Itertiol Jourl of Egieerig Reserh d Developmet e-issn : 78-067X, p-issn : 78-800X, www.ijerd.om Volume, Issue 5 (July 0), PP. 78-8 Desig of Hybrid Neurl Network Model for Qulity Evlutio of Objet Orieted Softwre Modules Amdeep Kur, Arj Sigh, Bljit Sigh 3,3 Deprtmet of Computer Siee Egieerig, BBSB Egieerig College, Ftehgrh Shib. Pujb, Idi Deprtmet of Mthemtis, Pujbi Uiversity, Ptil-400, Pujb, Idi. Abstrt The im of this pper is to evlute the qulity of objet orieted modules. I this work eurl etwork pproh is used log with PSO (prtile swrm optimiztio) to fid out fult proe ompoets of softwre. The evlutio mesures used re Aury, MAE (Me bsolute error), RMSE (Root me squre error) d the results re lulted o differet itertios. The Aury, MAE, RMSE is improved s umber of itertios ireses. Keywords Softwre egieerig, Fult proeess, Neurl Network, Prtile Swrm Optimiztio. I. INTRODUCTION Softwre qulity is the degree to whih softwre possesses desired ombitio of ttributes suh s relibility, mitibility, effiiey, portbility, usbility, d reusbility []. This desired ombitio of ttributes must be lerly defied. Vrious softwre metris hve bee idetified for the purpose of evlutig these hrteristis of softwre systems. The im of softwre metris is to predit the qulity of the objet orieted softwre produts. Fult-proeess of softwre module is the probbility tht the module otis fults. A oreltio exists betwee the fult-proeess of the softwre d the mesurble ttributes of the ode d testig [9]. Aurte preditio of fult-proe modules ebles the verifitio d vlidtio tivities foused o the ritil softwre ompoets Y M d Boj Cuki [3]. Therefore, softwre developers hve kee iterest i softwre qulity models. It is required tht fultproe preditio models should be effiiet d urte. Preditig the umber of fults i eh module or idetifyig the fult-proe modules i the erly stges of the softwre developmet life yle will help the developmet Reu Kumr Louisi []. Softwre omplexity metris ply useful role i this regrd beuse they re umeril mesures tht be obtied erly i the softwre life yle. There exists reltioship betwee mesures of softwre omplexity d the umber of errors lter foud i test d vlidtio. Fults i softwre systems otiue to be mjor problem. My systems re delivered to users with exessive fults. It hs log bee reogized tht seekig out fult-proe prts of the system d trgetig those prts for iresed qulity otrol d testig is effetive pproh to fult redutio. It is diffiult to idetify relible pproh to idetifyig fult-proe softwre ompoets Prvider S. Sdhu [9]. Bsili et l. [4] empirilly vlidted CK metris d foud o orreltio mog metris. The pper stted tht ll metris were effetive i preditig fult proeess exept LCOM (Lk of Cohesio of Methods). However the metri suite ws pplied to the soure ode beuse some of their metris mesure ier omplexity of lss, d suh iformtio ot be obtied util the lgorithm d struture of the lss re determied t the ed of desig phse. Khoshgoftrr et l. [6] itrodued the use of the eurl etworks s tool for preditig softwre qulity. They ompred the eurl-etwork model with oprmetri disrimit model, d foud the eurl etwork model hd better preditive ury. This model used domi metris derived from the omplexity metri dt. These metris re ot dequte for detetig objet-orieted fults. Sie the objet-orieted prdigm exhibits differet hrteristis from the proedurl prdigm, softwre metris i objet-orieted prdigm eed to be used. With the iresig use of objetorieted methods i ew softwre developmet there is growig eed to improve urret prtie i objet-orieted desig d developmet Toshihiro Kmiy et l. [7] preseted method to estimte the fult-proeess of the lss i the erly phse, usig severl omplexity metris for objet orieted softwre. They itrodued four hekpoits ito the lysis/desig/implemettio phse, d estimte the fult-proe lsses usig the pplible metris t eh hekpoit. They estimte the fult-proeess by usig the multivrite logisti regressio lysis. Athr Mhweerwt [8] proposed fult preditio model bsed o supervised lerig usig Multilyer Pereptro Neurl Network d the results re lyzed i terms of lssifitio orretess d bsed o the results of lssifitio, fulty lsses re further lyzed d lssified ordig to the prtiulr type of fult. Muso & Khoshgoftr [0] use disrimit lysis tehique to ivestigte some spets of the reltioship betwee progrm omplexity mesures d progrm fults. Mie mie thet thwi, tog-seg quh [] proposed eurl etwork model log with objet orieted metris. This empiril study presets the preditio of fults i three idustril rel time system usig multiple regressio model d eurl etwork model d foud tht eurl etwork model predit the umber of fults more extly th multiple regressio model. 78
