A Method to Risk Analysis in Requirement Engineering Using Tropos Goal Model with Optimized Candidate Solutions K.Venkatesh Sharma 1, Dr P.V.

Transcription

1 250 A Method to Rik Analyi in Requirement Engineering Uing Tropo Goal Model with Optimized Candidate Solution K.Venkateh Sharma 1, Dr P.V.Kumar 2 1 Reearch Scholar in JNTUK Kakinada, Andhra Pradeh, India 2 Profeor in CSE Department, Omania Univerity, Hyderabad, Andhra Pradeh, India Abtract The requirement engineering i a field, in which oftware are modeled according to the requirement of the uer. The oftware developed under requirement engineering will atify the uer motly on their perpective. So, recent reearche are concentrating on the oftware development and analyi baed on requirement engineering. The requirement engineering procee are alo challenged by the rik in developing the oftware. So an efficient rik analyi ytem and rik management ytem i inevitable for the oftware development proce under requirement engineering. In the propoed approach, a modified Tropo goal model for tackling the rik aociated with the oftware development cycle i adopted. The Tropo goal model i three layer goal model, the top layer i the goal layer, the event layer in the middle and upport layer in the bottom. The propoed approach define a method to analyze the aociation between the node of each layer to evaluate their chance of raiing the rik. The evaluation are aeed baed on the node in the goal layer. On thorough analyi on the aociation and impact of event layer and upport layer node to the goal layer node, the chance of raiing rik can be calculated. The propoed approach explore the relative parameter like atifaction parameter and denial parameter to efficient analyi of the rik factor. The experiment are conducted on java programming under JDK platform and detailed analyi ection i provided to find the cot to rik meaure. Keyword: Requirement Engineering, Tropo Goal model, Candidate olution, Goal layer, Event layer. 1. Introduction Generally rik analyi i ued for tudying all the conideration, which lead to the frailer of the program. It i a method and technique for documenting the impact of extenuation trategie [1] and for judging ytem criticality [2]. Rik analyi i alo hown important in the oftware deign phae to ae criticality of the ytem [3] where rik are examined and neceary tep are introduced. Uually, countermeaure correpond to a deign, ytem fine tuning and then with a limited margin of change. However, it may happen that the rik reduction reult in the reviion of the entire deign and poibly of the initial requirement, introducing thu extra cot for the project [4]. Requirement engineering i a proce baed method for defining, realizing, modeling, relating, documenting and maintaining oftware requirement in oftware life cycle that help to undertand the problem better [5]. It ha been hown that a large proportion of the publication in oftware development can be related back to requirement engineering (RE) [6]. RE i the proce of dicovering the purpoe in the oftware development, by identifying takeholder and their need, and documenting thee in a form that i amenable to analyi, communication and ubequent implementation [7]. Failure during the RE procedure have a ignificant negative impact on the overall development proce [8]. Reworking requirement failure may take 40% of the total project cot. If the requirement error are found late in the development proce, e.g. during maintenance, their correction can cot up to 200 time a much a correcting them during the early tage of the development proce [9]. Adequate neceitie are therefore eential to enure that the ytem the cutomer expect i produced and that unneceary exertion are avoided. According to Goal-Oriented Requirement Engineering, analyi of takeholder goal lead to ubtitute et of functional requirement that can each accomplih thee goal. Thee alternative can be evaluated with repect to non functional neceitie poed by takeholder. In the previou paper, they propoe a goal-oriented approach for analyzing rik during the requirement analyi phae. Rik are analyzed along with takeholder interet, and then countermeaure are identified and introduced a part of the ytem requirement. Thi work extend the Tropo goal modeling formal framework uggeting new concept, qualitative reaoning technique, and methodological procedure. The approach i baed on a conceptual framework compoed of three primary layer:

