Estimating the Development Effort of Web Projects in Chile

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1 Estmatng the Development Effort of Web Projects n Chle Sergo F. Ochoa Computer Scences Department Unversty of Chle (56 2) M. Cecla Bastarrca Computer Scences Department Unversty of Chle (56 2) Germán Parra Computer Scences Department Unversty of Chle (56 2) Abstract Ths artcle presents a method to fast estmate the development effort of Web-based nformaton systems n Chle. The method, called Chlean Web Applcaton Development Effort Estmaton (CWADEE), addresses a necessty to get effort estmatons n a short perod of 24 to 72 hours usng lmted nformaton. In contrast wth other exstng methods, CWADEE uses raw hstorcal nformaton about development capablty and hgh granularty nformaton about the system to be developed, n order to carry out such estmatons. Ths method s smple and specally suted for small or medum-sze Webbased nformaton systems. CWADEE has been appled to twenty-two projects wth very accurate results. Keywords Effort Estmaton Method, Szng Metrc, Web-based Informaton Systems, Web Engneerng. 1. Introducton Companes developng Web-based systems face the challenge of estmatng the requred development effort n a very short tme frame. Ths problem does not have a standard soluton yet. On the other hand, effort estmaton models that have been used for many years n tradtonal software development are not very accurate for Webbased software development effort estmaton [16]. Web-based projects are naturally short and ntensve [12], so not havng an approprate effort estmaton model pushes developers to make hghly rsky estmatons [16]. Moreover, the rapd evoluton and growth of Web related technology, tools and methodologes makes hstorcal nformaton quckly obsolete. Although, the software effort estmaton process s a completely necessary and crtcal task, t stll looks more lke a craft than a scence [4][17]. The process s manly dependent of the project type and the features of the development scenaro. Ths work s focused on small and medum szed Webbased nformaton systems developed n mmature scenaros. Ths knd of projects represents about 50% of the projects that are beng developed n Chle today [18]. Chlean developers face a hghly compettve market, where clents requre cost estmatons n a matter of hours for ther projects, and these estmatons should be accurate f they want the development to be cost effectve. In large projects, the estmaton s less problematc because there s always tme scheduled for carryng out ths process. These estmatons are the bass of the budget gven to the clent. Based on such budget the software development companes sgn contracts wth the clent. In other words, the effort estmaton carred out for budget purposes usually establshes the busness rules for the project. Wthout an approprate model, cost estmaton s done wth a hgh uncertanty and the development effort estmaton reles only on the experence of an expert, whose estmatons are generally not formally documented. Ths expert knows well the development capabltes of the company and s able to nterpret the clent's requrements wth hgh accuracy. Chlean development companes rarely change the expert n charge of effort estmaton, n contrast to what occurs wth other personnel nvolved n Web-based software development. Ths person usually becomes a bottleneck for budget delverng. Therefore, the estmaton process can be slow and/or lttle accurate. Ths s a crtcal stuaton for development. Chlean clents are budget centrc, beng such budget the base of the contract. In order to deal wth such problem we have been studyng the last 3 years the software development process n Chle, orented to the development of small and medum sze Web-based nformaton systems. Based on the analyss of these results, we dentfed a low usablty of the well-known effort estmaton methods and a necessty of a model to support estmaton n such scenaro. Due that, we developed a method for fast estmatng the Web-based software development effort and duraton, whch s adapted to development of Webbased projects n Chle. We called t Chlean Web Applcaton Development Effort Estmaton (CWADEE). The method s specfcally applcable to estmate the development effort of small to medum-sze Web-based nformaton systems n mmature development scenaros.

