A Da a Mining Me hod o Suppo Decision Making
in So wa e De elopmen P ojec s
J.L. ´
Al a ez and J. Ma a
Dp . Ing. Elec ´onica, Sis . In o m´a icos y Au om´a ica
Uni e sidad de Huel a
al a ez, ma a
g
@uhu.es
J.C. Riquelme and I. Ramos
Dp . Lenguajes y Sis emas In o m´a icos
Uni e sidad de Se illa
iquelme, amos
g
@lsi.us.es
Key wo ds: Knowledge Disco e y in Da a, Da a Mining, Dynamic Models, So wa e Enginee ing.
Abs ac : In his pape , we p esen a s a egy o induce knowledge as suppo decision making in So wa e De elopmen
P ojec s (SDP). The mo i e o his wo k is o educe he g ea quan i y o SDP do no mee he ini ial cos
equi emen s, deli e y da e and he quali y o he inal p oduc . The main objec i e o his s a egy is o
suppo he manage in he decision aking o es ablish he policies om managemen when beginning a
so wa e p ojec . Thus, we apply a da a mining ool, called ELLIPSES, on da abases o SDP. The da abases
a e gene a ed by means o he simula ion o a dynamic model o he managemen o SDP. ELLIPSES ool
is a new me hod o ien ed o disco e knowledge acco ding o he expe ’s needs, by he de ec ion o he
mos signi ican egions. The me hod essence is ound in an e olu iona y algo i hm ha inds hese egions
one a e ano he . The expe decides which egions a e signi ican and de e mines he s op c i e ion. The
ex ac ed knowledge is o e ed h ough wo ypes o ules: quan i a i e and quali a i e models. The ool also
o e s a isualiza ion o each ule by pa allel coo dina e sys ems. In o de o p esen his s a egy, ELLIPSES
is applied o a da abase which has al eady been ob ained by means o he simula ion o a dynamic model on a
p ojec concluded.
1 INTRODUCTION
Cu en ly, many So wa e De elopmen P ojec s
(SDP) do no mee he ini ial cos equi emen s, de-
li e y da e and he quali y o he inal p oduc . The
eason o his si ua ion is he g ea quan i y o a -
ibu es ha in luence on he de elopmen p ocess,
whose alues should be es ablished by he manage
o he p ojec . These alues depend on he di e en
managemen policies as well as he ma u i y le el o
he o ganiza ion o de elopmen .
In he adi ional me hod, he manage akes a de-
cision o he alues o hese a ibu es acco ding o
his expe ience, he ini ial a ailable esou ces and he
equi emen s o he p ojec . This decision is a e y
di icul ask because i is necessa y o decide each a -
ibu e indi idually and u he mo e o bea in mind
he a ibu es al oge he .
The simula ion o dynamic models o he manage-
men o SDP (Abdel-Hamid and Madnick, 1991) p o-
duces an imp o emen as opposi e o adi ional s a ic
models. The dynamic models allow o analyze, be-
o e beginning hede elopmen , he esul o he man-
age ’s decision. Howe e , i he manage has unce -
ain y abou many a ibu es hen many simula ions
will be necessa y and he canno make an exhaus i e
analysis o he p ocess.
Da a mining me hod can be used o sol e his p ob-
lem (Aguila e al., 2001). Da a mining is a machine
lea ning p ocess ha induce pa e ns om da abases
(Chen e al., 1996; Fayyad e al., 1996).
Thus, a da abase can be gene a e h ough he sim-
ula ion o a dynamic model o he de elopmen p o-
cess. Each ins ance o he da abase is composedo he
a ibu es (pa ame e s) used on he simula ion and he
a ibu es ( a iables) ob ained om he simula ion. A
da a mining me hod can be applied o his da abase.
In his pape , we p esen an o e iew o his p o-
cess. Thus, we o e he esul s a e applying ou da a
mining ool, called ELLIPSES ( ´
Al a ez e al., 2002),
on a da abase. This da abase has been gene a ed us-
ing he dynamic model o he managemen o SDP
desc ibed in (Ramos and Riquelme, 1999).
The emaining pa o he pape is o ganized as ol-
lows. In sec ion 2, a b ie desc ip ion o he use o he
dynamic model on SDP is in oduced. In sec ion 3,
he s a egy o apply da a mining me hods on SDP is
gi en. In sec ion 4, he ELLIPSES ool is desc ibed.
1
ICEIS 2003 -
The case s udy and expe imen al esul s a e desc ibed
in sec ion 5 and he pape is conclude in sec ion 6.
2 DYNAMIC MODELS AND
SOFTWARE ENGINEERING
A he beginning o he 90’s a signi ican e en
ook place in so wa e enginee ing ield: he use o
he i s dynamic model (Abdel-Hamid and Madnick,
1991) applied o SDP. In he las yea s, new dynamic
models o SDP and powe ul simula ion en i on-
men s (S ella, Vensim, iThink, Powe Sim, e c.) ha e
s ongly suppo ed he complex p ocess o decision
making. The po en ial o he simula ion o he dy-
namic models o he o ma ion and aining o he
manage o p ojec s has been p o ed in (Ruiz e al.,
2001; Dhiel, 1991; G aham e al., 1992). These sys-
ems o e o he manage , wi hou isks, he impac
ha can ha e on a p ojec he applica ion o manage-
men policies.
Thus, he simula ion o so wa e p ojec s by dy-
namic models can be used o accomplish:
A p io i analysis: he simula ion o he p ojec is
made be o ebeginning he de elopmen . This anal-
ysis gua an ees he li e cycle o he p ojec apply-
ing he analyzed policies.
Moni o ing analysis: he simula ion o he p ojec is
made du ing he de elopmen p ocess. This analy-
sis pe mi s o compa e he eal s a e p ojec wi h
he esul s o he simula ion.
Pos -mo em analysis: he simula ion is made a e
he de elopmen p ocess. This analysis helps o im-
p o e he esul s on u u e p ojec s.
Ob iously, he g ea quan i y o si ua ion ha he
manage needs o simula e in a p ojec p e en s o
make an analy ical exhaus i e. Thus, he manage
only will be able o accomplish some simula ions. In
he nex sec ion, a solu ion o his p oblem is gi en.
3 DATA MINING AND
SOFTWARE ENGINEERING
Da a Mining me hods ha e been on many ields o -
e ing excellen esul s. Howe e , he e a e no many
esea che s using hese me hods on he so wa e en-
ginee ing ield. A possible con ibu ion o he da a
mining on he so wa e enginee ing is he imp o e-
men o he de elopmen p ocess. Thus, da a mining
algo i hms analyze de elopmen p ocess da abases o
induce a se o managemen ules.
This s a egy has an incon enience: he e a e no
many eal da abases o de elopmen p ocess. This
Figu e 1: P ocess o induce managemen ules om so -
wa e de elopmen p ojec s.
p oblem can be sol ed using a simula ion en i on-
men wi h a dynamic model o he managemen SDP.
Thus, he a ibu es wi h ce ain unce ain y le el
a e selec ed by he manage acco ding o his expe-
ience, he equi emen s o he p ojec and he man-
agemen policies. Fo each one o hem, he manage
chooses a ange, since he do no know ha he alue
o a a ibu e will be 37, bu know ha will be be-
ween 30 and 40. The simula ion en i onmen will
choose a andomly alue o hese a ibu es and will
simula e he p ojec gene a ing he esul s o cos , de-
li e y da e and quali y o he inal p oduc . This p o-
cess is epea ed un il he da abase has he necessa y
ins ances. Figu e 1 shows he sequence o his p oce-
du e. Finally, a da a mining algo i hm is applied on
his da abase ob aining a se o managemen ules.
The manage can be use he in e es ing knowledge
ha hese ules o e . The e a e some c i e ia in o de
o choose he ules:

To choose he ules ha co e mo e ins ances.

To choose he ules wi h less a ibu es.

I i is a pos -mo em analysis, o choose he ules
o hose he ange o each a ibu e con ains he
ini ial alue.

I i is a p io i analysis, o choose he ules whose
a ibu es can be con olled easily.

To choose he ules ha o e he bes esul s.
3.1 Adjus men o he s a egy
The desc ibed s a egy p e iously has a p oblem i
we wan o apply ou ool. A da abase gene a ed ac-
co ding o he p e ious p ocedu e is composed by a -
ibu es: pa ame e s (inpu a ibu es) and a iables
(ou pu a ibu es), whose alues a e all con inuous.
Howe e , ELLISPES ool needs a aining se whose
a ibu es a e con inuous alues, bu he class is a ca -
ego ical alue.
In o de o sol e his p oblem, he con inuous a i-
ables should be ans o med in only one ca ego ical
a ibu e using he expe ience o he p ojec manage .
This way, he manage needs o es ablish he max-
imum alues o each ou pu a ibu e (cos , deli e y
da e and quali y), in such a way ha i hese alues
2
A Da a Mining Me hod o Suppo Decision Making
in So wa e De elopmen P ojec s
Figu e 2: G aphical ep esen a ion o an hipe ellipse a) wo
b) h ee dimen ional.
a e su passed he p ojec is conside ed as no accep -
able. In o wa d, each se o maximum alues o he
a iables will be deno ed ”cu ing sec ion”.
Thus, each ”cu ing sec ion” p oduces a di e en
aining se on he gene a ed da abase, whe e he in-
s ances whose ou pu a ibu es su pass hose alues
a e conside ed as ”good”, and he ins ances whose
ou pu a ibu es main ain hose alues a e conside ed
as ”no good”. The ”good” ins ances a e labelled wi h
”G” and he ”no good” ins ances a e labelled wi h
”N”. The new aining se s a e composed o he inpu
a ibu es and he new class.
4 ELLIPSES TOOL
ELLIPSES ool is a da a mining me hod o induce
in e es ing egions in da abases. The co e o he al-
go i hm is a e olu iona y p ocess (Goldbe g, 1989;
Holland, 1975; Michalewicz, 1999) whose indi idu-
als a e ellip ical su ace in he sea ch space.
The esul s a e shown by quan i a i e and quali a-
i e ules, and a isualiza ion by pa allel coo dina es
sys ems is also shown.
In he nex sec ions, he p elimina y de ails, a b ie
desc ip ion o he algo i hm and he isualiza ion a e
desc ibed.
4.1 P elimina ies
Ou me hod uses conical egions o ind he mos sig-
ni ican ules. These egions con ain he ea u es o
each class. This sec ion o e s he basic de ini ions o
he models o ules used in ou ool.
An hype ellipse ( he w appe ) is equal o an el-
lipse o ci cum e ence in a wo-dimensional space
R
2
. An hype ellipsoid ( he w apped olume) is equal
o an ellipsoid o ci cle in a wo-dimensional space
R
2
. Figu e 2 o e s a g aphical ep esen a iono hese
concep s. Figu e 2a) ep esen s an ellipse o cen e
(

