Intelligent Water Drops (IWD) Algorithm for COQUAMO Optimization

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1 Proceedigs of the World Cogress o Egieerig ad Computer Sciece Vol I WCECS, October -,, Sa Fracisco, USA Itelliget Water Drops (IWD) Algorithm for COQUAMO Optimizatio Abdulelah G. Saif, Safia Abbas, ad Zaki Fayed, Member, IAENG Abstract Software quality estimatio is oe of essetial aspects i software proects. Accurate quality estimates are ecessary for goodly developig software systems. May estimatio methods have bee proposed. Amog those methods, COQUAMO, the model used to estimate the quality of the software proect i defects/ksloc (or some other uit of size). Nowadays, estimatio models are based o eural etwork, the fuzzy logic modelig etc. for accurately estimate software developmet effort, time ad quality. As, eural etworks desig have ot clear guidelies ad fuzzy logic approach usage is more difficult, a meta-heuristic Itelliget Water Drops (IWD) algorithm ca offer some improvemets i accuracy for software quality estimatio. This work adapts the IWD algorithm for optimizig the curret of COQUAMO model to achieve more accurate estimatio of software developmet quality. The experimet has bee coducted o NASA software proects. This work is the first oe used to optimize COQUAMO. Idex Terms COQUAMO, IWD algorithm, Software quality estimatio I. INTRODUCTION A software proect that is completed o time, withi budget, ad delivers a quality product that satisfies users ad meets requiremets is said to be successful. However, may software proects fail. Oly a third of all software developmet proects were successful, i terms of they met budget, schedule, ad quality goals as a report give by the Stadish Group states []. Most proect fails usually are due to the plaig ad estimatio steps, ot due to the implemetatio steps. Several studies have bee doe durig the last decade, for fidig the reaso of the software proects failure. iteret sites were searched extesively by Galorath et al. who foud reasos for the software proect failures. Amog the foud reasos, isufficiet requiremets egieerig, poorly plaed proect, suddely decisios makig at the early stages of the proect ad iaccurate estimatios were the most importat reasos []. Therefore, accurate software cost, time ad quality estimatio is ecessary ad is critical to both developers ad customers. Software cost estimatio focuses Mauscript submitted July, ; revised July,. The authors gratefully ackowledge the support of Ai Shams Uiversity ad Yeme govermet i supportig them. Abdulelah Ghaleb Farha Saif is Ph.D studet at Ai Shams Uiversity, Egypt (phoe: 44; abdulelah.saif@gmail.com). Safia Abbas Mahmoed Abbas is lecturer at Ai Shams Uiversity, Egypt ( Safia_abbas@yahoo.com). Zaki Taha Ahmed Fayed is Emeritus Professor at Ai Shams Uiversity, Egypt ( ZFayed@hotmail.com). o the time ad the effort required to complete a software proect. Software cost estimatio starts at the proposal state ad cotiues throughout the life time of a proect []. The huma-effort occupies the large part of software developmet cost ad most cost estimatio methods focus o this aspect ad give estimates i terms of perso-moth [4]. Some software defects are uavoidable durig software developmet, eve if accurate plaig, well documetatio ad proper process cotrol are performed carefully. These software defects affect the quality of software product which might be the mai cause of proect failure []. Therefore, i order to maage budget, schedule ad quality of software proects, several software estimatio methods have bee developed. Amog those methods, COCOMO II is the most widely used model for estimatig the effort i persomoth ad the time i moths for the whole software proect ad also at differet stages, ad COQUAMO is the model used to estimate the quality of the software proect i terms of defects/ksloc (or some other uit of size). Nowadays, most estimatio models are based o eural