2 It s probable that ths development scenaro s smlar to those n other Latn Amerca's countres, although currently we cannot assure t. Local tradtons are dffcult to change. CWADEE does not replace the expert n charge of effort estmaton process. Instead, t provdes hm/her wth a tool for makng more precse estmatons and based on more sound bases n the short tme avalable for such task. Based on our prevous studes and knowledge of the Chlean scenaro, we estmate that the current error range n development effort estmatons for small and medum sze projects s between 50 and 200%. The proposed method ntends to support the experts n order to reduce ths range. Next Secton presents a more detaled descrpton of the Chlean effort estmaton scenaro. Secton 3 dscusses the work done n software development effort estmaton and methods and tools specfcally for Web-based systems. Secton 4 descrbes the CWADEE method and ts man components. Secton 5 presents and analyzes the results obtaned n our experence applyng t. Fnally, Secton 6 presents the conclusons and the future work. 2. Effort Estmaton Scenaro The Chlean effort estmaton scenaro s not completely dfferent from other scenaros, but due to some of ts characterstcs, well-known effort estmaton methods have low applcablty. The central characterstcs are the followng: Expert centered. An expert s n charge of the effort estmaton process, who knows the development capablty of the organzaton. He/she s responsble for calculatng the budget for clents and to establsh development compromses. The expert s experence s very useful for the company but not necessarly good for other companes. Companes rarely rsk recrutng new people for ths task because the learnng cost of the expert s hgh. Because of ths feature, the amount of experts n each development company s stable and usually qute low tryng not to ncrease the fx costs of the company. These experts are expensve due to the responsblty they have. Lttle hstorcal nformaton. Development companes generally have lttle hstorcal nformaton about past projects. Usually, they have the products produced by the project and an approxmaton of the total amount of man-hour spent on t. Besdes, such nformaton s usually unorganzed and t s perceved by the expert as not very relable because of ts ncompleteness. Short tme to estmate. Development companes generally have between 24 and 72 hours to estmate the development effort of small or medum szed projects, from the moment the clent provdes the Web applcaton requrements and nformaton. In that tme, the experts should analyze and clarfy the nformaton provded by the clent, revse hstorcal nformaton, carry out the estmaton, and buld the budget. Gross ganed nformaton. The nformaton gven by the clent about the problem to be solved tends to be gross graned and could have unclear areas for the clent. The clent wants the estmaton to nclude some flexblty to adjust such unclear ssues. Therefore, a lot of experence s requred from the estmator to dmenson the system realstcally only based on hgh level nformaton. Qute fxed development tme. Generally, the development projects arse as response to mmedate needs of the clents. Therefore, the development tme for a project s qute fxed and the estmaton process becomes a problem of feasblty and/or money. Budget centrc. The Chlean scenaro s budget centrc. Typcally, the clents request budgets to several development companes and usually the lowest development cost s chosen. Ths means that the developer should prepare budgets for many clents whch could make the experts to become a bottlenecks. On the other hand, work of such experts s an nvestment wth low probablty of success, because several budgets are requested and only one s chosen. These features make the Chlean effort estmaton scenaro be hghly demandng and lttle motvatng. The well-known effort estmaton models have shown low applcablty to support ths process; therefore, t s currently carred out n a handmade way. Next Secton, presents and dscusses some related work. 3. Related Work From the begnnng of software engneerng as a research area more than three decades ago, several development effort estmaton methods have been proposed. We can classfy these methods for our research as those for tradtonal software and those for Weborented software. The tradtonal effort estmaton methods are those used to estmate the development effort of software that conssts of programs n a programmng language, whch eventually nteract wth data fles or databases. Generally, these software have an actve executon thread that provdes system servces. On the other hand, the Web-orented methods use dfferent metrcs and they are focused on estmatng the development effort of products that are event-orented. These products generally nvolve code n a programmng language, magery, look-and-feel, nformaton structure, navgaton and multmeda objects. Tradtonal effort estmaton methods lke COCOMO [1][3] are manly based on metrcs lke Lnes Of Code (LOC) [15] or Functon Ponts (FP) [10][13]. The