1
; 
2
)
, g ea e axis
a
1
and smalle axis
a
2
o wo
a ibu es
x
1
and
x
2
( wo-dimensional space
R
2
) and
igu e 2b) shows an hype ellipse o h ee a ibu es
x
1
,
x
2
and
x
3
( h ee-dimensional space
R
3
).
(
x
1


1
)
2
a
2
1
+
(
x
2


2
)
2
a
2
2
= 1
(1)
(
x
1


1
)
2
a
2
1
+
(
x
2


2
)
2
a
2
2

1
(2)
(
x
1


1
)
2
a
2
1
+
(
x
2


2
)
2
a
2
2
+
:::
+
(
x
d


d
)
2
a
2
d

1
(3)
The equa ion o he ellipse in
R
2
is shownin 1. The
equa ion o an ellipsoid is shown in 2. This equa ion
is ob ained changing
=
by

in he equa ion o he
associa ed ellipses. Gene alizing, in
R
d
, he equa ion
o an hype ellipsoid is shown in 3.
I x
1
(

1
; a
1
)
and ::: and x
d
(

d
; a
d
)
)
C
i
(4)
h
(
x
i
; a
i
) =
8
>
>
>
<
>
>
>
:
L
a g e i a
i
>
40%
A
x
ML
a g e i
25%
A
x
< a
i

40%
A
x
M
edium i
15%
A
x
< a
i

25%
A
x
MS
ho i
5%
A
x
< a
i

15%
A
x
S
ho i a
i

5%
A
x
(5)
I x
1
(

1
; E
1
)
and ::: and x
d
(

d
; E
n
)
)
C
i
(6)
The models o he ules (quan i a i e and quali a-
i e)used in ou ool a ebased on 1, 2 and 3. Thus, he
quan i a i e model is ob ained di ec ly by he equa-
ion o he ellipse. This model is shown in 4 and i
o e s he cen al

i
alue and he ex en (wid h)
a
i
o
each a ibu e, and he associa ed class
C
i
. The quali-
a i e model uses i e labels o speci y he ex en . Fo
each a ibu e
x
i
, a
E
j
label is gene a ed by
h
(
x
i
; a
i
)
unc ion, acco ding o 5, whe e
A
x
is
x
iM

x
im
,
x
iM
is he maximum and
x
im
he minimum o
x
i
a ibu e. The quali a i e model is shown in 6. The
in e p e a ion o hese models o ule is e y in ui i e
because he ule does no di e om he ypical clas-
si ica ion ules. Thus, le be
: (
y
1
; y
2
; :::; y
n
)
, i
y
i
2
[
x
i

