Journl of Computer Science 2 (3): 276-282, 2006 ISSN 1549-3636 2006 Science Publictions Softwre Cost Estimtion Model Bsed on Integrtion of Multi-gent nd Cse-Bsed Resoning Hsn Al-Skrn Informtion Technology College, MIS Deprtment, Yrmouk University, Irbed, Jordn Abstrct: Accurte softwre cost estimtion is vitl tsk tht ffects the firm's softwre investment decisions before committing required resources to tht project or bidding for contrct. This study proposes n improved Cse-Bsed Resoning () pproch integrted with multi-gent technology to retrieve similr projects from multi-orgniztionl distributed dtsets. The study explores the possibility of building softwre cost estimtion model by collecting softwre cost dt from distributed predefined project cost dtbses. The model pplying method to find similr projects in historicl dt derived from mesured softwre projects developed by different orgniztions. Key words: Mobile gent, COCOMO,, mgnitude of reltive error INTRODUCTION Softwre becomes incresingly expensive to develop nd is mjor cost fctor in ny informtion system budget. The ccurcy of estimtion of softwre project cost hs direct nd significnt impct on the qulity of the firm s softwre investment decisions. Mngement crefully considers costs nd benefits of softwre before committing the required resources to tht project or bidding for contrct. Accurtely estimting new softwre project is still gol of every project mnger. Unfortuntely such preliminry estimtion is difficult to mesure becuse there is little informtion bout the project t n erly stge. Over- or under-estimtion of softwre costs my result in costly errors such s projects re rejected s too expensive; projects my omit importnt fetures; projects re bndoned. Accurte project estimtion cn reduce these unnecessry costs nd increse the orgniztion's efficiency nd effectiveness. Most of tody's softwre cost estimtion models re built on using dt from projects of single orgniztion. Using such dt hs well known benefits such s ese of understnding nd controlling of collected dt. But different reserchers hve reported contrdictory results using different softwre cost estimtion modeling techniques. Myrtveit nd Srensrud [1] stte tht it is still difficult to generlize mny of the obtin results. This is due to the chrcteristics of the dtsets being used nd dtsets smll size. The fct, tht mny studies rely on using orgniztion-specific dtsets, mkes the results more bised becuse the dt t hnd re specific to given orgniztion. Brind [2] found tht cost estimtion models using single-compny dtset do not perform significntly better thn models using multi-compnies dtset. Chrcteristics of the dtsets being used ply mjor role. It hs been estblished tht relying on orgniztionspecific dtsets leds to poor softwre cost predictions due to the following problems [3, 4] : * It's too expensive to collect dt on previous projects from single orgniztion. * Informtion bout older projects my no longer be vlid or pproprite due to the new technologies tht orgniztion is using. * It's difficult to ensure consistency of the collected dt. Mssively collected dt bout softwre projects present n interesting spect of softwre cost estimtion. One purpose of this reserch is to ddress the issues of the dtset chrcteristics nd usge of lrge number of dtsets. This study is bsed on selecting nd using lrge number of dtsets coming from distributed softwre project dtbses of different orgniztions of comprble domins. This pproch supports the fst construction of cost estimtion models, it lso helps orgniztions, who do not hve their own dt or expertise, to ccess externl dt come from similr types of projects to build their own cost estimtion model; provides lrger nd up to dte project dtsets. Recent reserch hs demonstrted the potentil of the use of Artificil Intelligence (AI) methodology to estimte the cost of softwre to provide both consistency nd more ccurte estimtes. This study presents n lterntive pproch of softwre cost estimtion bsed upon n AI methodology, nmely Cse Bsed Resoning (), similr to the one pplied by Shepperd et l. [5], combined with mobile gent technology. The pproch mkes use of previous experience to solve newly encountered problems. The pst experience is recorded in cse Corresponding Author: Hsn Al-Skrn, Informtion Technology College/MIS Deprtment, Yrmouk University/Irbed /Jordn, Tel: 962-2-7211111, Fx: 962-2-7274725 276
