Grey System Theory Approach to Quality of Intranet

Transcription

1 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) Grey System Theory Approach to Qualty of Intranet N. Slavek 1, D. Krmpotć 2, D. Blaževć 3 Assocate Professor, Dept. of Software Engneerng, Faculty of Electrcal Engneerng Osjek, Osjek, Croata 1 PG Student, Dept. of Computer Engneerng and Automaton, Faculty of Electrcal Engneerng Osjek, Osjek, Croata 2 Assstant professor, Dept. of Computer Engneerng and Automaton, Faculty of Electrcal Engneerng Osjek, Osjek, Croata 3 ABSTRACT: Ths work dscussed the effectveness of he Gray system theory to determne the Intranet qualty. ISO 9126 [1] s used to defne the Intranet qualty factors and characterstcs. The rankng of these factors and characterstcs were used to determne the mportance of ths qualty attrbutes. The Grey system provdes multdscplnary approaches for analyss and abstract modellng of systems for whch the nformaton s lmted, ncomplete and characterzed by random uncertanty. The Grey relatonal analyss uses nformaton from the Grey system to dynamcally compare each characterstc quanttatvely. KEYWORDS: Grey system theory, Grey relatonal analyss, Intranet, Intranet qualty. I.INTRODUCTION In ths paper we nvestgate the effectveness of the Grey system theory to determne the rankng of relatve mportance of Intranet qualty factors and characterstcs. The ntranet emerged n the nnetes as the applcaton of nternet technologes to nternal busness applcatons [2]. The number of ntranet applcatons ncreases and software developers face a new set of qualty requrements. More and more organzatons from all sectors are attracted by the tremendous benefts of ntranet applcatons: cost and tme savngs, ncrease of productvty, flexblty, open archtecture, consstency, and removal of departmental boundares [13]. The ntranet technology s dentcal to that of the nternet. Only n one way are the two dfferent: prvate versus publc usage [16]. Whle there s lot of smlartes between them, they are really two dfferent thngs. The nternet s the global World Wde Web, whle an ntranet s a prvate nternet operatng wthn a company. One man dfference s that users of an ntranet can get on the nternet, but due to protecton measures lke computer frewalls, global nternet users cannot get onto the ntranet unless they have access to t. An ntranet can be run wthout an nternet connecton. From a management perspectve, successful ntranet applcaton mples hgher return on nvestment, low rsk, shorter payback tme, more busness opportuntes, and technology - enabled employees, and shorter development lfe - cycle [3]. In the wave of use of the ntranet, there s an ncreasng need for qualty management. The questons are [2]: can an ntranet applcaton be measured quanttatvely? Can well-known software qualty factors and characterstcs be appled? The defnton of qualty s 'the totalty of features and characterstcs of a product or servce that bear on ts ablty to satsfy stated or mpled needs' [10]. The qualty s customer focused, the mportance of software qualty factors and characterstcs of ntranet applcaton should be determned for dfferent types of users: software system professonals, end-users, software developers, and end user management. II. RELATED WORK There has been lttle study of qualty measurement for ntranet applcatons. Welch, D. E [16] analyzed the ntranets as the New Engne of Corporate Productvty. Hareton K. N. Leung [2] attempts to dentfy practcal software metrcs for ntranet applcatons. From the results of a survey, the three key qualty characterstcs are dentfed; the relablty, functonalty and effcency. Schnader, C. [13] analyzed the ntranet archtecture ntegratng nformaton desgn wth busness plannng.mccall, Rchards, and Walters [12] propose factors and crtera for software qualty. Lu, S., Ln, Y. [11] ntroduced the grey systems; ts foundatons, methodology and applcatons. Sfen, L., Forrest, J. [15] analyzed the current developng status on grey system theory. Chh-Hung T.,Chng-Lang C., Leh C. [7] applayed grey relatonal Copyrght to IJAREEIE DOI: /IJAREEIE