a
i
; x
i
+
a
i
℄
8
i
hen he i em
is associa ed
wi h he class
C
i
, acco ding o 4. In he quali a i e
model, he label es ablishes he di e ence be ween
y
i
and
x
i
.
The
C
i
o an hype ellipsoid is assigned as ollows.
Le be
: (
x
1
; x
2
; :::; x
d
; C
i
)
i em, i
i
sa is ies he
equa ion 3 hen he i em is wi hin he olume o he
hype ellipsoid. Thus, he majo i y class wi hin he
hype ellipse is he associa ed class o i .
4.2 E olu iona y P ocess
The main objec i e o ou ool is o induce he e-
gions o he sea ch space wi h a g ea e numbe o
3
ICEIS 2003 -
ELIPSES Algo i hm
1. T
Read T aining se
2. Repea
3. i e
i e + 1
4.
P
i
Ini ialize popula ion on T
5. Repea
6. E alua e
P
i
on T
7. Selec he bes in
P
i
o
P
i
+1
8. Selec 10% in
P
i
o
P
i
+1
9. C osso e
P
i
indi iduals o
P
i
+1
10. Mu a e
P
i
+1
11.
P
i
+1
is
P
i
12. Un il numbe gene a ions
13.
Selec he bes o
P
i
14. i alpha( )
>
ALPHA THEN add(R, )
15. Un il (i e =ITER o be a( )
>
BETA)
16. Show R ules
17. Visualiza ion R by Pa allel Coo dina es
END.
Figu e 3: ELLIPSES Algo i hm.
he ins ances belonging o he same class and o pe -
mi he human-expe in e ac ion in o de o es ablish
some c i e ia o he sea ch p ocess. The inal esul
shows a educed and easie in e p e able se o ules.
ELLIPSES is based on E olu iona y Algo i hm (EA).
EA a e a heu is ic sea ch echnique ha has demon-
s a ed o be obus o a a ie y o complex sea ch
space.
Figu e 3 shows he ELLIPSES algo i hm. I e a-
i ely, he EA inds he bes hype ellipse
based on
he numbe o posi i e and nega i e i ems in he hy-
pe ellipsoid. Le be
al pha
(
)
he pe cen age o he
same class i ems in
, i
al pha
(
)
is g ea e han
he p ede ined human-expe pe cen age
ALP H A
,
hen egion
is conside ed. This p ocess is epea ed
un il eaching a p ede ined human-expe numbe o
ules o p ede ined human-expe pe cen age
B E T A
,
whe e
be a
(
)
is he numbe o co e ed cases. Fi-
nally, he ules a e shown acco ding o 4 and 6 (quan-
i a i e and quali a i e models), and hey a e shown
by pa allel coo dina e sys ems.
4.2.1 Rep esen a ion
An indi idual (a easible solu ion) is a se
I
=

1
,...,

d
,
a
1
,...,
a
d
g
whe e
d
is he numbe
o a ibu es and

i
; a
i
2 <
a e, espec i ely, he
cen e and ex en o he
x
i
a ibu e and hey ep esen
he equa ion o an hype ellipsoid acco ding o 3.
Figu e 4 shows a g aphical ep esen a ion o he
indi iduals.
In p ac ice, an indi idual ep esen s a sea ch space
egion. Each egion will be associa ed o a class ha
will be deduced by he majo i yclass o he da a i ems
Figu e 4: Rep esen a ion o an indi idual in he ELLIPSES
e olu iona y algo i hm.
in he hype ellipsoid.
4.2.2 Fi ness unc ion
The i ness (o me i as a solu ion) o an indi idual is
ob ained by aining se i em analysis. An i em can
be in o ou o he hype ellipse. The ou i ems a e
igno ed. The di e en classes o he i ems in he hy-
pe ellipse a e coun ed and he associa ed class o he
indi idual is he majo i y class. Thus, he i ems wi h
he same class a e posi i e cases and he i ems wi h
di e en classes a e nega i e cases.
Fu he mo e, nex i e a ion mus di ec he e olu-
iona y p ocess o o he egions. Thus, he posi i e
cases co e ed by disco e ed ules a e conside ed co -
e ed cases. Finally, ou me hod needs o ob ain he
g ea es egion. Thus, he ampli ude o he hype -
ellipse is he hype ellipse olume di ide by sea ch
spaces olume.
(
i
) =
P os
(
i
)

N eg
(
i
)

C o e
(
i
)