2 251 aet, event, and treatment. In the field of oftware engineering, the requirement engineering i getting pecial attention a it i baed on the takeholder interet. The main factor that a requirement engineering proce conider are buine requirement and uer requirement. The requirement are ued to enhance the development of the oftware product with low cot and the time it hould atify all the requirement. One of the enitive area, which every oftware development proce concentrate i the rik involved with the proce. So, particular aement meaure have to be taken in order to minimize the rik in oftware development proce. YuditiraAnar and Paolo Giorgini [13] have propoed a method for rik analyi in requirement engineering. The method deal with a oftware development method called, Tropo Goal Model and with a Probabilitic Rik Analyi (PRA). Inpired from their work, we are planning to propoe an approach on extending the Tropo model with rik analyi feature. Tropo goal model conit of three layer, mainly Goal layer (GL), Event layer (EL) and Treatment layer (TL). The GL conit of et of goal that ha to fulfill by the proce and EL contain the contruct which help to achieve the goal. The TL i working a the input, which help in achieving the goal. The main contribution of the paper are, A goal oriented approach i furnihed to analyze the cot and rik aociated with requirement engineering A genetic algorithm i utilized to optimize the candidate olution The ret of the paper i organized a; ection 2 decribe the literature urvey regarding the requirement engineering and rik analyi. The 3 rd ection contain the problem decription behind in propoing the approach. The 4 th ection include the propoed goal model and cae tudy ued for it to analyze the rik and cot in requirement engineering. The 5 th ection conit of the experimental analyi of the propoed goal model. Finally, the 6 th ection include the concluion of the work.. 2. Literature Review The following ection decribe review about ome recent work regarding the requirement engineering and rik analyi related to it. Security rik aement in the requirement phae i challenging becaue probability and damage of attack are not alway numerically meaurable or available in the early phae of development. Selecting proper ecurity olution i alo problematic becaue mitigating impact and ide-effect of olution are not often quantifiable either. In the early development phae, analyt need to ae rik in the abence of numerical meaure or deal with a mixture of quantitative and qualitative data. GolnazElahietal[14] propoe a rik analyi proce which intertwine ecurity requirement engineering with a vulnerability-centric and qualitative rik analyi method. The method i qualitative and vulnerability-centric, in the ene that by identifying and analyzing common vulnerabilitie the probability and damage of rik are evaluated qualitatively. They alo provided an algorithmic deciion analyi method that conider rik factor and alternative ecurity olution, and help analyt elect the mot cot-effective olution. The deciion analyi method enable making a deciion when ome of the available data i qualitative. JacKyAnget al [10] ha developed an expert ytem that ha leat focu on requirement engineering. In fact, requirement engineering i important to get all the requirement needed for an expert ytem. If the requirement do not meet the client need, the expert ytem i conidered fail although it work perfectly. Currently, there are a lot of tudie propoing and decribing the development of expert ytem. However, they are focuing in a pecific and narrow domain of problem. Alo, the major concern of mot reearcher i the deign iue of the expert ytem. Therefore, we emphaize on the very firt tep of ucce expert ytem development requirement engineering. Hence, we are focuing in the requirement engineering technique in order to preent the mot practical way to facilitate requirement engineering procee. They have analyzed expert ytem attribute, requirement engineering procee in expert ytem development and the poible technique that can be applied to expert ytem development. Luka Pilatet al [11] have propoed an approach for problem in requirement engineering i the communication between takeholder with different background. Thi communication problem i motly attributed to the different language poken by thee takeholder baed on their different background and domain knowledge. We experienced a related problem involved with tranferring and haring uch knowledge, when takeholder are reluctant to do thi. So, they take a knowledge management perpective of requirement engineering and carry over idea for the haring of knowledge about requirement and the domain. We cat requirement engineering a a knowledge management proce and adopt the concept of the piral of knowledge involving tranformation from tacit to explicit knowledge, and vice vera. In the context of a real world problem, we found the concept of knowledge holder and their relation to categorie of requirement and domain knowledge both ueful and important. Thi project wa cloe to become a failure until knowledge tranfer ha been intenified. The knowledge management perpective provided inight for explaining improved knowledge exchange.