etwork, geetic algorithm, the fuzzy logic modelig etc. for accurately estimate software developmet effort, time ad quality. As, eural etworks have ot clear guidelies for desig ad fuzzy logic approach usage is more difficult, the meta-heuristic itelliget water drops (IWD) algorithm ca offer some improvemets i accuracy for software quality estimatio. This work adapts the IWD algorithm for optimizig the curret of COQUAMO model to achieve more accurate estimatio of software developmet quality. The experimet has bee coducted o NASA software proects. This work is the first oe used to optimize COQUAMO. The rest of the paper is orgaized as follow: sectio II related works, sectio III COQUAMO model, sectio IV dataset descriptio, sectio V IWD algorithm, sectio VI results aalysis ad sectio VII discusses ad cocludes the paper. II. RELATED WORK There are may predictio models that ca be used to predict software defects such as machie learig based models (artificial eural etworks (ANN), Bayesia belief etworks (BBN), reiforcemet learig (RL), geetic algorithms (GA), geetic programmig (GP) ad decisio trees) [] ad fuzzy logic models [] etc. However each oe has its ow advatages ad disadvatages ad each oe ca be used for specific proects at differet stages []. Because COCOMO II is the most widely used ad stadard model for estimatig the effort i perso-moth ad the time i moths for a software proect at differet stages [4] ad COQUAMO [][] is a extesio of it, COQUAMO ISBN: ---- ISSN: - (Prit); ISSN: - (Olie) WCECS

2 Proceedigs of the World Cogress o Egieerig ad Computer Sciece Vol I WCECS, October -,, Sa Fracisco, USA model will deserve much attetio to improve it. Because today's proect quality evaluatio based o old of COQUAMO model may ot match the required accuracy, therefore by calibratio, the accuracy of results i this method will be icreased ad the aim of this research is to use IWD algorithm to optimize the curret COQUAMO model to achieve accurate software quality estimatio ad reduce the ucertaity of COQUAMO usig IWD algorithm. III. COQUAMO MODEL Costructive quality model (COQUAMO), which is show i figure, is a extesio of the existig costructive cost model (COCOMO II) ad cosists of two sub-models; defects itroductio (DI) sub-model ad defects removal (DR) sub-model. A. Defect Itroductio (DI) Sub-Model The DI sub-model s iputs iclude source lies of code ad/or fuctio poits FPs as the sizig parameter, adusted for both reuse ad breakage, ad a set of multiplicative DI-drivers divided ito four categories, platform, product, persoel ad proect. These DI-drivers are a subset of the cost parameters required as iput for COCOMO II. Developmet flexibility FLEX driver has o effect o defect itroductio ad thus here its values for ratig are set to. The decisio to use these drivers was take after the author did a extesive literature search ad did some behavioral aalyses o factors affectig defect itroductio. The outputs of DI sub-model are predicted umber of otrivial defects of requiremets, desig ad code itroduced durig developmet life cycle; where o-trivial defects iclude: Critical (causes a system crash or causes a serious damage or eopardizes persoel) High (causes impairmet of critical system fuctios ad o workaroud solutio exists) Medium (causes impairmet of critical system fuctio, though a workaroud solutio does exist). Based o expert-udgmet, a iitial set of values to each of ratigs of the DI-drivers that have a effect o the umber of defects itroduced ad overall software quality were proposed ad we are used them i our implemetatio. B. Defect Removal (DR) Sub- Model The aim of the defect removal (DR) model is to estimate the umber of defects removed by several defect removal activities, amely automated aalysis AUTA, people reviews PEER ad executio testig ad tools EXTT. The DR model is a post-processor to the DI model. Each of these three defect removal profiles removes a fractio of the requiremets, desig ad codig