, Sec ion 5 summa ises he pape and d aws he
conclusions and lessons lea n .
2 SOFTWARE PROCESS SIMULATION
Simula ion can be applied in many c i ical a eas in suppo o so wa e enginee ing. I
enables one o add ess issues be o e hese issues become p oblems. Simula ion is
mo e han jus a echnology, as i o ces one o hink in global e ms abou sys em
beha iou , and abou he ac ha sys ems a e mo e han he sum o hei
componen s (Ch is ie 1999). A simula ion model is a compu a ional model ha
ep esen s an abs ac ion o a simpli ied ep esen a ion o a complex dynamic
sys em. Simula ion models ha e as a main ad an age he possibili y o
expe imen ing di e en managemen decisions.
Thus i becomes possible o analyse he e ec o hose decisions in sys ems
whe e he cos o isks o expe imen a ion make i un easible. Ano he impo an
ac o is ha simula ion p o ides insigh s in o complex p ocess beha iou which is
no possible o be analysed by means o s ochas ic models. Like many p ocesses,
so wa e p ocesses can con ain mul iple eedback loops, such as associa ed wi h
co ec ion o de ec s. Delays esul ing om hese de ec s may ange om minu es o
yea s. The esul ing complexi y makes i almos impossible o men al analysis o
p edic he consequences.
The common objec i es o simula ion models consis on supplying mechanisms o
expe imen , p edic , lea n and answe ques ions such as Wha i ...? A so wa e
p ocess simula ion model can be ocussed on ce ain aspec s o he so wa e
p ocess o he o ganisa ion. I is impo an o bea in mind ha a simula ion model
cons i u es an abs ac ion o he eal sys em, and so, i ep esen s only he pa s o
he sys em ha ha e been in ended o be modelled. Fu he mo e, cu en ly a ailable
modelling ools such as STELLA, POWER-SIM and Vensim help o ep esen he
so wa e de elopmen p ocess as a sys em o di e en ial equa ions. This is a
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ema kable cha ac e is ic as i makes i possible o o malise and de elop a scien i ic
basis o so wa e p ocess modelling and imp o emen . Some no iceable applica ions
o he dynamic app oach o model so wa e p ocess can be ound in (Kellne e al.
1999).
3 DIFSPI STRUCTURE
P ojec managemen is composed o ac i i ies ha a e in ima ely in e ela ed in he
sense o ha a ce ain ac ion pe o med o e a de e mined a ea will possibly a ec
o he a eas. Fo ins ance, a ime delay will always a ec he cos o he p ojec bu i
may o may no a ec he mo ale o he de elopmen eam o he quali y o he
p oduc . The in e ac ions among he di e en a eas o p ojec managemen a e so
s ong ha on some occasions he h oughpu o one o hem can only be achie ed
by educing he h oughpu o ano he . A clea example o his beha iou can be
ound in he equen p ac ice o educing he quali y o he numbe o equi emen s
o be implemen ed in a ce ain e sion o he p oduc wi h he aim o accomplishing
he ime o cos es ima es.
Dynamic models help o unde s and his in eg a ed na u e o p ojec managemen
as hey desc ibe i h ough di e en p ocesses, s uc u es and main
in e ela ionships. In he amewo k p oposed he e, p ojec managemen is
conside ed as a se o dynamic in e ela ed p ocesses. P ojec s a e composed o
p ocesses. Each p ocess is composed o a se ies o ac i i ies designed o he
achie emen o an objec i e (Paulk e al. 1993). F om a gene al poin o iew, i could
be said ha p ojec s a e composed o p ocesses ha all in one o he ollowing
ca ego ies:
− Managemen p ocess. This ca ego y collec s all hose p ocesses ela ed o he
desc ip ion, o ganisa ion and con ol o he p ojec .
− Enginee ing p ocess. All hose p ocesses ela ed o he speci ica ion and
de elopmen ac i i ies o he so wa e p oduc a e collec ed in his ca ego y.