bse dtbse. When new problem emerges, the system retrieves projects from the dtbse to find similr cses to the current problem nd the closest mtch is modified to fit the new problem. The modified cse lso will be stored in the cse bse s lerned cse to sve the experience nd cn be reused in the future. In problem-solving is seen s process, which involves the retrievl of similr prior cses from cse bses using mobile gent methodology nd the dpttion of retrieved cses solutions to fit the new problem s requirements. Relted work: Severl different directions in the reserch on the estimtion of softwre development cost emerged during the lst two decdes. Some potentil solutions of the bove problem hve been developed bsed on lgorithmic models (e.g. Constructive Cost Model (COCOMO, COCOMO II [6, 7], Function Points, Price-to-win nd SLIM [8] ), expert judgment nd estimtion by nlogy. Most of the lgorithmic softwre estimtion models re bsed on nlyticl methods nd derived from the sttisticl or numericl nlysis of historicl projects dt. The generl form of eqution used by COCOMO nd Function Points methods cn be represented s: y E = xs, Where, E is effort, S is size mesured s number of lines of code or function points, x is productivity prmeter nd y is economics of scle prmeter. COCOMO model provides three equtions ccording to the project development mode (embedded, semidetched nd orgnic). Their prmeters need to be djusted to locl circumstnces. The relesed version of COCOMO II hs been used to conduct empiricl nlysis of the model. The generl form of eqution used by COCOMO II is: E = X [ S] B+ SF EM, Where, X is bseline multiplictive constnt, B is bseline exponentil constnt, SF re scle fctors (understnding product objectives, flexibility, tem coherence, etc.), EM re effort multipliers (softwre relibility, dtbse size, reusbility, complexity, etc.) None of mentioned bove methods hve been shown to be convincing or consistent in solving the problem. Some of these lgorithmic methods my led to reltive errors s high s 600% [9]. The prediction ccurcy is mesured bsed on stndrd metrics such s Mgnitude of Reltive Error (MRE). MRE is defined s: MRE = Effort ctul Effort Effort ctul estimted If the vlue of MRE is lrge, then the model overestimtes the cost, while lrge negtive vlue would indicte, tht the model under-estimtes the softwre cost. Reserchers hve begun to turn their ttention to non-lgorithmic methods nd in prticulr, to set of pproches bsed on expert judgment, rule bsed, neurl networks nd cse bsed resoning. Expert judgment methods rely on the use of humn expertise to estimte softwre cost [10]. These techniques re useful in the bsence of quntified empiricl dt nd re bsed on prior knowledge of experts in the field. Insted of strting estimting softwre cost from scrtch, softwre mngers rely on their pst experiences nd understnding of the problem. They ttempt to find pst cses similr to the new project nd to dpt old estimtions to fit the new sitution. However, this humn-bsed pproch lcks consistent nd systemtic procedure for cost estimtion nd s result, might led to over- or under-estimtion of the cost of the softwre project. The mjor drwbck of this method is tht n estimte is only s good s the expert s opinion. The rule bsed systems cn be used for estimtion when no further rules re fired up from known or new fcts. This technique hs been dopted from the Artificil Intelligence domin where known fct fires up rules, which in turn my ssert new fcts. Kellner nd etc. Kellner, Mdchy nd Rffo [11] developed rule bsed system to estimte the cost of softwre. In the lst decde, significnt effort hs been put into the development of softwre estimtion models using neurl networks [12]. Neurl networks re bsed on the principle of lerning from exmple; no prior informtion is specified. Neurl network estimtion models must be trined by providing them with historicl project dt input vlues (project size, complexity, skill levels, etc) nd utomticlly djusting their lgorithmic prmeter vlues until it is very good t predicting results for the trining dt set. These models suffer from the sme kinds of sttisticl problems with the trining dt s the lgorithmic techniques. Very lrge dt sets re needed to ccurtely trin neurl networks. Estimtion by nlogy bsed on the comprison of the softwre under considertion with similr projects. There is no single best softwre cost estimtion model, but method is rted mong the best methods in vriety of circumstnces [13]. Experiment showed tht pproch provides better ccurcy thn lgorithmic methods. systems del only with those problems tht occur in prctice, while lgorithmic system must hndle ll possible problems. solutions re derived from form of resoning which close to the humn problem solving s opposed to rule bsed or neurl nets. cn operte in circumstnces where it is not possible to generte n lgorithmic 277