defects itroduced from DI model. Each profile has levels of icreasig defect removal capability, amely Very Low, Low, Nomial, High, 'Very High' ad Extra High with Very Low beig the least effective ad Extra High beig the most effective i defect removal. To determie the defect removal fractios (DRF) associated with each of the six levels (i.e. very low, low, omial, high, very high, extra high) of the three profiles (i.e. automated aalysis, people reviews, executio testig ad tools) for each of the three types of defect artifacts (i.e. requiremets defects, desig defects ad code defects), the author coducted a -roud Delphi ad we used the values of DRF resulted from -roud Delphi i our implemetatio. The iputs of DR sub-model iclude software size i thousad source lies of code KSLOC ad/or fuctio poits, defect removal profiles levels ad umber of otrivial defects of requiremets (Req), desig (Des) ad code (Code) from DI model. For more details about COQUAMO, see [] []. Software size estimate Software platform, product, persoel ad proect attributes Defect removal profile levels Fig.. Costructive quality model (COQUAMO). For each of the three artifacts: Estimated itroduced defects i requiremets: DI Req = A. (Size) B. ( DI Driver ) Re q ) () Estimated residual defects i Requiremets: DR Req = C. DI Req. ( DRF ) r Re q () r Estimated itroduced defects i desig: DI Des =A. (Size) B. Estimated residual defects i desig: ( DI Driver ) Des ) () DR Des =C. DI Des. ( DRF ) rdes (4) r Estimated itroduced defects i code: DI Code =A. (Size) B. COQUAMO Defect Itroductio Sub-Model Defect Removal Sub- Model Estimated residual defects i code: Number of residual defects (Defect desity per uits of size) ( DI Driver ) Code ) () DR Code =C. DI Code. ( DRF ) rcode () r A, A, A, C, C ad C are the multiplicative calibratio costats for each artifact. Size is the size of the software proect measured i terms of KSLOC (thousads of source lies of code, fuctio poits FPs or ay other uit of size), here KSLOC is coverted to FPs as software size ISBN: ---- ISSN: - (Prit); ISSN: - (Olie) WCECS

3 Proceedigs of the World Cogress o Egieerig ad Computer Sciece Vol I WCECS, October -,, Sa Fracisco, USA measure by assumig c laguage is used for implemetatio. B, B ad B accout for ecoomies / disecoomies of scale. (DI-driver)Req, (DI-driver)Des ad (DIdriver)Code are the defect itroductio driver for each artifact ad the th factor. r = to for each DR profile, amely automated aalysis, people reviews, executio testig ad tools. DRF rreq, DRF rdes ad DRF rcode are Defect Removal Fractio for defect removal profile r ad artifact type (Req, Des ad Code). IV. DATASET DESCRIPTION Experimets have bee coducted o NASA data set foud i []. The dataset cosist of completed proects with its size i kilo lie of code (KLOC) ad actual quality i defects/ KLOC. Here KSLOC is coverted to FPs as software size measure by assumig c laguage is used for implemetatio. Multipliers (DI-Drivers) ad scale factors ratig from Very Low to Extra High are also give i the dataset. Defects removal activities levels (ratigs) are ot foud i NASA dataset, so the ratigs are assumed to be of Nomial ratig. I this data set, there is o classificatio of defects ito requiremets (Req), desig (Des) ad Code (Code) defects, So the total defects of each proect i the dataset are coverted ito Req, Des ad Code defects accordig to Joes report [] such that documetatio defects =. per fuctio poit FP, requiremets defects= per FP, desig defects =. per FP, code defects=. per FP ad bad fixes defects=.4. Therefore, Req, Des ad Code defects for each proect (Pr ) i the data set are calculated as follow: Pr Req defects = Pr Des defects= Pr Code defects= total defects of Pr total defects of Pr total defects of Pr *(+.) () *(.+.) () *(.