2 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) analyss to the Vendor Evaluatonal Model. Xong, Y. [17] used the grey relatonal evaluaton of fnancal stuaton of lsted company. III. ISO QUALITY FACTORS McCall, Rchards, and Walters [12] propose a useful categorzaton of factors and crtera that effect software qualty. It s dffcult and n some case mpossble to develop measures of these qualty factors. Many of the metrcs can be measured only subjectvely. ISO 9126 mplfed the model n an attempt to dentfy qualty attrbutes for software. It s general and commonly accepted, beng state-of-the art n product qualty specfcaton. The standard dentfes sx key qualty attrbutes called qualty factors lsted n Table 1. The ISO qualty factors do not necessarly lend themselves to drect measurement. However, they do provde a worthwhle bass for ndrect measures and a checklst for assessng the qualty of the system. Functonalty Relablty Usablty Effcency Table 1 Qualty attrbutes accordng to ISO 9126 The degree to whch the software satsfes stated or mpled needs. The capablty of software to mantan ts level of performance under gven condtons for a gven perod of tme. Attrbutes that determne the effort needed for use and the assessment of such use by a set of users. The relatonshp between the level of performance of the software and the amount of resources used under stated condtons. Mantanablty The effort needed to make specfed modfcatons. Portablty The ablty of the software to be transformed from one envronment to another. These qualty factors can be further broken down nto lower level qualty characterstcs, as shown n Fg. 1. The ISO 9126 model conssts of 32 software qualty characterstcs. Fg.1. Herarchcal vew of ISO 9126 model In ths paper the ISO 9126 standard was chosen, and ts software qualty factors were used to dentfy key qualty attrbutes of ntranet applcaton. The rankng of these factors and characterstcs were used to determne the mportance of ths qualty attrbutes. Ther relevance was studed to dentfy the key qualty characterstcs. Ths study wll utlze the Grey relatonal analyss n the Grey theory to establsh a complete and accurate evaluaton model for selectng the Intranet wth the best qualty factors and characterstcs. Copyrght to IJAREEIE DOI: /IJAREEIE

3 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) IV. GREY SYSTEM THEORY The Grey system Theory was frst proposed by Deng (1982) [2]. Grey system provdes multdscplnary approaches for analyss and abstract modellng of systems for whch the nformaton s lmted, ncomplete and charactersed by random uncertanty. The term grey stands for abstractng poor, ncomplete and uncertan, and s especally used n relaton to the concept of nformaton. Grey system theory typcally deals wth systems, objects or concepts havng well defned external boundares but nternal uncertanty or vagueness, whle conversely fuzzy mathematcs deals wth systems, objects or concepts havng a well defned nternal characterstcs but not well defned boundares [11]. The nformaton that s ether ncomplete or undetermned s called Grey. The Grey system provdes multdscplnary approaches for analyss and abstract modelng of systems for whch the nformaton s lmted, ncomplete and characterzed by random uncertanty [15]. The ams of Grey system theory are to provde theory, technques, notons and deas for resolvng (analyzng) latent and ntrcate systems, for example [8]: 1. To defne and consttute Grey process replacng the stochastc process and to fnd the real tme technques nstead of statstcal model to deal wth the grey process, n order to obtan an approach to modelng wth few data, avodng searchng for data n quanttes; 2. To develop a novel famly of Grey forecastng methods nstead of tme seres and regressve methods all of these may be referred to as an approach to deal wth grey processes; 3. To buld a dfferental model Grey model (GM) by usng the least 4 data to replace dfference modelng n vast quanttes of data; 4. To study the mechansm theory, ncludng Grey sequence theory and Grey structure theory; 5. To establsh a non functon model nstead of regressve analyzng; 6. To study feelng and emoton functons and felds wth whtenng functons; 7. Based on Grey relatons, Grey elements and Grey numbers to be used to study Grey mathematcs nstead of classcal mathematcs study; 8. To turn the dsorderly raw data nto a more regular seres by Grey generatng technques for the beneft of modelng nstead of modelng wth orgnal data; 9. To develop novel control technques e. g. the Grey forecastng control replacng classcal control whch s referred to as afterward control, also relatonal control, generatng control and programmng control Man contrbutons to Grey System Theory came from: Grey Systems and control, Grey Relatonal Analyss (GRA), and Gray Modelng (GM). The GRA uses nformaton from the Grey System to dynamcally compare each factor quanttatvely. Approach s based on the level of smlarty and varablty among all factors to establsh ther relaton. GM s developed based on requrements for system modelng wth lmted data. The 1 st order one varable Grey Model denoted as GM (1, 1) s especally applcable for forecastng. GM (1, 1) model uses the varaton wthn the system to fnd out the relatons between sequental data and then establsh the predcton model [15]. The three terms that are typcal symbols and features for Grey System are [7]: a) The Grey number n Grey system s a number wth less complete nformaton b) The Grey element represents an element wth ncomplete nformaton. c) The Grey relaton s the relaton wth ncomplete nformaton. There are several aspects for the theory of Grey system [14]: 1. Grey generaton: Ths s data processng to supplement nformaton. It s amed to process those complcate and tedous data to gan a clear rule, whch s the whtenng of a sequence of numbers. The expected goal for each factor s determned based on the prncple of data processng, descrbed as follows: I) If the expectancy s the bgger- the-better, than t can be expressed by formula (2). II) If the expectancy s smaller- the- better, than t can be expressed by formula (3) Copyrght to IJAREEIE DOI: /IJAREEIE