F C
+
Ampl
(
i
)
(7)
Ou algo i hm maximizes he i ness unc ion
o
each indi idual
i
. The i ness unc ion is gi en in 7,
whe e
P os
(
i
)
and
N eg
(
i
)
a e he posi i e and nega-
i e cases in he hype elipsoid ha ep esen he in-
di idual i,
C o e
(
i
)
a e he co e ed cases by p e-
ious hype ellipses,
F C
is he co e u e ac o and
Ampl
(
i
)
is he hype ellipse ampli ude. Co e u e
ac o (
F C
) is a alue in he in e al
[0
::
1℄
, and i o -
e s he possibili y o elaxing he co e ed cases, so,
i
F C
is closed o 1, hen he co e ed cases a e con-
side ed nega i e cases, and i
F C
is closed o 0, hen
he co e ed cases a e igno ed.
4.2.3 Gene ic Ope a o s
The e a e ee gene ic ope a o s: selec ion, c osso e
and mu a ion. To o m a new popula ion ( he nex
gene a ion), he indi iduals a e selec ed acco ding o
hei i ness by he selec ion ope a o . Many selec ion
p ocedu es a e cu en ly in use, ou algo i hm uses
oule e wheel p ocedu e, whe e indi iduals a e se-
lec ed wi h a p opo ional p obabili y o hei ela i e
i ness. This ensu es ha an indi idual is chosen in a
expec ed numbe o imes app oxima elyp opo ional
o i s ela i e pe o mance in he popula ion. Thus,
high- i ness (good) indi iduals s and a be e chance
4
A Da a Mining Me hod o Suppo Decision Making
in So wa e De elopmen P ojec s
Figu e 5: The middle poin c osso e ope a o in he EL-
LIPSES e olu iona y algo i hm.
Figu e 6: The uni o m poin c osso e ope a o in he EL-
LIPSES e olu iona y algo i hm.
o selec ing, while low- i ness indi iduals a e mo e
likely o disappea .
Selec ion canno in oduce any new indi iduals
in o he popula ion. These indi iduals a e gene a ed
h ough c oss-o e and mu a ion ope a o s. C osso e
ope a o is pe o med by selec ing wo indi iduals
called pa en s, and gene a ing new indi iduals called
o sp ing. Inou algo i hm, he c osso e ope a o has
wo componen s: he middle poin c osso e and he
uni o m c osso e . They a e pe o med wi h a p ob-
abili y
p
 oss
ha chooses be ween he middle poin
c osso e and he uni o m c osso e . The middle
poin c osso e andomly spli s he indi iduals in wo
pa s. Then he agmen s a e exchanged gene a ing
wo new indi iduals. Figu e 5 g aphically shows his
p ocess. The uni o m c osso e decides , indepen-
den ly o each coe icien o an indi idual, whe he i
con ibu e o no o he new indi idual. An example
o his p ocedu e is shown in igu e 6.
ij
=
ij

Quan

P e M u

ij
(8)
Finally, he mu a ion ope a o is in oduced o p e-
en p ema u e con e gence o local op imum by an-
domly sampling newpoin s in he sea ch space. Th ee
a ian s a e implemen ed: cen e mu a ion, ampli-
ude mu a ion and ex eme mu a ion. Mu a ion is pe -
o med wi h p obabili y
p
mu
on an indi idual. When
an indi idual mus be mu a ed, a p obabili y chooses
be ween he di e en ope a o s. The cen e and am-
pli ude mu a ion ope a o s al e he cen e (

1
; :::; 
d
)
and he ex en (
a
1
; :::; a
d
) o he hype ellipse, espec-
i ely, acco ding o 8, whe e
ij
is he ac o o al-
e ,
Quan
and
P e M u
ake hei alues om[0..1],
Quan
is he andom quan i y ha
ij
is al e ed and
P e M u
is he pe cen age o mu a ion ha de e -
mines how he mu a ion in luence on
ij
. The ex-
eme mu a ion ope a o al e s bo h cen e (

i
) and
ex en (
a
i
) o an a ibu e (
x
i
). Thus, he mu a ion le
he middle alue o
x
iM

x
im
o

i
and le
x
iM

x
im
2
o
a
i
. The objec i e o his ope a o is o co e he
a ibu e.
Figu e 7: ELLIPSES Pa allel Coo dina e Sys ems.
4.3 Pa allel Coo dina e Sys ems
Al hough ou ool o e s wo models o ules and he
quali a i e model is easily in e p e ed, some imes i
is necessa y o p o ide he in o ma ion using ano he
philosophy. Thus, a isualiza ion o he ela ionships
among he a ibu es o e s a good suppo o he ex-
pe . The isualiza ion echnique used in ou algo-
i hm is shown in his sec ion. This echnique o e s
he ela ionships among a ibu es by pa allel coo di-
na es (Inselbe g, 1985).
A pa allel coo dina e sys em is composed by a se
o pa allel axes sepa a ed by a ixed dis ance. Each
axis co esponds wi h an a ibu e and hey a e esca-
la ed on he ange o he a ibu e. Thus,
d
axes a e
necessa y o ep esen
d
a ibu es. In his sys em, a
line ep esen s each da a i em. This line in e sec s
wi h each axis on he alue o he i em o ha a -
ibu e. Figu e 7a) shows he adi ional pa allel coo -
dina e sys em.
In ou me hod, each egion is ep esen ed on a pa -
allel coo dina e sys em. Bu , all da a i ems in a e-
gion a e no ep esen ed on pa allel coo dina e sys-
em. Thus, only he minimal alue and he maximal
alue, o each a ibu e, a e ep esen ed on each axis
and hese alues a e joined by illed polygonal. Fig-
u e 7b) o e s an example o his me hod. The in e nal
lines a e elimina ed. The objec i e o his a ian is o
o e a clea e and compac ision o he ela ionships
be ween he a ibu es.
5 CASE STUDY
In his case s udy, we will analyze he in luence o
he policieso pe sonnelmanagemen on he a iables
o a SDP wi h es ic ions in he deli e y ime.
Thus, he used a ibu es on his s udy a e: he a -
e age delay o he new echnicians’ adap a ion ha
has inco po a ed o he p ojec ( echnicians’ in eg a-
ion), he a e age delay o he echnicians’ exi o
he p ojec ( echnicians’ discha ge), he a e age delay
o he new echnicians’ inco po a ion in he p ojec
( echnicians’ ec ui ing) and he maximum delay on
he deli e y ime. Table 1 shows mo e in o ma ion
5