3 estmaton strateges supported by LOCs have shown several problems. Most workng Web projects agree that LOCs are not sutable for early estmaton because they are based on desgn [16]. Other reported problem s that the work nvolved n the development of multmeda objects and look-and-feel cannot be measured n LOCs. Also, an mportant amount of relable hstorcal nformaton s needed to estmate effort usng ths metrc, and ths nformaton s hard to get n Web-based projects. Fnally, to carry out estmatons usng LOCs requres a long analyss of the hstorcal nformaton, whch reduces the capablty to get relable fast estmatons. Speed s an mportant requste of Web-based projects developed n Chle. Smlarly, tradtonal estmaton methods based on FPs are not approprate because applcatons do more than transform nputs to outputs,.e. the effort necessary for developng a Web-based applcaton s much more than the effort requred for mplementng ts functonalty. FPs do not consder the magery, navgaton desgn, look-andfeel, and multmeda objects, among others. In other words, the tradtonal categores of FPs should be redefned. Ths knd of estmaton also requres an mportant amount of relable hstorcal nformaton, whch supports the used values of each FPs. Although there are several software effort estmaton methods lke Prce-S, Slm and Seer [3], COCOMO s the most well known and used by the software ndustry. It had shown to be approprate n many development scenaros. The frst verson of such method used LOCs as the fundamental metrc to support the estmatons. Then, Boehm proposed COCOMO II, whch could use alternatvely LOCs, FPs or Object Ponts [2]. Although COCOMO II was not defned to support the development effort estmaton of Web applcatons, many people found the way to adapt the object pont concept n order to get a szng estmaton [3]. Object ponts are an ndrect metrc, smlar to FPs, whch consders three categores: user nterfaces, reports and components, whch are probably needed to develop the fnal product. Every element n the system s categorzed and classfed n three complexty levels: basc, ntermedate and advanced. Then, based on these classfed elements, and takng nto account the hstorcal nformaton, t s possble to generate a good estmaton. Object Ponts and COCOMO II seem to be acceptable for tradtonal or multmeda software projects, but they are not good enough to get accurate effort estmatons for Web-based nformaton systems developed n Chle. The complexty of the estmaton process and the need for detaled hstorcal nformaton make them dffcult to apply n ths scenaro. Several sze metrcs have been proposed for Web applcatons, lke Object Ponts, Applcaton Ponts and Multmeda Ponts [6]. However, the most approprate seems to be Web Objects (WO) [16]. WOs are an ndrect metrc that s based on a predefned vocabulary that allows defnng Web systems components n term of operands and operators. To estmate the amount of WOs that are part of a Web-based applcaton t s necessary to dentfy all the operators and operands present n the system. Then, they are categorzed usng a predefned table of Web Objects predctors and also they are classfed n three levels of complexty: low, average and hgh. The fnal amount of WO n a Web-based applcaton s computed usng the Halstead equaton [10], and t s known as the volume or sze of the system. The effort estmaton and the duraton of the development are computed usng WebMo (Web Model), whch s an extenson of COCOMO II [16]. Ths model uses two constants, two power laws, several cost drvers, and the product sze expressed n WO (see Fgure 1). Fgure 1. WebMo Effort Estmaton Model Constants A and B, and power laws P1 and P2 are defned by a parameter table n the model. Ths table contans the values obtaned from a database of former projects (hstorcal nformaton). The cost drvers are parameters used to adjust the effort and duraton n terms of the development scenaro. For ths model nne cost drvers were defned: product relablty and complexty (RCPX), platform dffculty (PDIF), personnel capablty (PERS), personnel experence (PREX), facltes of tools and equpment (FCIL), schedulng (SCED), reuse (RUSE), teamwork (TEAM) and process effcency (PEFF) [16]. Each cost drver has dfferent values that may be: very low, low, normal, hgh, and very hgh. The combnaton of WebMo and Web Objects s, n ths moment, the most approprate method to estmate the development effort of Web applcatons. However, ths combnaton does not seem to be the best for Chlean development scenaros because t needs an mportant amount of hstorcal