3 252 Mina Attarha and Naer Modiri [12] have adopted a critical and pecific oftware ytem lat longer and they are ought to work for an organization for many year, maintenance and upporting cot of them will grow to high amount in the upcoming year. In order to develop and produce pecial aimed oftware, we hould piece, claify, combine, and prioritize different requirement, pre-requiite, co-requiite, functional and non functional requirement (by uing requirement engineering proce, they can claify the requirement). Development and production of pecial oftware require different requirement to be categorized (different requirement can be categorized uing oftware requirement engineering). In other word, we have to ee all requirement during the oftware' life cycle, whether they are important and neceary for our oftware at preent time or they are not important currently but will become important in future. Requirement engineering aim i to recognize the tockholder' requirement and their verification then gaining agreement on ytem requirement, i not jut a phae completed at the beginning of ytem development not required any more, but include part of next phae of oftware engineering a well. To achieve thi purpoe, we acquired a comprehenive knowledge about requirement engineering. Firt, they defined requirement engineering and explained it aim in the oftware production life cycle. The main activitie and purpoe of each requirement engineering activity i decribed. Moreover, the technique ued in each activity are decribed for a better comprehenion of the ubject. 3. Problem Decription The requirement engineering i a field, in which oftware are modeled according to the requirement of the uer. The oftware developed under requirement engineering will atify the uer motly on their perpective. So, recent reearche are concentrating on the oftware development and analyi baed on requirement engineering. The requirement engineering procee are alo challenged by the rik in developing the oftware. So an efficient rik analyi ytem and rik management ytem i inevitable for the oftware development proce under requirement engineering. In the approach, a modified Tropo goal model for tackling the rik aociated with the oftware development cycle i propoed. The Tropo goal model i three layer goal model, the top layer i the goal layer, the event layer in the middle and upport layer in the bottom. The propoed approach define a method to analyze the aociation between the node of each layer to evaluate their chance of raiing the rik. The evaluation are aeed baed on the node in the goal layer. On thorough analyi on the aociation and impact of event layer and upport layer node to the goal layer node, the chance of raiing rik can be calculated. The propoed approach explore the relative parameter like atifaction parameter and denial parameter to efficient analyi of the rik factor. The important factor about the propoed approach i that, it give preference to the relationhip between the different layer of the Tropo goal model 3.1 Tropo Goal model Tropo goal model i a oftware development model, which i characterized by concept of agent goal, tak, and reource and ue them throughout the development proce from early requirement analyi to implementation. Early requirement analyi model provide the organizational etting, where the ytem-tobe will eventually operate. The Tropo model i extended by adding contraint and relation in order to ae the rik factor. The Tropo GR model mainly conit three tuple, i.e. the number of node (N), number of relation (R) and uncertain event (U). Conidering a Goal Rik (GR) model, the Tropo G-R model conit of mainly three layer, namely goal layer, event layer and upport layer. The goal layer conit of goal, which are the need that have to be achieved. The event layer conit of event node, which erve to achieve the goal and the bottom layer, the upport layer, which contain the node which upport either the event node or goal node. Each of the three contraint i characterized by everity value and the everity i marked with four meaure trong poitive (++), poitive (+), negative (-) and trong negative (--). The contruct poe two attribute, atifaction and denial, repreented by SAT (c) and DEN (c), where c i the contruct either goal, event and upport. The evidence of contruct c will be atified for SAT() and denied DEN(c).In probability theory, if Prob(A) = 0.1 then we can infer that probability of A i 