Bo h ca ego ies in e ac du ing he ime cycle o he p ojec . F om an ini ial plan
pe o med by he p ojec managemen p ocesses, enginee ing p ocesses begin o be
execu ed. Using he in o ma ion ga he ed abou he p og ess o his second g oup o
p ocesses, p ojec managemen p ocesses de e mine he modi ica ions which need
o be made o he plan in o de o achie e he p ojec objec i es. The DIFSPI
p oposed ollows his same classi ica ion and i is s uc u ed a ending o p ojec
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managemen and enginee ing p ocesses. In bo h le els, he u ilisa ion o dynamic
models o simula e he eal p ocesses and o de ine and de elop a his o ical
da abase will be he main ea u e
The enginee ing p ocesses in he DIFSPI he dynamic models simula e he li e
cycle o he so wa e p oduc . The bene i s ha simula ion p o ides a his le el a e
he ollowing:
− To build a model i is necessa y o imp o e he knowledge one has abou he
so wa e de elopmen p ocess as i is equi ed o es ablish he limi s and he
scope o hose eal beha iou s o be modelled and simula ed.
− The pa ame e s equi ed by he model and he ables ha de e mine i s ime
beha iou will cons i u e he main elemen s o a me ics collec ion p og am o
de ine a his o ical da abase.
− The e ec i e applica ion o his me ics p og am will eed he da abase.
The his o ical da a ga he ed will assess in he alida ion and calib a ion o he
model.
− The dynamic model will inally simula e he so wa e p ocesses wi h he
knowledge and he ma u i y ha he o ganisa ion has a he momen .
− The u ilisa ion o he dynamic model allows he es ablishmen o a baseline o he
p ojec , he in es iga ion o possible imp o emen s, and he de elopmen o a
his o ical da abase which can be ed ei he by eal o simula ed da a.
The dynamic models o his le el a DIFSPI should ollow he le els o isibili y and
knowledge o he enginee ing p ocesses ha o ganisa ions ha e a each ma u i y
le el. I is ob ious ha he complexi y o he dynamic model used in le el 1
o ganisa ions canno be he same as ha one o he models capable o simula ing
he enginee ing p ocesses o , o ins ance, le el 4 o ganisa ions.
Managemen p ocesses a e di ided in o wo main ca ego ies:
− Plan. I g oups he p ocesses de o ed o he design o he ini ial plan and he
equi ed modi ica ions when he p og ess epo s indica e he appea ance o
p oblems. The models o his g oup in eg a e adi ional es ima ion and planning
echniques oge he wi h dynamic ones.
− Con ol. In his g oup all he models designed o he moni o ing and acking
ac i i ies a e ga he ed. These models will also ha e he esponsibili y o
de e mining he co ec i e ac ions o he p ojec plan. The e o e, he simula ion o
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p ocess imp o emen s will be o an eno mous impo ance.
4 DIFSPI UTILISATION
The po en ial applica ions o he DIFSPI ha e al eady been men ioned in he o me
sec ions. In his sec ion some o he da a ob ained when DIFSPI was applied inside a
local so wa e de elopmen o ganisa ion a e p o ided. This local o ganisa ion could
be placed a le el 1. A i s he so wa e p ocess capabili y o his o ganisa ion was
unp edic able because i was cons an ly changed o modi ied as he wo k
p og essed. Pe o mance depended on bo h he capabili ies o he p ojec manage
and he echnical eam. Mo eo e , he e we e ew s able so wa e p ocesses in
e idence. Acco ding o Le el 1 o ganisa ions, he so wa e p ocess he e was
pe cei ed as an amo phous en i y, ‘a black box’, and isibili y in o he p ojec 's
p ocesses was e y limi ed. Requi emen s lowed in o he so wa e p ocess in an
uncon olled manne , gi ing a p oduc as a esul . The pu pose o his applica ion
was o insu e ha he amewo k could ep oduce he beha iou obse ed in a eal
p ojec and, he e o e, could igge a me ics collec ion p og am, and help in decision
making, p edic ing and cos es ima ing. Table 1 shows he cha ac e is ics o he
p ojec ha was simula ed o his case s udy oge he wi h he da a o he baseline
epo ed by he simula ion. I should be no ed he e ha he da a epo ed by he
simula ion con o ms he co e measu es ecommended by he So wa e Enginee ing
Ins i u e (SEI) (Ca le on e al. 1992).
Size o he p ojec = 80,000 LOC
REAL DATA SIMULATED DATA
Time 250 days Time 263 days
Ini ial Wo k o ce 8 echnician E o 4,361 echnician-day
E o 4,780 echnician-day Quali y 80% ( asks e ised)
Wo k o ce 9 echnician
Table 1: Real and simula ed da a o he case s udy
The scena io shown in Table 2 helps o analyse he impac o he size o he
echnical s a o e he main ou a iables ( ime, e o , quali y, and o e all
wo k o ce). Two di e en cases we e simula ed. The i s one (CASE 1) had a
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schedule o 250 days and 16 pa - ime echnicians. The second case (CASE 2) had a
schedule o 150 days and 16 ull- ime echnicians.