detaled nformaton to carry out the estmaton. Also, the WO dentfcaton and categorzaton process s dffcult to carry out n a short tme, and t requres an expert that also knows how to carry out the project. The expert should be competent about crtcal techncal decsons of the development process because n small projects the techncal feasblty study s mplctly ncluded n ths estmaton [4]. Ths expert feature and the complexty to dentfy and classfy WO make WebMo unfeasble to use n Chlean scenaros. Provded that the effort estmaton methods presented are no approprate to estmate the development effort of Web-based nformaton systems n Chlean scenaros, n the next secton we present the CWADEE method. It ntends to be more approprate to estmate the

4 development effort of small or medum sze projects, especally n scenaros that requre fast estmaton wth lttle hstorcal nformaton. 4. The CWADEE Method In order to help the expert acheve more accurately effort estmatons n Chlean scenaros, CWADEE ntroduces a new szng metrc based on the data model of the nformaton system to be developed: Data Web Ponts (DWP). DWP s an ndrect szng metrc that takes nto account the characterstcs of Chlean experts, the avalable estmaton tme, the lack of a great amount of hstorcal nformaton, and the knd of nformaton (hgh level) used to estmate. The dea behnd the DWP s to dentfy the system functonalty, analyzng ts data model. Trujllo and Straub showed t s possble to nfer between 50 to 60 percent of an nformaton system functonalty followng a set of predefned patterns [20]. In ths way, DWP capture the essence of these patterns makng explct the relatonshp between the data model and the sze of the Web applcaton. Each pattern corresponds to one and only one knd of DWP, and vce versa. Informaton systems functonalty s largely determned by the data model. Generally, these systems are organzed as a combnaton of repostory and MVC patterns [5]. DWP takes advantage of the standard relatonshp between data model and functonalty n an nformaton system. Next secton descrbes the concept of Data Web Pont. Secton 4.2 presents the CWADEE effort estmaton method. Then, the followng sectons explan the components, whch are part of CWADEE. 4.1 The Data Web Ponts DWPs are smlar to other ndrect metrcs such as FPs [10][13], Object Ponts [2, 3], or Web Objects [16] n the fact that they represent abstract concepts that are used to obtan the sze of the system to be developed. In partcular, DWPs represent system functonalty from the pont of vew of ts data model. Therefore, two experts analyzng the same data model should get the same sze for a system, but the development effort could be dfferent dependng on partcular characterstcs of the development envronment such as: the expertse of the development team, and the avalable supportng tools, among others. DWP ntends to smplfy the effort estmaton process by gvng the expert better tools for mprovng or tunng up ths process. Ths metrc has the advantages of beng fast and easy to apply, whenever the requred gross gran hstorcal nformaton s avalable. Ths fact makes t especally sutable to estmate Web-based projects n Chle. Borrower user_d frst_name last_name password book_lmt Borrower C Checkout copy user_d lent due returned Copy Copy copy Call_Number status Staff user_d frst_name last_name password shft Book Publsher Name cty country sbn Author Ttle Year seres Publsher Prnts Call_Number name Book Book Call_Number Fgure 2. Example of an Informaton System of a Lbrary The types of enttes and relatonshps assocated to DWPs are the followng: regular, dependent, relaton, relatonshp 1 to N and relatonshp 1 to 1. Regular enttes are those that do not contan a foregn key and whose prmary key dentfes them (Borrower, Staff, Book and Publsher n Fgure 2). Dependent enttes are those related to a regular entty through a foregn key and whose prmary key s formed by the prmary key of a regular entty and a key of ts own (Copy). Relaton enttes separate a N to M relatonshp between two other enttes and they do not have a prmary key of ther own; ts key s formed by the prmary keys of the enttes partcpatng n the relatonshp (Checkout and Prnt). Ths type of entty can also have other attrbutes defned. Relatonshps 1 to N are establshed between a relaton entty and another entty, where one or more nstances of the relaton entty are assocated wth an nstance of the other entty (Borrower, Checkout, Copy Checkout and Book Prnt). Fnally, relatonshps 1 to 1 are those establshed between a relaton entty and another one, where one and only one nstance of the relaton entty s assocated wth an nstance of the other entty (Publsher Prnt). Takng nto account the patterns defned n [20], the dfferent categores of DWPs follow these patterns. In contrast to Fowler s analyss patterns [7] and Hay s data model patterns [9], here each type of entty and relatonshp that represents a pattern s consdered a

