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Aprendizagem de máquina para análise de indicadores na engenharia de software

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Academic year: 2017

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NA ENGENHARIA DE SOFTWARE

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ABSTRACT: Performance indicators are important resources for quality management in software

deve-lopment7Ke data volume produced Ey tKese indicators tends to increase signi¿cantly over monitoring time, which complicates analysis and decision making. The historical basis become complex, when considered the amount of data monitored and the indicators diversity (different types, granularity and frequency). This paper proposes the use of machine learning techniques for analysis of these bases using arti¿cial neural networks combined with information visuali]ation techniques. $ model of indicators is used, based on the processes of the MPS reference model for Software (MPS-SW), grouped accor-ding to the strategic perspectives of the Balanced Scorecard (BSC).

KEY WORDS:4uality of software process, ,ndicators, MPS-SW, BSC, $rti¿cial 1eural 1etwork, 9

isu-ali]ation of information.

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1R DSUHQGL]DGR VXSHUYLVLRQDGR H[LVWHRSDSHOGHXPHVSHFLDOLVWDHQWLGDGH H[WHUQDTXHDSUHVHQWDDRDOJRULWPRDOJXQV FRQMXQWRV GH SDGU}HV GH HQWUDGDV H VHXV FRUUHVSRQGHQWHV SDGU}HV GH VDtGD (VVD VDtGDSRGHVHUXPYDORUFRQWtQXRRXSRGH SUHGL]HU XP UyWXOR GH FODVVH SDUD R REMH -WR GH HQWUDGD 1R FDVR GH XPD 51$ SRU H[HPSOR QD IDVH GH WUHLQDPHQWR GD UHGH D FDGD HQWUDGD GH GDGRV R HVSHFLDOLVWD LQGLFDGHPDQHLUDH[SOtFLWDVHDUHVSRVWD FDOFXODGDpERDRXUXLPSURFHVVRGHURWX -ODJHPGRVGDGRV(QWmRDUHVSRVWDIRUQH -FLGDSHODUHGHpFRPSDUDGDjUHVSRVWDHV -SHUDGD&DVRRUHVXOWDGRVHMDGLIHUHQWHGR GHVHMDGRXPHUURpLQIRUPDGRjUHGHSDUD TXHRVDMXVWHVSRVVDPVHUUHDOL]DGRVD¿P GHPHOKRUDUDVUHVSRVWDVIXWXUDV

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1R DSUHQGL]DGR VHPLVXSHUYLVLR -nado são usados tanto dados rotulados, FRPR GDGRV QmR URWXODGRV SDUD R WUHLQD -mento. Em muitos casos, o uso de alguns dados rotulados em meio aos dados não ro-WXODGRVPHOKRUDFRQVLGHUDYHOPHQWHDSUHFL -Tabela 2 - .3,VDJUXSDGRVVHJXQGRSHUVSHFWL

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8PD 51$ SRVVXL FRPSRUWDPHQWR EDVHDGR QRV JUXSRV GH QHXU{QLRV GR Fp -UHEURKXPDQRTXHUHFHEHPHWUDQVPLWHP LQIRUPDo}HV DWUDYpV GRV GHQGULWRV H D[{ -QLRV UHVSHFWLYDPHQWH .29$&6 4XDQGRVHDSUHVHQWDXPFRQMXQWRHVSHFt¿ -FRGHHQWUDGDVHVXDVUHVSHFWLYDVVDtGDVD XPD51$HODpFDSD]GHDXWRDMXVWDUVHXV SHVRVVLQiSWLFRV2DMXVWHGDVFRQH[}HVp REWLGRSRUPHLRGHDSUHQGL]DGRDGRWDQGR FRPR FULWpULR GH WUHLQDPHQWR H SRVWHULRU DQiOLVHXPDGHWHUPLQDGDIXQomRGHDWLYD -ção. Os treinamentos das RNAs consistem QRPDSHDPHQWRGRUHODFLRQDPHQWRIXQFLR -QDOH[LVWHQWHHQWUHDVHQWUDGDVHDVVDtGDV $SyVRWUHLQDPHQWRDUHGHGHYHVHUFDSD] GHWRUQDUJHQpULFRRFRPSRUWDPHQWRGRSUR -FHVVRQRPRPHQWRHPTXHRXWUDVHQWUDGDV GLIHUHQWHVGDVTXHIRUDPXWLOL]DGDVGXUDQWH R WUHLQDPHQWR VHMDP DSUHVHQWDGDV %5$ -*$HWDO