ICEIS 2003 -
Table 1: SDP a ibu es
A ibu es Desc ip ion uni ini ial alue ange
inpu s
ASIMDY (A) The a e age delay o he new echnicians’ adap a ion ha has inco po-
a ed o he p ojec
days 20 [5,15]
HIREDY (H) The a e age delay o he new echnicians’ inco po a ion in he p ojec days 30 [5,10]
MXSCDX (M) The maximum delay on he deli e y ime % 1.16 [1, 1.2]
TRNSDY (T) The a e age delay o he echnicians’ exi o he p ojec days 10 [5,10]
ou pu s
JBSZMD Necessa y e o o ca y ou he p ojec echnicians-days 1111 -
SCHCDT De elopmen ime days 320 -
ANERPT Final p oduc quali y e o s/ asks 0 -
Table 2: ”Cu ing sec ion” on CRCCRT da abase
CRCCRT JBSZMD SCHCDT ANERPT
Cu ing sec ion 1 -

352
(10%)

0
:
45
(0.45%)
Cu ing sec ion 2 -

352
(10%)

0
:
35
(0.35%)
abou hese a ibu es. Fu he mo e, his able shows
he a iables ha will be analyzed: deli e y ime, cos
and quali y o he inal p oduc .
The da abase, ha we will call CRCCRT, has
been gene a ed using a quick ec ui ing wi h e-
s ic ion on he deli e y ime s a egy1. Thus,
he a ibu es ela ed wi h pe sonnel managemen
(ASIMDY, HIREDY and TRNSDY) ake alue inside
he in e al conside ed as quick and he a ibu e e-
la ed wi h he deli e y ime managemen (MXSCDX)
akes alues inside he in e als o ixed e m and
mode a e e m.
In his s udy has been analyzed wo ”cu ing sec-
ions” o he CRCCRT da abase. The alues o he
a iables o each ”cu ing sec ion” a e shown in a-
ble 2. The objec i es o each ”cu ing sec ion” a e
di e en acco ding o he used alues. Thus, he ob-
jec i e o he cu ing sec ion 1 is o induce manage-
men ules whe e he deli e y ime canno o e come
a he es ima ed (320 days) in mo e han 10% (352
days) and quali y o he p ojec canno g ea e han
0.45 e o s/ asks. The objec i e o he cu ing sec ion
2 is equal conce ning deli e y ime, bu he e a e e-
s ic ions s onge conce ning quali y. Tha is o say,
he inal p oduc quali y canno g ea e han 0.35 e -
o s/ ask. This new es ic ion will educe he numbe
o ins ances and, consequen ly, he eliabili y o he
induced knowledge, bu , howe e , he quali y o he
inal p oduc will be g ea e han in he p e ious cu -
ing sec ion.
1A quick ec ui ing s a egy has been analyzed since
some esea ches (Ramos and Ruiz, 1998) ha e shown ha
he ul illmen o he deli e y ime is a o ed by hese poli-
cies hough he cos could be inc eased.
Figu e 8: Visualiza ion by pa allel coo dina es o he man-
agemen ules induced om cu ing sec ion 1.
5.1 Cu ing sec ion 1: es ic ions o
ime and quali y
I a da a mining me hod, e.g. ELLIPSES, is applied
o he aining se gene a ed by he cu ing sec ion
1, hen a se o ules o he managemen o SDP
can be ob ained. These ules will o e in o ma ion
on he policy o pe sonnel managemen (ASIMDY,
HIREDY, TRNSDY) and he maximum pos pone-
men o he deli e y ime o he p ojec (MXSCDX)
when he objec i e is o ob ain a inal p oduc wi h
a deli e y ime (SCHCDT) less han 352 days and a
quali y (ANERPT) less han 0.45 e o / asks; ega d-
less o he cos o he necessa y e o o ca y ou he
p ojec ( able 2, cu ing sec ion 1).
The ules induced by ELLIPSES, labelled as
”good”, on his aining se a e:
R1: A(11.9,3.14) & H(7.9,1.57) & M(1.185,0.015)
R2: A(13.4,1.65) & H(9.3,1.53) & M(1.160,0.018)
R3: A(13.6,1.01) & M(1.178,0.021) & T(8.6,1.17)
The quali a i e models o hese ules a e2:
R1: A(11.9,ML) & H(7.9,ML) & M(1.185,MS)
R2: A(13.4,M) & H(9.3,ML) & M(1.160,MS)
R3: A(13.6,MS) & M(1.178,MS) & T(8.6,M)
The isualiza ion o g aphical ep esen a ionby pa -
allel coo dina es o hese ules a e shown in igu e 8.
The in e p e a ion o his knowledge would be:
2(L) La ge, (ML) Medium La ge, (M) Medium, (MS)
Medium Sho and (S) Sho .
6
A Da a Mining Me hod o Suppo Decision Making
in So wa e De elopmen P ojec s