model (no sttisticlly significnt reltionships could be found). Cse bsed resoning: Cse Bsed Resoning () hs been ttrcting much ttention recently s prdigm with wide vriety of pplictions. In this study, issues relted to construction of cost estimtion model nd composition of cse, where subcses re distributed cross different distributed dtbses, re discussed. is n AI methodology combined with dtbse of cses relted to the topics under considertion for re-using pst experience. In this pproch, resoner tries to remember previous cses similr to the current one nd uses them to solve the current problem. The systems store therefore numerous cses relted to the mtters considered. Often the pst experiences provide importnt clues or direct nswers to the current problem. technique ws described by Amodt nd Plz [14] s combintion of the following four processes nd shown in Fig. 1. * Retrieve previously experienced cse or cses relted to the current problem. * Re-use this or these cse(s) in one wy or nother. * Revise the solution bsed on re-using previous cses. * Retin the new solution (s new cse) by dding it into the existing cse-bsed dtbse. In such wy, system will grdully grow lrger nd become precious resource. Given Problem New Cse Mp Retrieve Fig.1: The cycle Adpt Add Most Similr Cse Approved Cse Revise Cse-bsed resoning hs severl dvntges: New Cse * Mny erly studies showed tht presented better prediction ccurcy thn other models. * method reflects the sme method tht humn experts use when mking estimtes by pplying nlogicl resoning. * cn hndle both quntittive nd qulittive dt * systems cn use existing solution nd dpt it to the current sitution. * systems cn be implemented very quickly. * is simple nd flexible, compred to lgorithmic models. Test * To dd new knowledge to system, user only needs to dd new cses to the system. * cn effectively support ll the steps in the softwre cost estimtion process from storing pst cses, retrieving similr cses to dpting the retrieved cse for the new project. * pproch tkes dvntges of expert prior knowledge * systems cn hndle filed cses. (Identify potentilly high risk situtions. Recent reserch by Shepperd's group [5] hs creted new utomted pproch to use in softwre cost estimtion. The pproch is very successful in providing ccurte estimtes. In order to find cse from lrge number of cses, the similrity of cses should be nlyzed. The estblishing similrity of cses is the bsis of nd cse serching. Similrity of cses is influenced by set of ttributes which mke the cse different from others. These ttributes re the key ttributes of cse. The cses which hve one or more similr key ttributes re similr. Every key ttribute of cses represents the cses from specific perspective. Project size, trget pltform, qulity of system requirements re some of the ttributes tht cn ct s key ttributes. One of the more likely used key ttributes is the project size which represents the number of lines of code the project will hve. It cn be estimted using different techniques such s Genetic Progrmming nd Neurl Networks [15]. Some ttributes, such s development environment, ppliction type, business re type nd others, cn ct s sub-key ttributes. Cse serching model is used to compre nd filter the cses from the cse bse to find similr cses. The cse serching model is bsed on the key nd sub-key ttributes. To mke the cse serching model more effective, cse index reflecting the min feture of the cses is build. This index is recommended especilly when the volume of the cse bse is lrge. Mobile gent: Mobile gents cn be defined s utonomous, problem-solving computtionl entities cpble of effectively performing opertions in dynmic unpredictble environments. Such environments re known s multi-gent systems. Agents interct nd mybe cooperte with other gents. They re cpble of exercising control over their ctions nd interctions. Using mobile gent technology solves the problem of heterogeneity of networks, low bndwidth of communiction chnnels, reduces network trffic by processing dt loclly insted of trnsmitting the dt over network. It could ccelerte development with gent components nd enhnce modulrity, reusbility, flexibility nd relibility. A mobile gent consists of two different prts: the code itself, which composes of the instructions tht 278