Desig of Hybrid Neurl Network Model for Qulity Evlutio of Objet Orieted Softwre Modules A vriety of softwre fult preditios tehiques hve bee proposed, but oe hs prove to be osistetly urte. Therefore, there is eed to fid the best preditio tehiques for give preditio problem.. The tehiques used i Khoshgoftrr et l. [6] used to predit fults tht use trditiol metris re ot geerlly pplible to objet orieted system. The dt olleted i Bsili et l. [4] is ot of idustry. So there be iresed omplexity whe it omes to lrge objet orieted system of orgiztio. 3 CK metris evlute stti omplexity of objet orieted softwre i Bsili et l. [4], V. R. Bsili [5]. Severl OOD speifitios ilude dymi iformtio. It is eessry to evlute suh dymi omplexity. 4. Models used i Khoshgoftr [] tke Irese time to desig d develop qulity ses. So, i this study PSO Tehique is used log with Neurl Network to predit fult proe modules i objet orieted. II. MATERIALS AND METHODS The proposed study fid out the fult proeess of objet orieted modules. The tehique used i this study is Prtile swrm optimiztio log with Neurl Network. The evlutio mesures used re Aury, Me Absolute Error, Root Me Squred Error. Step-: Fid the struturl ode d desig ttributes of softwre. The dtset tke is from NASA KC.The dtset is relted to softwre defet preditio whih otis 09 modules d metris. Step-: Lod the triig dt.i this step the 80% of KC dtset is used for triig. Step-3: Perform Prtile Swrm Optimiztio bsed Neurl Network triig. I this step Prtile Swrm Optimiztio toolbox for Mtlb is used d the triig is performed with the help of this toolbox. Step-4: Lod testig dt. I this step 0% of k dtset is used for testig. Step-5: I this step testig is doe d the performe is lulted o the bsis of Aury, MAE (Me Absolute Error), RMSE (Root Me Squred Error) The evlutio mesures tht re used to rry out the results re disussed below: Me bsolute error (MAE) Root me squre error (RMSE) Aury Me bsolute error Me bsolute error, MAE is the verge of the differee betwee predited d tul vlue i ll test ses; it is the verge preditio error. The formul for lultig MAE is give i equtio show below:... () Assumig tht the tul output is, expeted output is Root me-squred error RMSE is frequetly used mesure of differees betwee vlues predited by model or estimtor d the vlues tully observed from the thig beig modeled or estimted.... () The bsi flow of proposed model is give below: 79
Desig of Hybrid Neurl Network Model for Qulity Evlutio of Objet Orieted Softwre Modules Strt Fid the struturl ode d desig ttributes of S/W Lod the triig dt Perform the triig of NN with PSO Lod the testig dt PSO tried with NN is evluted gist the testig dt The performe is evluted i terms of Aury, MAE (me bsolute error) d RMSE(root me squre error). Stop III. RESULTS AND DISCUSSIONS The PSO tehique log with Neurl etwork is used to fid the fult proe ompoets of objet orieted softwre modules.the results re lulted o differet itertios d eurl etwork gives better results s umber of itertios ireses. No. of prtile Fig: Fidig Gbest vlue by PSO by differet itertio Dimesi os 30 50 No. of itertios Gbest vlue Aur y MAE RMSE 00.469 94. 0.099 0.09 500.890 94.78 0.090 0.0 000.873 96.5 0.079 0.0 500.8609 96.98 0.097 0.0990 000.8709 97.75 0.078 0.0957 Tble: Performe o the bsis of differet itertios by PSO 80
Desig of Hybrid Neurl Network Model for Qulity Evlutio of Objet Orieted Softwre Modules Grph:Showig performe by PSO o differet itertios PSO used with eurl etwork is givig better results s the umber of itertios ireses. The ompriso is show i tble give below: S.o. Algorithm Aury MAE RMSE PSO with NN 97.75 0.078 0.0957 Geerlized regressio etwork 97.66 0.065 0.056 Tble : Compriso of PSO with NN with geerlized regressio etwork o the bsis of differet mesures Grph :Compriso o the bsis of ury Grph3: Compriso o the bsis of MAE Grph 4: Compriso o the bsis of RMSE IV. CONCLUSION A vriety of softwre fult preditio tehiques hve bee proposed, whih hve bee lredy pplied i softwre egieerig pplitios. A eurl etwork is tried to reprodue give set of orret lssifitio exmples, isted to produe formul or rules. Neurl etworks re o lier tehiques tht re ble to model omplex futios. I this work NN pproh is used log with PSO to fid out fult proe ompoets of softwre. The evlutio mesures used re Aury, MAE, RMSE d the results re lulted o differet itertios. Result is lulted o differet itertios. The ury is improved s the umber of itertios ireses. The results showed tht proposed tehique is better s ompred to trditiol pproh d give better results. 8
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