5 DWP. In order to facltate the explanaton of DWPs, we wll use a smple lbrary data model, shown n fgure 2. Each of these enttes represents a desgn pattern that has a typcal functonalty assocated wth t. Ths functonalty can be translated nto standard servces provded to Web-based nformaton systems. Trujllo has shown that not only servces, but also the strategy of usng the data model are useful as a bass for developng nformaton systems n the Chlean scenaro [20]. Type of DWP Table 1. Defnton of the DWPs amount Amount of DWP Weght Factor Total of DWP Regular Enttes 4 x 9 = 36 Dependent Enttes 1 x 9 = 9 Relatonshp Enttes 2 x 3 = 6 Relatonshp 1 a N 3 x 6 = 18 Relatonshp 1 a 1 1 x 3 = 3 Total of DWP = 72 Analyzng the data model we can easly dentfy DWPs through of a systematc process; so systematc that t could even be automated. Thus, we can fll the table of DWPs n Table 1 to calculate the system sze. The weght assgned to each category of DWP represents the development effort of each one, and t s based on the experence of the expert estmator. The expert can update ths weght every tme he/she beleves t s approprate. Table 1 shows the estmaton of the DWPs assocated wth the model shown n Fgure 2. The resultng sze value wll be consdered as the man factor n the CWADEE effort estmaton equaton (n secton 4.2). Ths metrc seems to be more usable for expert estmators n Chle, provded that most of them could apply the proposed data model analyss. The gross gran nformaton obtaned n ntervews wth the clent usually allows us to buld a good and fast approxmaton to the fnal data model. Then, the DWP can be drectly derved from ths model. Up to ths pont all the process s systematc and unambguous. The knowledge of the expert s requred for more challengng actvtes such as: defnng approprate cost drvers and weghts for DWPs. Generally, these tunng aspects have drect ncdence on the accuracy of the effort estmaton for ths knd of projects. 4.2 Effort Estmaton The DWPs are an approxmaton of the whole sze of the project; so, t s necessary to know what porton of the whole system DWPs represent. Ths knowledge s acheved through a relatvely smple process (brefly descrbed n Secton 4.3) and t s generally done by the expert estmator. Assumng that the estmaton factors n the computaton of the effort are subjectve, flexble and adjustable for each project, the role of the expert becomes very relevant. Once the value of the porton or representatveness s calculated, the expert can adjust the total number of DWPs and he/she can calculate the development effort usng the followng equaton. * P = cd E UC ( DWP (1 X )) + Where: E s the development effort measured n man-hours. UC s the User Cost. cd are each of the Cost Drvers. DWP corresponds to the Web applcaton sze n terms of Data Web Ponts. X* s the coeffcent of DWP representatveness. P s a constant. The estmated value of real Data Web Ponts (DWP*) s calculated as the product of the ntal DWPs and the representatveness coeffcent X*. Ths coeffcent s a hstorcal value that ndcates the porton of the fnal product functonalty that cannot be nferred from the system data model. The process of defnng such coeffcent s presented n the next secton. The user cost s descrbed n Secton 4.3, and t represents the system functonalty that s assocated wth each user type. The defned cost drvers (cd ) are defned n Secton 4.4, and they are smlar to those defned by Refer for WebMo [16]. The last adjustable coeffcent n CWADEE corresponds to constant P that s the exponent value of the DWP*. Ths exponent s a value very close to 1.00, and t must nether be hgher than 1.10 nor lower than Ths constant s value depends on the project sze measured n DWPs. In order to determne ths value, 22 Web-based projects developed n the Computer Scence Department at the Unversty of Chle were studed. As a result, ths constant was assgned the value 1.05 for projects smaller than 300 DWPs, and 1.02 for projects larger than 300 DWPs. 4.3 Identfcaton of the Representatveness Coeffcent The representatveness coeffcent ndcates the amount of addtonal effort could be needed to address the ssues that cannot be nferred from data model, takng nto