2WLSRGHSUREOHPDDERUGDGRQHVWH WUDEDOKRDYDOLDomRGHJUXSRVGHLQGLFDGR -UHVGHGHVHPSHQKRDVVRFLDGRVDRSURFHV -VR GH GHVHQYROYLPHQWR GH -VRIWZDUH SRGH VHUWUDWDGRSRU51$VeXPFDVRHPTXH RVLQGLFDGRUHVDWULEXWRVSRVVXHPYDORUHV de medição e metas bem diferentes, sem XP SDGUmR QRV WLSRV GH GDGRV GH¿QLGRV SRGHPFRQWHUYDORUHVLQWHLURVERROHDQRV UHDLV HWF 8PD RUJDQL]DomR SRGH ³HQVL -QDU´FRPEDVHHPFULWpULRVGHSHVRVFRPR GHWHUPLQDGRVFRQMXQWRVGHLQGLFDGRUHVSR -GHPH[SUHVVDUPHWDVGHFRQWUROHDMXVWDQ -GRVHXVSDUkPHWURVVREGHPDQGD$SHVDU GRVSHVRVGHXPD51$JHUDOPHQWHVHUHP GHWHUPLQDGRV SHORV WUHLQDPHQWRV GD UHGH GXUDQWH R WUHLQDPHQWR RV JHVWRUHV SRGHP FRQWULEXLU FRP HVVH ³DMXVWH GH SDUkPHWUR´ QDV SHQDOL]Do}HV GH DSUHQGL]DGR SDUD TXHRUHVXOWDGRVHMDRPHOKRUSRVVtYHOSDUD VHXVLQGLFDGRUHV'HYLGRDRVFXVWRVHQYRO -YLGRV QRV SURFHGLPHQWRV TXH HQYROYHP R WUHLQDPHQWRDVWpFQLFDVGH51$GHDSUHQ -GL]DGR VHPLVXSHUYLVLRQDGR PRVWUDPVH DGHTXDGDVQDDYDOLDomRGHLQGLFDGRUHVGH GHVHPSHQKRGHSURGXomRGHVRIWZDUH

4. APLICAÇÃO DE RNA NA AVALIAÇÃO DE INDICADORES DE DESEMPENHO

(VWD VHomR DSUHVHQWD XP H[SH -rimento realizado com indicadores de de-VHPSHQKR GH XP SURFHVVR SURGXWLYR GH XPDHPSUHVDTXHHPERUDQmRVHMDGDiUHD GH SURGXomR GH VRIWZDUH PRVWURX PXLWDV semelhanças com o tratamento de indica-GRUHVGHVVDiUHD$LGHLDIRLYHUL¿FDURFRP -SRUWDPHQWRGD51$HPXPFHQiULRSRXFR IDYRUiYHOFRPSRXFDVDPRVWUDVSDUDWUHL -QDPHQWRHSRXFRVLQGLFDGRUHVFRQVLGHUDQ -GRDDSOLFDomRGH51$HPHPSUHVDVTXH HVWmR FRPHoDQGR D SDGURQL]DU VHXV SUR -FHVVRVFRPRVQtYHLV*H)GR0366:

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-1D LQWHQomR GH DX[LOLDU XP HVSH -FLDOLVWDHPIXWXURVSURFHVVRVGHURWXODJHP GDVDPRVWUDVIRUDPDSOLFDGDVWpFQLFDVGH YLVXDOL]DomRQRFRQMXQWRGHGDGRVFODVVL -¿FDGRSHORDOJRULWPR3HUFHSWURQ2XVRGH PtQLRGDHPSUHVDIRUQHFHGRUDGRVGDGRV XWLOL]DQGRRFULWpULREDVHDGRQDPHWiIRUDGR VHPiIRUR³YHUGH´REWHYHRUyWXOR³6DWLVID -WyULR´³$PDUHOR´¿FRXFRPUyWXOR³5HJXODU´ H ³9HUPHOKR´ ³,QVDWLVIDWyULR´ $VVLP VH D VDtGDIRUQHFLGDSHOD51$REWLYHUUyWXOR³6D -WLVIDWyULR´ 9HUGH LQGLFDUi TXH R FRQMXQWR de indicadores analisados está em confor-PLGDGH GHPRQVWUDQGR TXH RV SURFHVVRV HVWmRFRQWURODGRV6HRUHVXOWDGRGHVDtGD IRU³5HJXODU´$PDUHORLQGLFDUiTXHRSUR -FHVVR SRVVXL UXSWXUDV VHQGR QHFHVViULD PDLRU DWHQomR GD JHVWmR GRV SURFHVVRV 3RU¿PVHDVDtGDIRU³,QVDWLVIDWyULD´LQGL -FDUiTXHRVSURFHVVRVQmRHVWmRH¿FD]HV VHQGRQHFHVViULDVDo}HVFRUUHWLYDV

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Tabela 3 - 7RWDOGHDFHUWRVHQWUHFODVVHVUHDO YVSUHGLWD

WpFQLFDVGHYLVXDOL]DomRGDLQIRUPDomRDQ -WHVGDURWXODJHPGRVGDGRVSRGHDMXGDUQD REVHUYDomR GR FRPSRUWDPHQWR GRV DWULEX -WRVLQGLFDGRUHVLGHQWL¿FDQGRSRVVtYHLVHU -URVQRSURFHVVRGHURWXODJHPHPHOKRUDQ -do os resulta-dos no treinamento da RNA. A )LJXUDDSUHVHQWDRUHVXOWDGRGDSURMHomR multidimensional desses dados, baseada nos atributos de cada amostra, utilizando DWpFQLFDGHSURMHomR/63/HDVW6TXDUHV 3URMHFWLRQ3$8/29,&+HWDO1HV -VD WpFQLFD DV FRUHV UHSUHVHQWDP DV FODV -VHV FDGD DPRVWUD UHSUHVHQWD XP SRQWR H FDGDSRQWRFRUUHVSRQGHDXPSURMHWRLGHQ -WL¿FDGRSHODOHWUD³3´2VSURMHWRVPDLVVL -PLODUHVVmRSRVLFLRQDGRVEHPSUy[LPRVQR SODQRGHSURMHomRHQTXDQWRRVPDLVGLVVL -PLODUHV GLVWDQWHV VmR SRVLFLRQDGRV PDLV afastados.

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1HVVHWLSRGHSURMHomRpSRVVtYHO SHUFHEHUTXHDOJXQVSURMHWRVGHGLIHUHQWHV FODVVHV HVWmR VH PLVWXUDQGR YHU R FHQWUR GD SURMHomR PDV QmR VH SRGH H[SOLFDU R PRWLYRSHORTXDOLVVRRFRUUH'HVVDIRUPD VHIH]QHFHVViULDDEXVFDSRURXWUDIRUPD GH YLVXDOL]DomR GHVVH FRQMXQWR GH GDGRV FXMDUHSUHVHQWDomRYLVXDOGHVWDTXHRUHOD -cionamento entre os atributos dos dados. Assim, a Figura 5DSUHVHQWDRUHVXOWDGRGD WpFQLFDGHFRRUGHQDGDVSDUDOHODVSURSRV -WDSRU,QVHOEHUJH'LPVGDOH1HVVD WpFQLFDDVFRUHVFRUUHVSRQGHPjVFODVVHV RV HL[RV UHSUHVHQWDP RV DWULEXWRV H DV OL -QKDVUHSUHVHQWDPDVLQVWkQFLDVGRVGDGRV &DGD OLQKD FRUWD R HL[R SDUDOHOR QR YDORU FRUUHVSRQGHQWHDRDWULEXWR'HVWDPDQHLUD p SRVVtYHO REVHUYDU R FRPSRUWDPHQWR GRV DWULEXWRV GRV GDGRV LGHQWL¿FDQGR SDGU}HV RXGLVFUHSkQFLDV

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WHUVXDFODVVL¿FDomRUHYLVDGDRTXHFHUWD -mente contribuiria na acurácia dos testes