The expec ed beha iou o p ojec s wi h a high le el o pe sonnel is ha he a e age
p oduc i i y pe echnician achie ed will be lowe . The a e age p oduc i i y pe
echnician in he baseline was 0.8926 asks/( echnician*day). CASE 1 and 2 bo h
had he double ini ial wo k o ce han ha o he baseline, al hough schedules and
esou ce alloca ion we e di e en be ween hem. The a e age p oduc i i y ob ained
o case 1 and 2 was, espec i ely, 0.8277 asks/( echnician*day) and 0.8142
asks/( echnician/day).
CASE 1 CASE 2
Time 135 days Time 140 days
E o 1,396 echnician-day E o 3,596 echnician-day
Quali y 91% Quali y 91%
Wo k o ce 18 echnician Wo k o ce 16 echnician
Table 2: Simula ed da a o scena io analysis
5 CONCLUSIONS
Mo i a ed by lessons lea n om ano he Sys em Dynamics applica ion in an
indus ial en i onmen , he de elopmen o a amewo k o combine he adi ional
es ima ion ools wi h he dynamic app oach has been ini ia ed. The main objec i e o
his dynamic amewo k is o assess p ojec manage s and membe s o he SEIG o
de ine, e alua e and implemen p ocess imp o emen s o achie e highe le els o
ma u i y. The whole p ocess o de elopmen o he amewo k also helps o design a
speci ic me ics collec ion p og am which, once implemen ed, con ibu es o build and
eed a his o ical da abase inside an o ganisa ion.
Wi h he applica ion o DIFSPI in a le el 1 o ganisa ion impo an bene i s we e
ob ained. Fi s , i mus be men ioned ha du ing he p ocess o model building, he
p ojec manage gained much new insigh in o hose aspec s o he de elopmen
p ocess ha mos ly in luence he success o he p ojec ( ime, cos and quali y).
Second, ha ing he possibili y o gaming wi h he DIFSPI, i allowed him o be e
unde s and he unde lying dynamics o he so wa e p ocess. As a consequence,
se e al p ocess imp o emen sugges ions we e easily designed and, mos
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impo an ly, analysed using simula ion o scena ios. Finally, empla es and guidelines
o a me ics collec ion p og am we e almos au oma ically de i ed om he
equi emen s o he dynamic modules.
Ou u u e wo k will mainly concen a e on esea ch owa ds a ull de elopmen o he
dynamic modules ha implemen he key p ocess a eas o he highe ma u i y le els.
Once his de elopmen has been accomplished i is in ended o alida e he comple e
DIFSPI in eal indus ial en i onmen s.
REFERENCES
1. Paulk, M., Ga cia, S.M., Ch issis, M.B., Bush, M., 1993. Key p ac ices o he
capabili y ma u i y model. Ve sion 1.1 Technical Repo CMU/SEI-93-TR-25.
So wa e Enginee ing Ins i u e, Ca negie Mellon Uni e si y, Pi sbu g, PA.
2. Ca le on, A., Pa k, R.E., Goe he , W.B., Flo ac, W.A., Bailey, E.K., P leege , S.L.,
1992. So wa e measu emen o DoD sys ems: ecommenda ions o ini ial co e
measu es. Technical Repo CMU/SEI-92-TR-19. So wa e Enginee ing Ins i u e,
Ca negie Mellon Uni e si y, Pi sbu g, PA.
3. Ch is ie, A.M., 1999. Simula ion in suppo o CMM-based p ocess imp o emen .
The Jou nal o Sys ems and So wa e, 46, (1999), 107-112.
4. Kellne MI, Madachy R, Ra o D. So wa e p ocess simula ion modeling: Why?
Wha ? How? The Jou nal o Sys ems and So wa e, 46, (1999), 91-105.
CORRESPONDENCE
Me cedes Ruiz Ca ei a
Dp o. de Lenguajes y Sis emas In o má icos
E.S. de Ingenie ía
C/ Chile, nº1
11003 - Cádiz (Spain)
Phone: +34 956 015 714 Fax: +34 956 015 139
e-mail: [email p o ec ed]
Acknowledgemen s
The au ho s wish o hank o Comisión In e minis e ial de Ciencia y Tecnología,
Spain, (unde TIC2001-1143-C03-02) o suppo ing his esea ch e o .
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