6 account the hstory of smlar projects. Among these ssues are: addtonal functonalty, magery, look-andfeel, multmeda objects, navgaton, and nformaton structure, among others. Ths coeffcent can evolve along the tme and t s referental; therefore the expert can adjust t for each project to get more accurate effort estmatons. In order to calculate the representatveness of DWPs, we should analyze smlar fnshed projects, takng nto account the system data model and the total man-hours for each project. Usng the nverse of the CWADEE equaton, we can obtan the value of DWP* (real Data Web Ponts) used n each project. Such equaton s the followng: DWP * = P E UC Obtanng the real sze n Data Web Ponts (DWP*) of a fnshed project, we only requre the total man-hours used n t, and the experence of the estmator who can obtan the value of the UC and the cost drvers. For ths task not much hstorcal nformaton s needed. Once we have the value of DWP* for a hstorc project, we should compare t wth the DWPs that can be obtaned from the data model of such project. Thus, t s possble to obtan the representatveness coeffcent of the DWPs for the same specfc project usng the followng equaton: X = DWP * DWP Where: X corresponds to the percentage of the system sze whch cannot be nferred from the data model usng the DWPs. Ths process could be repeated n several projects wthn the company or development team n order to get a more accurate coeffcent. The partcpaton of the expert n the estmaton s crucal for ths reengneerng process, because only hs/her experence can determne the user cost and the value of the cost drvers that exsted n those projects. Thus, we can calculate the weghted mean of the representatveness of the DWPs n the company s hstory cd X * s the weghted mean of the fracton of the real sze of the project that s calculated from the DWPs of the data models that the company generates. 4.4 User Cost The User Cost (UC) s a functon of the user types to be supported by the system. CWADEE consders three user types defned as: manager, updaters and consultants. The manager user s n charge of supervsng the avalable applcatons n the system, actvatng and deactvatng functonal areas of the system, and mantanng the set of applcatons that keep the project n constant executon. The updater user uses the avalable functonalty n the system to modfy and consult to the stored nformaton. Fnally, the consultant user has access to part of the nformaton avalable n the system, but only for readng. On the other hand, CWADEE also consders the possblty that varable users, whch are a mx of the before mentoned user types, are present n the system. These users are a mx of the aforementoned. The user types a system supports are mportant to calculate the development effort because each type requres nterfaces developed and adjusted for t, but generally part of the functonalty s shared. For ths reason, CWADEE consders the scope of each user type n the system, and the functonalty reuse among user types n order to calculate the user cost. The User Cost s a varable dependent on each project, and so t can be determned n partcular for each one, and t s subject to the estmator subjectvty. For calculatng the user cost we should buld a table smlar to the one shown n Table 2. Ths table ndcates that the three predefned user types are present n the system, where the admnstrator has access to 30% of the applcatons functonalty; the updater uses 60% and the consultant 80%. There are also n the example two varable user types that correspond to the secretary that uses 30% of the applcatons functonalty and the area manager that uses 20%. On the other hand, each user type has a degree of reuse of system functonalty that s determned by the expert estmator. Ths reuse can come from the mplementaton of functonalty for other user types, or other systems. Once ths user types table s generated, t s possble to calculate the UC value usng the followng equaton: wth the followng equaton: UC = ( 1 ) X * = E E X Where: E s the effort n man-hours used n each project. X s the fracton of the sze represented by the DWPs. I Where: UC represents the User Cost used n the effort equaton. I s the fracton of the scope of each user type. R s the reuse degree of each user type. R

7 Ths equaton yelds values between 0 and 5. A value of UC of 0 means the system reuses all the functonalty assocated wth each user type; so, the development effort wll also be zero. On the other hand, f the user cost s fve, ths means that there s no reuse of any knd to mplement the system functonalty for each user type. 4.5 Cost Drvers Fnally, the CWADEE method has a seres of Cost Drvers taken from the WebMo model proposed by Refer [16] These Cost Drvers represent the avalable development scenaros for a partcular project. Such scenaros have postve and negatve nfluences over the development process that need to be taken nto account durng the estmaton process. Cost Drvers are subjectve factors n CWADEE, and all of them, but CLIEN, were defned for the WebMo model. COPLX: Product relablty and complexty. (Product attrbutes). DIFPLA: Platform dffculty. (Platform and net servers volatlty). PERS: Personnel capabltes. (Sklls, knowledge and abltes of the work force). EXPER: Experence of