0.9. Converely, baed on the idea of Dumpter-Shafer theory [1], the evidence of a goal being denied (DEN) cannot be inferred from evidence on the atifaction of the goal (SAT), and vice vera. For intance, the oftware development company ha the goal to develop called buine development oftware, which i effected by the event that need to be given importance called procurement_of_raw_material. The event may trigger the goal to either SAT() or to DEN() according to the upport value. If the upport uer_requirementha everity (--) then the goal reult in Den (). The attribute value are pecified more clearly by repreenting the value in different range like fully (f), partially (p) and none (n) and the priority of thoe value like f>p>n. The evidence for the atifaction of a goal mean that there i (at leat) ufficient ( ome, no ) evidence to upport the claim that the goal will be fulfilled. Analogouly, Full evidence for the denial of a goal mean that there i

4 253 ufficient evidence to upport the claim that the goal will be denied. According to the everity the event and goal are lited and the SAT value and DEN value are calculated. The other feature that i concentrated on the propoed approach i the relationhip between the goal, event and the upport. intance, the ++ contribution relation indicate that the relation propagate both SAT and DEN evidence, and the ++ contribution relation mean the relation only propagate SAT evidence toward target node. Alleviation relation are imilar to contribution relation but lightly Goal Layer Primary Goal G1 G2 G3 G4 G5 Event Layer E1 E2 E3 Support Layer S1 S2 S3 S4 Fig.1. Tropo Goal Model The relation R i the relation defined over different node in the defined goal rik model. The relation can be repreented a R [ N1,..., Nn N], where N i the target node and the N 1,,N n are the ource node. The relation are defined a three type, decompoition relation, contribution relation and alleviation relation. The decompoition relation, which ued are AND / OR, for refining the goal, event and upport. Contribution relation point the impact of one node to another. Our framework ditinguihe four level of contribution relation, ++, +, - and --. Each one of thee type can propagate either evidence for SAT or DEN or both. For differ in the emantic. The goal model depicted in the figure 1 project a main goal, which i aociated to a number of aociate goal. The affinitie of thee aociate goal are the main criteria behind the ucce of the main goal. The ucce rate i projected baed on the cot to which the main goal i achieved with an acceptable rik. The uual cot to rik analyi are baed on the SAT value and DEN value of the aociated goal. In the propoed approach, we define a methodology, which give priority to the aociate goal to minimize the cot and tolerate the error to a certain limit. The propoed approach decribe the cot to rik analyi through a cae tudy baed on the oftware development company. The following figure

5 254 depict the illutration of the SDC over the Tropo goal model. The figure 2 how the illutration of the propoed oftware development company in the model of Tropo goal model. In the figure, it i hown that the top layer of model contain the target goal and aociate goal. The event layer contain the aociated event, which are needed for the projection of the goal. The upporting event are lited in the upport layer a upport value. Now a cot to rik etimation i conducted to find the bet cot efficient way to calculate the target goal or achieve the goal. The propoed approach decribe the methodological proce and qualitative rik reaoning technique ued to analyze and evaluate alternative in a GR model. Particularly, we focu on finding and evaluating all poible way (called trategie) for atifying top goal with an acceptable level of rik. In other word, given a GR model, each OR-decompoition introduce alternative modalitie for top goal atifaction, namely different et of leaf goal that can atify top goal. Each of thee alternative olution may have a different cot and may introduce a different level of rik. Rik can be mitigated with appropriate countermeaure, which, however, may introduce additional cot and further complication. In the propoed rik analyi approach, two trategie are ued for evaluating the different rik caued by different event. Baed on the repone from the above lited analye, the rik are mitigated to an accepting level with feaible cot for development. The analye are detailed