4 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) III) If the expectancy s nomnal-the best, and when the targeted value s: X 0 : (Xj ) max, X 0 : (Xj ) mn, than t can be expressed by formula (4). 2. Grey modelng: The modelng s performed n order to establsh a set of Grey varaton equatons and Grey dfferental equatons, whch s called the whtenng of the model. Ths s done by step 1 to establsh a set of Grey varaton equatons and Grey dfferental equatons. The Grey model s denoted as GM (n, h), whch s a n-th order dfferental equaton of h varables. Ths Grey dfferental equtaton s used for nfnte nformaton. Most of the prevous researchers have focused on GM (1, 1) models because of ts computatonal effcency. GM (1, 1) model have tme varyng coeffcents. It means that the model s renewed as the new data become avalable to the predcton model. A Grey dfferental equaton havng N varables s called GM (1, N). 3. Grey predcton: By usng the Grey model to conduct a qualtatve predcton, ths s called the whtenng of development. Grey models predct the future values of a tme seres based on a set of the most recent data. 4. Grey decson: A decson s made under mperfect countermeasure and unclear stuaton, whch s called the whtenng of status. It s prmarly concerned wth the Grey strategy of stuaton, Grey group decson makng and Grey programmng [5 Deng ]. Grey strategy of stuaton deals wth the strategy makng based on mult objects whch are contradctory n the ordnary way. Important s to make a satsfactory strategy by means of effect measure maps, whch transfer the dsconformtes samples resultng from dfferent objects nto dentcal scales. 5. Grey relatonal analyss (GRA): Quantfy all nfluences of varous factors and ther relaton, whch s called the whtenng of factor relaton. It uses nformaton from the Grey system to dynamcally compare each factor quanttatvely, based on the level of smlarty and varablty among factors to establsh ther relaton. GRA analyze the relatonal grade for dscrete sequences. 6. Grey control: Work on the data of system behavor and look for any rules of behavor development to predct future behavor. The predcted value can be fed back nto the system n order to enable system control. Ths study wll adopt above mentoned research steps to develop an nfluence factors evaluaton model based on GRA, and apply to nfluence factors evaluaton and selecton. The Grey relatonal analyss uses nformaton from the Grey system to dynamcally compare each factor quanttatvely. V. GRAY RELATIONAL ANALYSIS Let the number of the lsted software project s m, and the number of the nfluence factors s n. Then an m x n value matrx (callng engenvalue matrx) s set up [17]. X = x1 (1), x1 (2),... x1 ( n) x2(1), x2(2),... x2( n) x (1), xm(2),... xm( n) m (1) In formula (1), x (k ) means the value of the number lsted project and the number k nfluence factors. Copyrght to IJAREEIE DOI: /IJAREEIE