the personnel. (Depth and wdth of the work force experence). INFRA: Infrastructure. (Tools, equpment and geographcal dstrbuton). SCHED: Schedulng. (Rsk degree assumed f the delvery tme s shortened.) CLIEN: Clent type. (Technology knowledge the clent has; requrements stablty.) WTEAM: Work team. (Ablty to work synergstcally as a team). EFPRO: Process effcency. (Development process effcency). Table 2. Example of a scope table for dfferent user types n a Web-based project Fxed Users Varable Users User types Fracton of the Scope (I) Reuse Degree (R) Manager Updater Consultant Secretary Area Manager Table 3. Reference values for cost drver Cost drvers for CWADEE Drver VL L N H VH COPLX DIFPLA PERS EXPER INFRA SCHED CLIEN WTEAM EFPRO Each of these cost drvers s classfed n a fve level scale: very low, low, normal, hgh and very hgh (VL, L, N, H, VH). In order to determne whch level corresponds to each cost drver, the estmator uses a seres of predefned tables that were bult usng hstorcal nformaton (gross gran) of other Chlean projects. Each cost drver has an assgned value n each category, and the product of each value s part of the equaton for calculatng the effort n the CWADEE method. The assgned values n each category are replaced n the CWADEE effort estmaton equaton n order to obtan the result n man-hours. Usng the nformaton of 22 Webbased projects developed by undergraduate and graduate students at the Computer Scence Department at the

8 Unversty of Chle, we have obtaned approxmate values for each cost drver n each category. Table 3 shows these values. 5. Obtaned Results Computer scence graduate and undergraduate students n the regular Software Engneerng course have used the current verson of the CWADEE model for the last 3 years. They used t for calculatng the effort requred for each project, and to help them establsh the lmts of the work to be done. At the begnnng of the course they are nstructed about ths effort estmaton technque, and the hstorcal cost drvers are presented and dscussed. Then, the students are grouped n teams of 5 to 7 people, and roles are assgned to the members. The roles are project manager, analyst, desgner, developer and tester. The last one s a dstrbuted role. Also, real projects and clents are assgned to each group. They have only 16 weeks to develop the project and the frst week s used to establsh the lmts of each development. Ths actvty s a negotaton between the project manager and the clent based on development effort estmaton (E), whch can be calculated takng nto account the tme avalable, the features of work group members, the group sze and the problem to be solved. Also, raw nformaton about these ssues collected n past projects can be used. The course nstructor and two ndustry experts, who know very well the course s development scenaro, evaluate the establshed lmts. These lmts are n drect relatonshp wth the effort they estmate needed to develop the project. A summary of the obtaned results about the estmatons carred out by the students, s shown n Table 4. By takng nto account the DWP dentfed by each group and the project lmt defnton, the sze estmaton (or establshed lmts) s evaluated as good f t s smlar to expert opnon. It s evaluated as medum f the estmaton and the expert's opnon are close, or poor f they are not smlar. Projects Amount Table 4. Obtaned results usng CWADEE Sze Estmaton Qualty Fnal Product Qualty 15 Good In producton 9 Projects Need mnor adjust 6 Projects 5 Medum Need mnor adjust 2 Projects Incomplete 3 Projects 2 Poor Incomplete 2 Projects Table 4 shows that students wth lttle experence can get good estmatons usng CWADEE takng nto account the expert's opnons. Also, the projects well szed were put n producton or needed mnor adjustments, and the project poor szed were ncomplete. Independently of the expert's opnon, these results show that applcablty level of CWADEE to support the effort estmaton of small and medum szed Web-based development n Chlean scenaros. On the other hand, at least a couple of students who were taught CWADEE, have created ther Web software development companes, and they are currently usng ths technque to estmate the development effort. The results they report are better than the above presented, but they were obtaned n a no-controlled scenaro. The characterstcs of the mentoned course development scenaro are smlar to most of Chlean development companes. Currently, several students whose partcpate n the course are workng n the ndustry as developers of such projects and they corroborate ths fact. Therefore, the obtaned prelmnary values may be showng the proposed method s approprated to support the expert to mprove the qualty of effort estmaton n such scenaros. 