in the following ection. The analye are baed on the candidate olution extracted from the different goal achievement. In achieving the goal G1, we can ue anyone of the ource node G2, G3 and G4. The cae tudy dicued here ha 4 target node; they are G5, G1, G6 and G9. The different candidate olution for achieving the target are repreented with S. Conider following candidate olution which are ued for evaluating the target node. S1- G2 G4 G7 G10 G11 G12 S2- G2 G3 G8 G10 G11 G12 S3 - G2 G4 G7 G8 G10 G11 S4- G2 G4 G7 G8 G11 G12 S5 G3 G4 G7 G10 G11 G12 S6- G3 G4 G8 G10 G11 G12 S7 G2 G3 G4 G7 G10 G12 S8- G2 G3 G4 G8 G11 G12 The above lited are the candidate olution generated for the propoed oftware development model. Thee olution are generated according to the SAT value and DEN value defined by the Tropo goal model. The olution generated are only conidered the parameter likelihood and accepting factor like SAT and DEN, now the propoed approach initiate an optimization phae to extract the mot optimized olution among the generated candidate olution. 3.2 Genetic Algorithm for optimizing the candidate olution The genetic algorithm i one of the commonly ued optimization algorithm i the data management domain. We make ue of the optimization method of GA in the propoed approach to generate the optimized candidate olution. The candidate olution obtained from the Tropo goal model are conidered a the initial population, which i ubjected for the optimization. 3.3 Fitne evaluation S1- G2 G4 G7 G10 G11 G12 S2- G2 G3 G8 G10 G11 G12 S3 - G2 G4 G7 G8 G10 G11 S4- G2 G4 G7 G8 G11 G12 S5 G3 G4 G7 G10 G11 G12 S6- G3 G4 G8 G10 G11 G12 S7 G2 G3 G4 G7 G10 G12 S8- G2 G3 G4 G8 G11 G12 Initial population of GA The cot calculation function i conducted a the fitne function the propoed genetic algorithm for candidate olution. The candidate olution are formed of ix ource node value. The cot analyi i ubjected to extract the cot effective candidate olution among the extracted candidate olution. The cot i conidered the impact of each proce in the oftware development in the cae of the SDC. The cot of a deired target i obtained by calculating the aociation of that node to the ource node. Thu, the cot can be plotted a a et of three tuple, Cot ( Gn) [ Cot ( G ), Cot ( E ), Cot( S )] ource n n Where, Cot (Gn) mean the cot of the n the goal node, which i under conideration. Cot (G ource ) i the number of ource goal node which i upporting the target goal node. The value Cot (E n ) and Cot (S n ) are the cot for the event node and the upport node. The cot value i alo affected by the SAT value and DEN value of the node that are relevant for achieving the target goal. The other important factor are that affect the cot are the likelihood and everity value of the event node that give upport the goal. The cot evaluation of a candidate olution i decribed in the following ection. S6- G3 G4 G8 G10 G11 G12,

6 255 G5-Earn Money Goal layer G1-BDS G6-CRM G9-WDS G2-Supply chain management G3-ERP G7-Cutomer feedback Fwee G10-Maintenance G11-Domain G4-Prediction G8-Solution G12-Employee atifaction E1- Quality E2- Procurement of material E3-Proceing of good E4-Product cot Event layer E5-Product delivery E6- Delivery of good E7- Analyi E8-Uer requirement identification E9- Feaibility E10- Salary S1-Problem identification S2-Uer requirement S3- Reource availability Support layer S4-Job experience S5- Projection S6- Invetment on product Fig.2. Tropo Goal Model for SDC Where the aociation of different ource goal are lited below, G3 SAT (P) E1 (++) S1 (+) E6 (-) = = 10; G4 SAT (F) E1 (++) S1 (+) E6 (+)= =20; G8 SAT (F) E1 (++) S1 (+) E7 (+)= =20; G10 DEN (F) E10 (--) S4 (-) = 0 G11 SAT (P) = 5 G12 DEN (P) = 5 COST (S6) = 60 In the imilar way, all other candidate olution are et for obtaining their cot value in achieving the target goal. After calculating all the cot value, a cot graph i plotted and a threhold i fixed for the cot value. The threhold i et baed on maximized SAT value and minimized DEN value. The candidate olution are then elected for the further genetic algorithm procee. Once the fitne of each candidate olution are calculated the GA ubject the two other procee uch a cro over and mutation