5 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) x 0 (k ) denotes the element of the referental seres for attrbute k, x (k ) denotes the element of the compared seres for attrbute k. Usually, three knds of nfluence factors are ncluded, they are: 1. Beneft type factor (the bgger the better), 2. Defect type (the smaller the better) 3. Medum type, or nomnal-the-best (the nearer to a certan standard value the better). It s dffcult to compare between dfferent knds of factors because they exert a dfferent nfluence. Therefore, the standardzed transformaton to these factors must be done. Three formulas can be used [7]: x (k)= x (k) mn x (k) / max x (k) mn x (k) (2) The frst standardzed formula s sutable for the beneft type factor. x (k)= max x (k) x (k) / max x (k) mn x (k) (3) The second standardzed formula s sutable for defect type factor. x (k)= 1 x (k) u / max x (k) u (4) The thrd standardzed formula s sutable for the medum type factor. The Calculaton of the Grey Relatonal Grade The grey relaton grade represents the correlaton between two seres. It can be calculated by steps as follows: a) The absolute dfference of the compared seres and the referental seres should be obtaned by usng the followng formula [17]: Δx(k)= x0 (k) x(k), (5) and the maxmum and the mnmum dfference should be fnd out. b) The dstngushng coeffcent p s between 0 and 1. Generally, the dstngushng coeffcent p s set to 0.5. c) Calculaton of the relatonal coeffcent and relatonal degree by (6) as follows: In Grey relatonal analyss, Grey relatonal coeffcent ξ can be expressed as follows: ξ (k )=( Δ mn+pδ max)/( Δx (k )+pδmax ) (6) and then the relaton grade follows as: =w(k)ξ(k) r (7) In equaton (7), ξ s the Grey relatonal coeffcent, w (k) s the proporton of the number k nfluence factor to the total nfluence ndcators. The sum of w (k) s 100%. The result obtaned when usng equaton (6) can be used to measure the qualty of the lsted software projects. The generaton of Grey relaton s shown n Fgure 2. Copyrght to IJAREEIE DOI: /IJAREEIE

6 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) Pogreška! Knjžna oznaka nje defnrana. Fg. 2. The generaton of Grey relaton dagram V. RELEVANCE OF ISO QUALITY CHARACTERISTICS IN INTRANET APPLICATIONS The study nvolves a survey of qualty characterstcs n ntranet applcatons. For that purpose we used publshed results from H.N.K. Leung [8]. A survey was performed to determne the user vew of the relatve mportance of qualty characterstcs n an ntranet applcaton. Respondents conssted of end-user, end-user management, developers and IT professonals. Responders were asked to rank the relatve mportance of the sx qualty characterstcs by assgnng a number from one (most mportant) to sx (least mportant) for each characterstc. Table 2 summarzes the responses, t shows the dstrbuton of scores for sx ntranet software. Copyrght to IJAREEIE DOI: /IJAREEIE

7 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) Table 2. Dstrbuton of scores for sx Intranet software Factors Score 1 S2 S3 S4 S5 S6 Functonalty F Relablty F Usablty F Effcency F Mantanablty F Portablty F For all scores, the expectancy s shorter-the-beter, or defect type factor, whch can be determned by the equaton (3). The reference seres s therefore Xo = (0, 0, 2, 1, 1, 0). The above equaton (3) can be used to do the standardzed transformaton n ths sample. The scorng ponts for each attrbute are normalzed n order to obtan matrx table of comparatve seres based on the expectancy of each ndvdual event, as shown n Table 3. An deal standard seres s establshed ( X 0 =1) n the last lne n Table 3. Table 3. The compared seres and the referal seres Factors Score 1 S2 S3 S4 S5 S6 F F F F F F VI. THE GREY RELATIONAL COEFFICIENT DETERMINATION Next step s to calculate the absolute dfference of the compared seres and the referental seres usng equaton (5), and fnd out the maxmum and mnmum. Table 4 shows the grey relaton coeffcent. Table 4. The Grey relatonal coeffcent Factors Score 1 S2 S3 S4 S5 S6 F F F F F F The Grey relatonal coeffcent for each contestant can be obtaned by equaton (6) shown n Table 4. The Grey relatonal grade s then obtaned usng equaton (6). The Grey relaton grade for each contestant are: Γ 01 = 0.674; Γ 02 = 0.715; Γ 03 = 0.672; Γ 04 = 0,604; Γ 05 = Γ 06 = The fnal results wth rankng are gven n Table 5. Copyrght to IJAREEIE DOI: /IJAREEIE