define the behvior of the gent nd its intelligence nd the current stte of execution of the gent. At lest three mjor requirements hve to be fulfilled for mobile gent to perform its job. They re common execution lnguge cross heterogeneous networks, for exmple Jv; trnsference of gents cross networks through communiction mechnism, for exmple MAP, TCP/IP, HTTP, or SMTP; protection of gents ginst hostile server nd gent server from mlicious gent. Agent my protect their dt nd informtion by using encryption/decryption techniques. A multi-gent system is composed of intelligent gents working towrds finding most similr cses. Agents ccess cse bses to retrieve the best mtching cse. In such system, ech of the gents my not be individully cpble of finding the best similr cse. Ech gent my retrieve the best locl cses, which, when ssembled, my not result in the best overll cse in terms of globl mesures. But coopertion mong them my led to chievement of the finl gols of finding the most similr cse or cses. Tht mens the cost prediction of project does not just rely on few projects stored loclly, but ffected by lrger size of dt (distributed dtsets). The min chrcteristics of Intelligent Agents within environment re: * Autonomy: the bility of gents to mke independent decision; * Ability to utonomously lern from experience; * Gol-driven: tht is the provision of detiled knowledge so tht gols cn be chieved; * Mobility: it llows the routing of gents through distributed system; * Rectivity: recting to chnges in the environment; * Ability to cooperte: group of gents work together to chieve common gol; * Ability to communicte: the gents must to be ble to communicte with other gents nd/or user. Agents require knowledge of the current sitution, skills to ccomplish tsks nd mke decisions on how to ct. Ech client gent will serch in locl cse bse. And ech one is ssocited with set of constrints representing the requirements of the softwre cost estimtion model. Client gents use centrlized serch mechnism to find n optiml or prtilly optiml projects to given problem instnce. Cndidte projects re stored t the dt structure of the gent. The client gents cn be simply divided into three types of gents: * Interfce gents: to communicte with the client * Mobile informtion gents: to collect informtion from distributed informtion resources. * Tsk gents: to solve the problem by selecting the best solution from the ccumulted informtion collected by the mobile informtion gents. J. Computer Sci., 2 (3): 276-282, 2006 279 Adpttion of nd mobile gent pproches to cost estimtion model: The complementry properties of nd mobile gent cn be dvntgeously combined to solve the softwre cost estimtion problem, where ny single technique fils to provide stisfctory solution. The mobile gent cn be effective in ddressing the problem of getting dt from different compnies. Within this pproch, softwre cost estimtion process consists of the following components: * Forml cse representtion: pst cses should be clssified by their ttributes nd focus on specific group of cses relevnt to the current sitution. * Identifiction of project ttributes for which n estimte is required. These ttributes re used s bsis for finding similr pst projects of known costs. * Hierrchicl cse indexing: construction of n efficient cse indexing technique to reduce serch time for retrieving similr cses. The closeness between pst cse nd new one is ssessed bsed on similrity metric for ccurte cse mtching. * Knowledge - bsed cost estimte dpttion: serch for the relevnt knowledge in cse nd if there is no complete mtch between the retrieved cse(s) nd the new one, revision of the existing solution to fit new problem. The most importnt softwre cost fctors (ttributes) to be considered in method re: * Project size (the size of source code mesured in number of lines of codes, number of HTML pges, or functions points) * Orgniztion type (Mnufcturing, bnking, services, dministrtion); * Trget pltform (minfrme, Network, PC, etc.); * Qulity of system requirements; * Development type (new development, redevelopment, enhncement); * Business re type ( engineering, sles, legl, inventory); * Appliction type (trnsction system, office utomtion, mngement informtion system, executive informtion system); * Project security ( need for security); * Complexity of the softwre; * Stff experience, vilbility nd skills; * Development environment; * Others (the volume of documenttion, the number of developers, the number of different files creted, the number of bugs reported nd so on). Cse representtion scheme is dependent on the cse size nd the complexity of the ttributes describing the cse. A cse of the softwre cost estimtion system