Figu e 8R1): Asimdy, de ined in he in e al [5,
15], akes middle high alues (wi h cen e 11.9 and
a ma gin o

3.14), Hi edy, de ined in [5,10],
akes middle high alues, bu wi hou eaching he
ex eme alues (cen e 7.9 wi h ma gin o

1.57)
and Mxscdx, de ined in [1,1.2], akes e yhigh al-
ues (cen e 1.185 and ma gin o

0.015), p ac i-
cally, o he ex eme.

Figu e 8R2): Asimdy akes high alues (cen e
13.4 wi h a ma gin o

1.65), Hi edy akes high
alues (cen e 9.3 and ma gin o

1.53) and
Mxscdx akes middle high alues (cen e 1.160 and
wi h a ma gin o

0.018).

Figu e 8R3): Asimdy akes high alues, bu wi h-
ou eaching he ex eme alues (cen e 13.6 wi h
ma gin o

1.01), Mxscdx akes high alues, bu
wi hou eaching he ex eme alues (cen e 1.178
and ma gin o

0.021) and T nsdy, de ined in he
in e al [5,10], akes middle and high alues, bu
wi hou eaching he ex eme alues(cen e 8.6 and
wi h a ma gin o

1.17).
5.2 Cu ing sec ion 2: es ic ions o
ime and g ea e le el quali y
The se o ules o he managemen o SDP ha can
be ob ained om he aining se gene a edby he cu -
ing sec ion 2 o e in o ma ion on he policy o pe -
sonnel managemen (ASIMDY, HIREDY, TRNSDY)
and he maximum pos ponemen o he deli e y ime
o he p ojec (MXSCDX) when he objec i e is o
ob ain a inal p oduc wi h a deli e y ime (SCHCDT)
less han 352 days and he quali y o he inal p oduc
(ANERPT) is less han 0.35 e o / asks; ega dless o
he cos o he necessa y e o o ca y ou he p ojec
( able 2, cu ing sec ion 2). Thus, he cu ing sec ion
2 es ablishes a g ea e le el o quali y ha he cu ing
sec ion 1.
The ules induced by ELLIPSES, labelled as
”good”, on he aining se ob ained by cu ing sec-
ion 2 om CRCCRT da abase a e:
R1: A(13.5,1.57) & H(8.7,0.28)
R2: A(12.4,2.04) & M(1.175,0.006) & T(6.9,1.19)
R3: A(14.8,0.74) & H(9.9,1.20) & M(1.095,0.055)
The quali a i e models o hese ules a e
2
:
R1: A(13.5,M) & H(8.7,S)
R2: A(12.4,M) & M(1.175,S) & T(6.9,M)
R3: A(14.8,MS) & H(9.9,M) & M(1.095,ML)
The isualiza ion o he ob ained ules by EL-
LIPSES on his aining se is shown in Figu e 9.
The in e p e a ion o his knowledge would be:

Figu e 9R1): Asimdy akes high alues (cen e
13.5 wi h a ma gin o

1.57) and Hi edy akes
high alues, bu wi hou eaching he ex eme al-
ues (cen e 8.7 wi h a ma gin o

0.28).
Figu e 9: Visualiza ion by pa allel coo dina es o he man-
agemen ules induced om cu ing sec ion 2.

Figu e 9R2): Asimdy akes high alues, bu wi h-
ou eaching he ex eme alues (cen e 12.4 wi h a
ma gin o

2.04), Mxscdx akes high alues, bu
wi hou eaching he ex eme alues (cen e 1.175
and ma gin o

0.006) and T nsdy akes low and
middle alues (cen e 6.9 wi h a ma gin o

1.19).