+.) () Where, documetatio defects are divided equally amog artifacts by assumptio (. for each). is a factor of c laguage to covert SLOC ito FPs. Bad fixes defects are related to DI-drivers. V. INTELLIGENT WATER DROPS (IWD) ALGORITHM I ature, flowig water drops are mostly see i rivers, which form huge movig swarms. The paths that a atural river follows have bee created by a swarm of water drops. while a atural water drop flows from oe poit of a river to the ext poit i the frot, Three chages occur durig this trasitio: the water drop velocity is icreased, the water drop soil is icreased, ad i betwee these two poits, soil of the river s bed is decreased. Based o these observatios, the Itelliget Water Drops have bee itroduced. These Itelliget Water Drops or IWDs flow i a graph of a give optimizatio problem. The, the resultig effect is that the best solutio is obtaied for the problem [, ]. A. Assumptio ad Represetatio IWD is basically developed for combiatorial optimizatio problems, with oly oe paper [] modifies it to be used for cotiuous optimizatio problems by usig the biary codig of edges. To use IWD for cotiuous optimizatio problems such as optimizig the COQUALMO model ad maitaiig the origial structure of IWD, the of COQUALMO model, A, B, C, A, B, C, A, B ad C, are assumed to be represeted by the graph by addig virtual odes umbered from to ad coectig those odes to each coefficiet as i figure, where i= soil soil soil soil soil soil A i Fig.. Coefficiets represetatio. B i Each of above ie is expressed by 4 digits which are chose amog digits by IWD algorithm accordig to miimum probabilities. First digit is itegral part of a coefficiet ad the remaiig are fractios part. The soils are placed o the edges betwee ad digits as i the figure. B. The Proposed IWD Algorithm To optimize the COQUALMO model, The mai steps of proposed IWD algorithm are i figure. VI. RESULT ANALYSIS IWD parameters iitial values: Number of IWDs=, Iitial Soil=, velocity=, local ad global soil updatig parameters=., a v =,b v =., c v =,a s =,b s =. ad c s =. The best result is achieved usig iteratios, ad a solutio set is received from which the best solutio is chose i.e. a solutio with the best fitess fuctio value (FitessAll). The fial best solutio obtaied for are: Accordig to this solutio, the resultig optimized COQUALMO model are the followig: A=.4, B=., C=., A=.4, B=., C=., A=.4, B=.ad C=.. Curret COQUALMO model are all equal. Tables, table I ad table II, show the compariso amog actual ad estimated Req, Des ad Code defects/fp obtaied from COQUALMO model usig its curret ad usig optimized by IWD, respectively for the first te proect dataset with their estimated proect size. Table III shows the compariso amog the actual ad estimated Req, Des ad Code defects/fp for the first te proect dataset usig optimized ad curret COQUAMO model with their estimated proect size. The graphical compariso described i table I ad table II is show i figure 4 ad figure, respectively. The graphical compariso described i table III is show i figure, figure ad figure. Ci ISBN: ---- ISSN: - (Prit); ISSN: - (Olie) WCECS

4 Proceedigs of the World Cogress o Egieerig ad Computer Sciece Vol I WCECS, October -,, Sa Fracisco, USA. Set parameters ad determie dataset. Iitialize the soils of virtual edges betwee ad their digits.. While (termiatio coditio ot met) do 4. IWDs are placed o the first ode A ad move to the ext util ode C is reached.. IWDs choose 4 digits amog digits as values for all accordig to miimum probabilities ad add the digits to their visited lists. If there is o improvemet i oe of the fitess fuctios (FitessCode) i step, IWDs choose 4 digits for each coefficiet radomly.. IWDs update their velocity. IWDs update soils o edges betwee ad chose digits ad load some soils accordig to IWD algorithm equatio 4 i [].. Each IWD i calculate estimated Req, Des ad Code defects for each proect i the dataset usig the values of chose by them.. Each IWD i calculates Magitude of Relative Error (MRE) for each proect, the equatio used for Req, Des ad Code, respectively are: FitessReqi = ActualReq EstimatedReqi / ActualReq () FitessDesi = ActualDes EstimatedDesi / ActualDes () FitessCodei = ActualCode EstimatedCodesi / ActualCode (). The fitess fuctios (Mea Magitude of Relative Error MMRE) for each artifact are calculated as the average value of all proects specific fitess values calculated durig steps ad which depeds o the differece betwee real ad estimated Req, Des ad Code defects. So, the fitess fuctios values should be miimized. FitessReq = / * FitessDes = / * FitessReqi () FitessDesi (4) FitessCode = / * FitessCodei () FitessAll=(FitessReq+FitessDes+FitessCode)/ (). Fid the iteratio best solutio (optioal).. Update the soils of virtual edges that form curret best solutio accordig to IWD equatio i [] (optioal).. ed while. 4. Retur the values of ad the estimated Req, Des ad Code defects. i - the IWD umber, the proect umber, ActualReq - is actual software Req defects, EstimatedReqi - is the estimated software Req defects, usig IWD i, ActualDes - is actual software Des defects, EstimatedDesi - is the estimated software Des defects, usig IWD i, ActualCode - is actual software Code defects ad EstimatedCodesi - is the estimated software Code defects, usig IWD i. Fig.. Proposed IWD algorithm. Pr. No TABLE I: ESTIMATED DEVELOPMENT DEFECTS VALUES BY CURRENT COQUAMO COEFFICIENTS FPs Actual Req Defects/FP Req Defects /FP Calculated by Curret COQUAMO Des Defects/FP Calculated by Curret COQUAMO Code Defects /FP Calculated by Curret COQUAMO TABLE II: ESTIMATED DEVELOPMENT DEFECTS VALUES BY OPTIMIZED IWD COEFFICIENTS Pr_No FPs Actual Req Defects/FP Req Defects/FP Calculated by Optimized IWD Des Defects/FP Calculated by Optimized IWD Code Defects /FP Calculated by Optimized IWD ISBN: ---- ISSN: - (Prit); ISSN: - (Olie) WCECS

5 Proceedigs of the World Cogress o Egieerig ad Computer Sciece Vol I WCECS, October -,, Sa Fracisco, USA Pr_No TABLE III: COMPARISON AMONG REQ, DES AND CODE DEFECTS VALUES VS. SIZE BY IWD AND COQUAMO 4 FPs Compariso amog actual ad COQUAMO Req,Des ad Code defecs values vs estimated proect size i FPs.. Actual Req Defects/FP Req Defects /FP Calculated by Optimized IWD Req Defects/FP Calculated by Curret COQUAMO Proect Size (FPs)... Des Defects/FP Calculated by Optimized IWD Des Defects/FP Calculated by Curret COQUAMO Fig. 4. Compariso amog defects values by COQUAMO Code Defects/FP Calculated by Optimized IWD Actual Req Defects/FP Code Defects /FP Calculated by Curret COQUAMO Req Defects /FP Calculated by Curret COQUAMO Des Defects/FP Calculated by Curret COQUAMO Code Defects /FP Calculated by Curret COQUAMO 4 Compariso amog actual ad optimized Req,Des ad Code defecs values vs estimated proect size i FPs Actual Req Defects Proect Size (FPs) Fig.. Compariso amog defects values by IWD. 4 Req Defects Calculated Actual Des Defects Des Defects Calculated Actual Code Defects Code Defects Calculated Compariso amog actual,optimized ad COQUAMO Req defecs values vs estimated proect size i FPs Proect Size (FPs).. Actual Req Defects Req Defects Calculated Req Defects Calculated by Curret COQUAMO Fig.. Compariso amog Req defects values by IWD ad COQUAMO. 4 Compariso amog actual,optimized ad COQUAMO Des defecs values vs estimated proect size i FPs Proect Size (FPs).. Actual Des Defects Des Defects Calculated Des Defects Calculated by Curret COQUAMO Fig.. Compariso amog Des defects values by IWD ad COQUAMO. 4 Compariso amog actual,optimized ad COQUAMO Code defecs values vs estimated proect size i FPs Proect Size (FPs).. Actual Code Defects Code Defects Calculated Code Defects Calculated by Curret COQUAMO Fig.. Compariso amog Code defects values by IWD ad COQUAMO. Table IV compares the MMRE (Mea Magitude of Relative Error) ad PRED (.) (predictio (.) which shows the performace of IWD ad COQUAMO i estimatig the Req, Des ad Code defects for the whole dataset. ISBN: ---- ISSN: - (Prit); ISSN: - (Olie) WCECS