consists minly of three prts: the description prt, solution prts nd the reltionship prt. The description prt contins the ttributes vlues describing the behviors of the cse, while the solution prt contins the solutions. The reltionship prt describes the reltionship informtion mong cses. Multiple cses cn be use to represent single problem. When the number of cses in cse-bse is very lrge, it is importnt to formulte indexing technique tht helps locte similr cses close to ech other efficiently. Dvid W. Ptterson nd others in [16] propose two efficient indexing schemes designed for use in systems. The first one is bsed on mtrix of cses indexed by their ttribute vlues. The second one is n extension of the first one by combining the mtrix with n dditionl tree-like indexing structure. The strength of these techniques lies in its bility to improve retrievl efficiency over time by reusing previously encountered solutions. solves the softwre cost estimtion problem in the following wy. The ttributes or fetures of the current problem (project) re identified. Then the current problem is mtched ginst the cses (projects) in the cse bse (using the most importnt ttributes) nd most similr cses (with known cost) re rnked. The most similr cse from these rnked cses is retrieved. Serching for similr cse is not only by the fetures in the description prt but lso by the reltion mong cses. If the retrieved cse completely mtches the current problem, it is used to suggest solution, which is reused nd tested for success. If prtil mtch occurs, then the proposed solution is revised nd dpted to fit new needs [17]. Retrieving cse strts with identifying set of relevnt descriptors (cost fctors), such s softwre size (number of lines of code), function points, security needs, use of softwre tools, etc. nd ends when best mtching cse hs been selected. The finl solution becomes new cse in the cse bse librry. The degree of similrity in is ssessed by mens of mtching function such s the Nerest Neighbor (NN) mtching function [18] : Similrity ( N, P ) = n i = 1 f ( N i = 1, P ) * w Where N is the new cse (new project), P is the previous cse (previous project), n is the number of fetures in ech cse, i is n individul feture from 1 to n, f is mtch function for ttribute in cses N nd P nd w is the weight of the -th ttribute which reflects the reltive importnce of tht ttribute. It is possible tht more thn one cse will hve the sme vlue of similrity coefficient or the vlues of similrity coefficients for different cses cn be very close. To select the most suitble cse from these n w, cndidte cses, the system, through the tsk gent, will suggest the best cse to choose. The overll frmework of the proposed system is presented in Fig. 2. It is composed of three different mjor components: front end user mchine, bck end server nd the softwre cost estimtion servers on the web. The system hs number of gents. Ech gent is designed to represent specific functionl unit. This requires three different gent types, one mobile nd two sttic (interfce gent, tsk gent nd mobile informtion gent). The client t the front end user mchine intercts with the system through web browser. The bck end server hs dtbse storing the previous projects, tsk mnger gent nd mobile informtion gent. The mobile informtion gent will visit the softwre cost estimtion servers on the web. Ech time client conduct serch, serching criteri will be generted t the bck end server nd sent by tsk gent s dt, stored in the mobile informtion gent, into the web. The mobile informtion gent will rom the web serching for the required informtion bsed on the given criteri. When informtion is found, the mobile informtion gent will send it bck to the tsk gent t the bck end server where it will be filtered nd then presented to the user. The informtion mobile gent serches cse bse for the most similr project ccording to the similrity metric nd uses it s cndidte project. End User Computer Web Browser Cost Estimtion Server Agent Server INTERNET Bck End Server Interfce Agent Tsk Agent Server Mobile Agent Cost Estimtion Server Agent Server Fig. 2: Architecture of the softwre cost estimtion model The process of estimtion of softwre cost by nd multi-gent consists of the following steps: After the client communictes with the system through interfce gent, ech client gents should execute the following lgorithms: Ech mobile informtion gent executes: Do locl retrievl: Select projects of the sme types, similr ppliction domins, size, etc. (to be defined by the client) from dtbse. The tsk gent executes: 280