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5. CONSIDERAÇÕES FINAIS

2 REMHWLYR GHVWH DUWLJR IRL SURSRU R XVR GH 5HGHV 1HXUDLV$UWL¿FLDLV 51$V SDUD R WUDWDPHQWR GH LQGLFDGRUHV GH GH -VHPSHQKRQRFRQWUROHGDTXDOLGDGHGDSUR -GXomRGHVRIWZDUH2XVRGHWpFQLFDVGHYL -VXDOL]DomRSRGHDMXGDUQDFODVVL¿FDomRGRV JUXSRVGHLQGLFDGRUHVSDUDWUHLQDPHQWRGD EDVHFRPRIRLPRVWUDGRHPXPH[SHULPHQ -WRVLPSOHV(PH[SHULPHQWRVPDLVFRPSOH -[RV FRP JUDQGH Q~PHUR GH DPRVWUDV D FRQWULEXLomR GDV WpFQLFDV GH YLVXDOL]DomR SRGHVHUDLQGDPDLVLPSDFWDQWH$SHVDUGR H[SHULPHQWRDSUHVHQWDGRXWLOL]DUXPD51$ GHDSUHQGL]DGRVXSHUYLVLRQDGRHPHPSUH -VDV SURGXWRUDV GH VRIWZDUH DV SHVTXL-VDV PRVWUDPTXHXPD51$GHDSUHQGL]DGRVH -PLVXSHUYLVRQDGRSRGHVHUPDLVDGHTXDGD ,VVRSRUTXHFRPELQDPSRXFRVLWHQVGHGD -GRVURWXODGRVUHGXomRGHHVIRUoRHFXVWR FRPXPDJUDQGHTXDQWLGDGHGHGDGRVQmR URWXODGRV SRGHQGR FRQWULEXLU SDUD REWHQ -ção de melhores resultados.

Em continuidade a esse trabalho, HVWmRVHQGRUHDOL]DGDVSHVTXLVDVFRPHP -SUHVDV UHDLV GH SURGXomR GH VRIWZDUH HP JUDQGHHVFDODHFRPSURFHVVRVGRVQtYHLV *HRX)3DUDLVVRRPRGHORGH.3,VDSUH -VHQWDGR WHP FRQWULEXtGR SDUD R HQWHQGL -mento dos indicadores utilizados em cada HPSUHVD$TXDQWLGDGHGHGDGRVGDVEDVHV

GHPRQLWRUDomRGHLQGLFDGRUHVWHPVLGRYD -ULiYHO8PDGDVHPSUHVDVFRQWpPGDGRVGH cinco anos de monitoramento, com carac-WHUtVWLFDVFRPSOH[DVHGHJUDQGHLQWHUHVVH jV SHVTXLVDV 2 PRGHOR GH DJUXSDPHQWR SRUSHUVSHFWLYDVGR%6&FRODERURXFRPR HQWHQGLPHQWRGHJUXSRVGHLQGLFDGRUHVHP alguns casos.

6. REFERÊNCIAS

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BARCELLOS, M. P. Uma estratégia para medição

de software e avaliação de bases de medidas para controle estatístico de processos de software em organizações de alta maturidade. 2009. 419 f.7HVH

'RXWRUDGR &XUVR GH (QJHQKDULD GH 6LVWHPDV H &RPSXWDomR8QLYHUVLGDGH)HGHUDOGR5LRGH-DQHL -UR

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dinâmica espaço-temporal em redes complexas. 2010. I7HVH'RXWRUDGR&XUVRGH&LrQFLDVGH

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025(,5$57/,0$*10$&+$'2%%0$ -5,1+2 : 7 9$6&21&(/26$ 528,//(5$ C. Uma abordagem para melhoria do processo de

software baseada em medição.9,,,6LPSyVLR%UDVL -OHLURGH4XDOLGDGHGH6RIWZDUH

MOURA, L. R. Gestão Estratégica da Informação: SURSRVLomR GH XP PRGHOR GH RUJDQL]DomR EDVHDGR no uso da informação como recurso da gestão em-SUHVDULDOI'LVVHUWDomR0HVWUDGR&XUVR de Faculdade de Economia, Administração e Contabi-OLGDGH8QLYHUVLGDGHGH6mR3DXOR6mR3DXOR

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