8 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) Table 5. The rankng Factor Result Rankng F F F F F F The rankng order for these sx qualty factors are F6, F2, F1, F5, and F4. VII. CONCLUSION Ths paper dscusses the scorng method that s beng used for ntranet qualty factors. Accordng to the analyss n ths paper t s shown that the Grey relatonal grade deduced by Grey theory can be used to establsh an accurate and precse evaluatng model for detectng the rankng of qualty factors. Grey relatonal analyss possesses an overwhelmng advantage to solve the problems that tradtonal method could not overcome when there s a te score dspute. REFERENCES ISO 2006, ISO/IEC :2006- Informaton technology-software measurement-functonal sze measurement - Part 6, ISO/IEC, Hareton. K. N. Leung, Qualty metrcs for ntranet applcatons, Informaton & Management Vol 38, p.p , Campbell, P., The Intranet: Slashng the cost of busness, Internatonal Data Corporaton, Chen, S., L, Z. And Xu, Q. Grey Target Theory Based Equpment Condton Montorng and Wear Moode Recognton Wear, Vol. 260, Chen, L. R., Ln, C. H., Hsu, R. C., Ku, B.G. & Lu C.S., A Study of L-on Battery Charge Forecastng Usng Grey Theory, IEICE/IEEE MTELEC'03, pp , Chang, Y. M., Hseh, H.H., The use of the Tagouch method wth grey relatonal analyss to optmze the thn-flm sputterng process wth multple qualty characterstcs n color flter manufacturng, Computers & Industral Engneerng, Vol.56, pp , Chh-Hung, T.,Chng-Lang, C., Leh, C., Applayng Grey Relatonal Analyss to the Vendor Evaluatonal Model, Internatonal Journal of the Computer, The Internet and Management, vol. 11, no. 3, pp , Deng, J., Introducton to Grey System Theory, The Journal of Grey System,pp. 1 24, Ebert, C., Dumke, R. Software Measurement, ISBN , Sprnger, Berln, NY, Kempter, H., Frank, H., Measurement based qualty control of real-tme system specfcaton Software Qualty Management III, Vol. 2, Lu, S., Ln, Y., An Introducton to Grey Systems: Foundatons, Methodology and Applcatons, IIGS Academc Publsher, Slppery Rock, McCall J., Rchards, P., Walters, G., Factors n Software Qualty, NTIS AD-A , 015, 055, Schnader, C., Intranet Archtecture Integratng Informaton Desgn wth Busness Plannng, ntrack.com/ntranet/arch.shtml, Suhk-Hook Suh., Fuzzy-Bayes Fault Isolator Desgn for BLDC Motor Fault Dagnoss, Internatonal Journal of Control, Automaton, and Systems, Vol.2, No. 3.,2004. Sfen, L., Forrest, J., The Current Developng Status on Grey System Theory, The Journal og Grey System Vol. 2, Welch, D. E., Intranets The New Engne of Corporate Productvty, welch.com/wpapers/ntranet.html, Xong, Y., Grey Relatonal Evaluaton of Fnancal Stuaton of Lsted Company, Journal of Modern Accountng and Audtng, Vol.3, No.2, ISSN , USA, Copyrght to IJAREEIE DOI: /IJAREEIE