* Receive cndidte projects from ech mobile informtion gent; * Merge cndidte projects; * Choose best project(s). If project is retrieved, then the ttributes tht do not mtch the ttributes of the current project will be grouped s new project nd these unmtched ttributes re used to do the retrievl gin. This process will be repeted until ll ttributes re collected nd the tsk mnger will merge the collected projects. If there re mny projects which hve the sme mount of ttributes, the one which is the most similr (the nerest neighbor) [19] to the current project will be selected by the tsk gent. A prototype of the softwre cost estimtion system is implemented using Jv so tht the system cn run on heterogeneous pltforms nd inference engine. The implementtion consists of the server side nd the client side connected through the Internet. The server side consists of the server gents nd dtbse. The client side consists of browser tht hs support for XML nd Jv pplets. Internet informtion server nd servlets re used for the web servers (Jv Servlet Clss running in server). MS-SQL is used for dtbse progrmming. Communiction between gents estblished through Jv Agent Development. The softwre gents communicte with ech other in XML messges. Request for similr projects is constructed t the client interfce gent. The min ttributes of the new project re entered. The tsk gent of the bck end server will do dt nlysis for this request though conducting cse identifiction using dtbse. The system is connected to the dtbse by JDBC to ccess the dt for initil reference of similr cses. The mobile informtion gents will crry the min ttributes nd their vlues nd then serch for similr projects in different web servers. The response results will be forwrded bck to the tsk gent to choose the best mtching project. The finl result will be presented to the client though his gent browser. CONCLUSION Softwre cost estimtion is n importnt nd hrd mngement tsk. This is due to the lck of informtion on mking decisions in the erly phses of the project development. In this study, new hybrid softwre cost estimtion model, which integrtes cse-bsed resoning nd multi-gent technology, hs been presented. The study described the ppliction of csed-bsed resoning to estimting the cost for developing softwre project using multi-orgniztion dtbses integrted with mobile gent technology. The mjor property of is sving the previous experience into cse bse nd reuse pst solved problems in order to propose solutions J. Computer Sci., 2 (3): 276-282, 2006 281 to new problems lter. The experience of solved problem cn be stored into the cse bse. Lrge collected softwre cost dt from different sources present n interesting spect of cost estimtion model, which my behve better thn models developed on projects coming from single dtbse. The proposed system my be used to produce estimtes for new projects by softwre orgniztions tht do not hve historicl projects cost dt or just strting up their softwre business. Future work primrily involves conducting experiments on sensitive empiricl dt coming from different sources using the proposed integrted pproch of nd multi-gent techniques. REFERENCES 1. Myrtveit nd E. Srensrud, 1999. A controlled experiment to ssess the benefits of estimting with nlogy nd regression models. IEEE Trns. Softwre Engg., 25: 510-525. 2. Brind, L., E.L.K. Emm nd K. Mxwell, 1999. An ssessment nd comprison of common softwre cost estimtion modeling techniques. Intl. Conf. Softwre Engg., Los Angeles, CA, pp: 313-322. 3. Brind, L.C., T. Lngley nd I. Wieczorek, 2000. A replicted ssessment of common softwre cost estimtion techniques. Proc. 22nd Intl. Conf. Softwre Engg, ICSE, pp: 377-386. 4. Mendes, E. nd B. Kitchenhm, 2004. Further comprison of cross-compny nd within-compny effort estimtion models for web pplictions. Proc. 10th Intl. Symp. Softwre Metrics (METRICS 04), pp: 348-357. 5. Shepperd, M. nd C. Schofield, 1997. Estimting softwre project effort using nlogies. IEEE Trns. Softwre Engg., 23: 12. 6. Boehm, B. nd E. Horowitz et l., 2000. Softwre Cost Estimtion with COCOMO II. Prentice-Hll. 7. Clrk, B., S. Chulni nd B. Boehm, 1998. Clibrting the COCOMO II Post-Architecture Model. Proc. Intl. Conf. Softwre Engg., pp: 477-480. 8. Chulni, S., B. Boehm nd B. Steece, 1999. Byesin nlysis of empiricl softwre engineering cost models. IEEE Trns. Softwre Engg., 25: 573-583. 9. Kemerer, C., 1987. An empiricl vlidtion of softwre cost estimtion models. Commun. ACM, pp: 416-429. 10. Host, M. nd C. Wohlin, 1998. Experimentl study of individul subjective effort estimtion nd combintions of estimtes. Proc. Intl. Conf. Softwre Engg., pp: 332-339. 11. Kellner, Mdchy nd Rffo, 1999. Softwre process modeling nd simultion: Why, wht, how. J. Systems nd Softwre, 46: 91-105.
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