6 Proceedigs of the World Cogress o Egieerig ad Computer Sciece Vol I WCECS, October -,, Sa Fracisco, USA MMRE = * MRE = Actual - Estimated Actual Actual - Estimated Actual () () PRED (p) = k / () k is the umber of proects where MRE is less tha or equal to p, ad is the total umber of proects. The graphical compariso described i table IV is show i figure. TABLE IV: PERFORMANCE MEASURE COMPARISON Results IWD COQUAMO MMRE_Req.4. MMRE_Des..4 MMRE_Code..44 Total MMRE.4.4 PRED_Req (.). PRED_Des (.).4 PRED_Code (.).4.4 Total PRED (.)..... MMRE_Req MMRE_Des MMRE_Code Performace Compariso Total MMRE PRED_Req (.) PRED_Des (.) Fig.. Performace measure compariso. PRED_Code (.) Total PRED (.) IWD COQUAMO From table IV ad figure, the MMRE of IWD for Req, Des ad Code defects is lower tha that of COQUAMO ad PRED(.) of IWD for Req, Des ad Code defects is larger tha that of COQUAMO. It shows clearly that optimized by IWD algorithm produces more accurate results tha the old. So, IWD algorithm ca offer some sigificat improvemets i accuracy ad has the potetial to be a valid additioal tool for the software quality estimatio curret COQUAMO model. The results show that, i the sample proects take from the dataset, the results obtaied usig the optimized with the proposed algorithm are better tha the oes obtaied usig the curret. The results also show that i the sample proects take from the dataset, the results obtaied usig the optimized with the proposed algorithm are close to the real defects values. The results also show that i the whole dataset, the MMRE of IWD is less tha that of COQUAMO ad PRED(.) is larger tha that of COQUAMO. I the future work or the ext paper, we adapt PDBO algorithm for optimizig the of COQUAMO ad compare it with IWD algorithm. REFERENCES [] Gary B. Shelly, Harry J. Roseblatt, Systems Aalysis ad Desig Nith Editio, Shelly Cashma Series,. [] Vahid Khatibi, Dayag N. A. Jawawi, Software Cost Estimatio Methods: A Review, Volume No., Joural of Emergig Treds i Computig ad Iformatio Scieces,. [] Sweta Kumari, Shashak Pushkar, Performace Aalysis of the Software Cost Estimatio Methods: A Review, Iteratioal Joural of Advaced Research i Computer Sciece ad Software Egieerig, Volume, Issue, July. [4] Astha Dhima, Chader Diwaker, Optimizatio of COCOMO II Effort Estimatio usig Geetic Algorithm, America Iteratioal Joural of Research i Sciece, Techology, Egieerig & Mathematics,, [] Nasa dataset cotais effort ad defect iformatio available at dem/asa-dem.arff. [] Hamed Shah-Hosseii, The itelliget water drops algorithm: a ature-ispired swarm-based optimizatio algorithm, It. J. Bio- Ispired Computatio, Vol., Nos. /,. [] Priti Aggarwal, Jaspreet Kaur Sidhu, Harish Kudra, applicatios of itelliget water drops, IISRO, Multi-Coferece, Bagkok,. [] Hamed Shah-Hosseii, A approach to cotiuous optimizatio by the Itelliget Water Drops algorithm, 4th Iteratioal Coferece of Cogitive Sciece (ICCS ) [] Mrial Sigh Rawat, Saay Kumar Dubey, Software Defect Predictio Models for Quality Improvemet: A Literature Study, IJCSI Iteratioal Joural of Computer Sciece Issues, Vol., Issue, No, September. [] Suita Chulai, results of Delphi for the defects itroductio model (sub-model of the cost/quality model extesio to COCOMO II), Ceter for software egieerig,. [] Suita Chulai ad Barry Boehm, Modelig Software Defect Itroductio ad Removal: COQUALMO (Costructive Quality Model), USC - Ceter for Software Egieerig, Los Ageles, CA -,. [] Capers Joes, software defect origi ad removal methods, Draft., December,. VII. DISCUSSION ad CONCLUSION This paper adapts IWD algorithm for optimizig COQUAMO. The proposed algorithm is tested o NASA dataset ad the obtaied results are compared with the oes obtaied usig the curret COQUAMO model. The proposed model is able to provide good estimatio capabilities. It is cocluded that By havig the appropriate statistical data describig the software developmet proects, IWD based ca be used to produces better results i compariso with the results obtaied usig the ISBN: ---- ISSN: - (Prit); ISSN: - (Olie) WCECS