6. Conclusons and Future Work The market evoluton forces clents to get nvolved n very short-term Web projects. Too often the results are ncomplete, unrelable and dffcult to mantan applcatons that fal to meet the busness needs, and cost more and take longer to develop than expected. Generally, the effort estmaton s not able to foresee and help avod these problems. Although developers spend tme tryng to estmate the software development effort realstcally and relably, they usually have very lttle tme for ths task and very lttle hstorcal nformaton s avalable. These characterstcs tend to make estmatons less relable regardng both tme and cost. An expert knows the development scenaro and the development capabltes of hs/her organzaton, but he/she generally does not have good tools to support an accurate, relable and fast estmaton (24-72 hours). In ths scenaro, t s really dffcult to fx a compettve budget wth lttle rsk of loosng money. In order to get fast and relable effort estmatons of Web-based nformaton systems development projects, ths paper presented the CWADEE method. It does not replace the expert estmator, but t provdes hm/her wth a tool for achevng a more accurate estmaton, based on real data n a shorter tme. CWADEE has been appled n a development scenaro smlar to most of the Chlean software development companes and t has shown be useful to support the effort estmaton process. Ths method needs to be appled n real software development companes n Chle, and ts performance needs to be formally measured. For the moment, t represents a general gude for the expert estmator for lowerng rsks and support estmatons. CWADEE s a repeatable and adjustable software engneerng method,

9 so t has the potental of beng more and more accurate as we apply t once and agan. Currently, we are applyng CWADEE n small sze Web-based nformaton systems n Chle; however, we could extend t to dfferent scenaros n the future. Acknowledgements Ths work has been partally funded by Fondo Naconal de Cenca y Tecnología de Chle (FONDECYT) grant No We acknowledge J.C.Trujllo of Motorola Chlean Center for Software Technology for hs contrbutons. References [1] B. Boehm, Software Engneerng Economcs, Prentce- Hall, January [2] B. Boehm, Anchorng the Software Process, IEEE Software, Vol. 13, No. 4, pages 73-82, July [3] B. Boehm, E. Horowtz, R. Madachy, D. Refer, B. K. Clark, B. Steece, A. Wnsor Brown, S. Chulan and C. Abts, Software Cost Estmaton n COCOMO II, Prentce-Hall, 1 st edton, January [4] B. Boehm and R. Farly, Software Estmaton Perspectves. IEEE Software, Vol. 17, No. 6, pages , Nov./Dec [5] F. Buschmann, R. Meuner, H. Rohnert, P. Sommerlad and M. Stal, "Pattern-Orented Software Archtecture, Volume 1: A System of Patterns", John Wley & Son Ltd; 1 st edton, August [6] A. J. C. Cowderoy, Sze and Qualty Measures for Multmeda and Web-Ste Producton. Proc. of the 14th Internatonal Forum on COCOMO and Software Cost Modelng, Los Angeles, CA, October [11] G. Jménez, G. Goa, J. Pérez Herrera, R. Bertone, H. Ramón. Estmatng the Effort based on the Data Model (n Spansh). IX Chlean Computer Scence Workshop, Punta Arenas, Chle, November [12] D. Lowe, Web Engneerng or Web Gardenng? WebNet Journal. Vol. 1, N 1 January - March [13] J. Matson, B. Barret and J. Mellchamp Software Development Cost Estmaton Usng Functon Ponts. IEEE Transactons on Software Engneerng, Vol. 20, No. 4, pages , Aprl [14] G. Parra. Estmatng the Development Effort and Duraton of Web Projects. Computng Engneerng Thess (n Spansh). CS Department, Unversty of Chle, January [15] D. Phllps, The Software Project Manager s Handbook. IEEE Computer Socety Press, [16] D.J. Refer, Web Development: Estmatng Quck to- Market Software. IEEE Software, Vol. 17, No. 6, pages 57-64, November. December [17] G. Rule, Bees and the Art of Estmatng. IEEE Software, Vol. 17, No. 6, page 2, November [18] E. Sacre, A Methodology To Develop Web Applcatons n Small and Medum Sze Enterprses (n Spansh). Master Thess. CS Department, Unversty of Chle, July [19] W. Sten, A Web Software Process for Small or Medum- Szed Projects, Focused on the Chlean Scenaro. Engneerng Thess (In Spansh). CS Department, Unversty of Chle, Aprl [20] J.C. Trujllo, Generatng Informaton Systems User Interfaces Usng Desgn Patterns. Master Thess (n Spansh). Computer Scence Department. Pontfca Unversdad Católca de Chle, [7] M. Fowler, Patterns of Analyss: Reusable Object Models. Object-Orented Software Engneerng Seres. Addson-Wesley [8] M. H. Halstead, Elements of Software Scence, Elsever North Holland, Dordrecht, The Netherlands, [9] R. Hay and R. Barker, Data Model Patterns: Conventons of Thought. Dorset House Publshng [10] Internatonal Functon Pont Users Group, Functon Pont Countng Practces Manual. Release 4.0, URL:

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