Figu e 9R3): Asimdy akes e y high alues (cen-
e 14.8 wi h a ma gin o

0.74), Hi edy akes
high alues (cen e 9.9 and ma gin o

1.20) and
Mxscdx low and middle alues, bu wi hou each-
ing he ex eme alues (cen e 1.095 wi h a ma gin
o

0.055).
5.3 Analysis o he induced
knowledge
The manage can choose he managemen ule ha
he wan s o apply, when he ules ha e been induced.
This decision mus ake in o conside a ion o he s ea-
u es o he p ojec : he ini ial a ailable esou ces, he
equi emen s o he p ojec , he managemen policies
as well as he ma u i y le el o he o ganiza ion o de-
elopmen . Tha is o say, all induced ules canno
apply di ec ly.
Analyzing he ob ained esul s o bo h cu ing sec-
ion, he bes ule o apply is R1 o he cu ing sec ion
2 acco ding o he c i e ia in o de o choose he ules
es ablished in sec ion 3. The ule R1 is e y easy o
apply, because he manage only mus supe ise wo
a ibu es (ASIMDY and HIREDY) in o de o ob-
ained an accep able le el o quali y. Bu his ule has
an incon enience wi h ega d o o he induced ules:
he in e als o ASIMDY and HIREDY a e less wide
han in o he ules. Thus, he manage does no ha e
a lo o ma gin when his ule is applied.
Figu e 10 shows he e olu ion o he p ojec i he
ules R1 and R3 o he cu ing sec ion 2 a e applied.
Analyzing his igu e, we can see ha he deli e y
ime is simila in bo h ules (350 and 351 days, e-
spec i ely), bu he necessa y e o o ca y ou he
p ojec in R1 (2615 echnicians/days) is lowe han
he e o in R2 (2769 echnicians/days). Summa iz-
ing, he wo ules ob ain simila esul s o deli e y
ime and quali y, bu R1 educes he cos o he p ojec
7
ICEIS 2003 -
Figu e 10: Resul s o he p ojec simula ion wi h he ules R1 and R3 o he cu ing sec ion 2.
diminishing he necessa y e o o de elopmen .
6 CONCLUSION
In his pape , wep esen a s a egy o induce knowl-
edge in da abases o so wa e de elopmen p ojec s.
The da abases a e gene a ed by simula ions o dy-
namic models o he managemen p ojec s. A da a
mining ool analyzes hese da a inducing he new
knowledge.
This s a egy can be used o make h ee analysis: a
p io i analysis, moni o ing analysis and pos -mo em
analysis
We uphold he use o his new s a egy as opposed
o adi ional s a ic model o simple dynamic models.
Acknowledgmen s
This wo k has been suppo ed by Spanish Resea ch
Agency CICYT unde g an TIC2001-1143-C03-02.
REFERENCES
Abdel-Hamid, T. and Madnick, S. (1991). So wa e P ojec
Dynamics: an In eg a ed App oach. P en ice-Hall.
Aguila , J., Ramos, I., Riquelme, J., and To o, M. (2001).
An e olu iona y app oach o es ima ing so wa e de-
elopmen p ojec s. In o ma ion and So wa e Tech-
nology, 43(14):875–882.
´
Al a ez, J., Ma a, J., and Riquelme, J. (2002). Mining
in e es ing egions using an e olu iona y algo i hm.
In ACM SIGAPP Symposium on Applied Compu ing
(SAC), pages 498–502.
Chen, M., Han, J., and Yu, P. (1996). Da a mining: An
o e iew om da abase pe spec i e. IEEE T ans. on
Knowledge and Da a Enginee ing, 8(6):866–883.
Dhiel, E. (1991). Pa icipa o y simula ion so wa e o
manage s: The design philosophy behind mic owo ld
c ea o . Eu opean Jou nal o Ope a ional Resea ch,
59(1):210–215.
Fayyad, U., Pia e sky-Shapi o, G., and Smy h, P.
(1996). F om da a mining o knowledge disco e y in
da abases. AI Magazine, 17(3):37–54.
Goldbe g, D. (1989). Gene ic algo i hms in sea ch, op i-
miza ion, and machine lea ning. Addison-Wesley.
G aham, A., Mo ec o , J., Senge, P., and S e man, J.
(1992). Model-suppo ed case s udies o manage-
men educa ion. Eu opean Jou nal o Ope a ional Re-
sea ch, 59(1):151–166.
Holland, J. (1975). Adap a ion inNa u al and A i icialSys-
ems. Uni e si y o Michigan P ess.
Inselbe g, A. (1985). The plane wi h pa allel coo dina es,
special issue on compu a ional geome y. The Visual
Compu e , 1:69–97.
Michalewicz, Z. (1999). Gene ics Algo i hms + Da a S uc-
u es = E olu ion P og ams. Sp inge -Ve lag.
Ramos, I. and Riquelme, J. (1999). The dynamic models o
so wa e de elopmen p ojec s and he machine lea n-
ing echniques. In In e na ional Con e ence on P od-
uc Focused So wa e P ocess Imp o emen .
Ramos, I. and Ruiz, M. (1998). Aplicaci´on de di e -
en es pol´ı icas de con a aci´on de pe sonal en un
p oyec o de desa ollo de so wa e. In IVIn e na ional
Cong ess on P ojec Enginee ing.
Ruiz, M., Ramos, I., and To o, M. (2001). A simpli ied
model o so wa e p ojec dynamics. Jou nal o Sys-
ems and So wa e, 59(3):299–309.
8