7 Croover The croover proce i one of the characteritic feature of the genetic algorithm to improve the tability of the population. The population conidered here i the et of candidate olution obtained after the fitne evaluation. The croover operation i executed by electing two candidate olution from the population and interchanging there character. A point i et to cro the two candidate olution and uch point i termed a the croover point. Let u conider the following example; we elect the following candidate olution for the croover proce, G3 G4 G8 G10 G11 G12 G2 G3 G5 G6 G7 G10 Fig.3. parent olution In the above figure 3, the two parent candidate olution are repreented, which are ubjected for the croover proce. The haded are in the olution are conidered a the croover point of the parent. Offpring1 G2 G3 G5 G10 G11 G12 Offpring2 G3 G4 G8 G6 G7 G10 Fig.4. offpring olution The figure 4 repreent the offpring olution obtained after the croover proce and the dark haded area repreent the croed parent characteritic. The offpring are then ubjected for the fitne evaluation. 3.5 Mutation The mutation proce i an aociated proce of the croover proce. The offpring generated after the croover proce are elected for the mutation proce. In mutation proce, one point i randomly elected from the offpring and a new character i aigned to elected point by replacing the exiting one. Offpring1 G2 G3 G5 G10 G11 G12 New offpring G2 G3 G5 G10 G9 G12 Fig.5 mutation The figure 5 repreent the proce of mutating an offpring according to the definition of genetic algorithm. In imilar way all the offpring are mutated. According to the genetic algorithm; a new population i created by applying croover and mutation over the initial population. The feaibility of the newly generated population i calculated baed on the fitne evaluation function defined by the propoed approach. Thu after the GA, the reultant will be a et of optimized candidate olution, which are provided with effective cot and acceptable range of rik. 4. Rik Analyi 1. Rik prioritization The cot analyi of the Tropo goal model pecifie the cot required for achieving the goal with the aociation from the event layer and the upport layer. The goal are not only aociated with cot but alo the rik aociated with it. The identification of rik i quite a tediou tak in the Tropo goal model, becaue a ame element can provide a plu and a negative impact in achieving the goal. So election of rik hould be o pecific to achieve the goal with minimum cot and acceptable rik. In the propoed approach, we incorporate a rik prioritization proce to analyze the rik. Conidering the SAT level and DEN level, the object in each layer can be grouped to et of rik. But, the level of the rik cannot be identified from thoe two parameter alone, o the rik prioritization play the role here. The rik prioritization can be calculated by probability method. The probability of an event to become a rik i calculated by the following equation, n( DEN) probabiltye ( ) n( DEN SAT) (1) Here, in eq (1) probability (e) i the probability of rik by an element e in the tropo goal model, n(den) i the number of element aociated with element e having DEN. The n(den+ SAT) i the number of element aociated with element e having SAT and DEN. According the probability value of the element, we create a lit called rik priority lit R lit. R lit [ e1, e2,..., e3] The element i aigned a rik level baed on it priority and the SAT level of the element aociated with the element. A threhold i et for characterizing the rik for particular element baed on the priority value and SAT level. An element i conidered a rik by,, if probabilit y( e) threhold and SAT( ) Rik( e), if probabilit y( e) threhold and SAT( ) So according to the ituation, the rik prioritizing factor confirm the element with mot rik for the tropo goal model. The rik prioritizing proce help to differentiate high rik and low rik element. The element with higher rik can affect eriouly on the cot of the goal, while element with lower rik level can be mitigated. Thu the

8 257 rik prioritization help in reducing the total number of element, which i conidered a rik and that intern peed up the cot analyi phae. Baed on the rik value derived from the above expreion, we create rik priority lit. R [ R( e1), R( e2),..., R( e priority _ lit The R priority_lit i ued in the cot to rik analyi phae in order to find the relevant rik to reach the target goal 2. Cot to Rik Analyi The cot analyi proceed to the cot to rik analyi. In thi analyi phae the cot and rik of the candidate olution are evaluated. The candidate olution conidered in the cot to rik analyi i the filtered olution from the cot analyi phae. Thi phae i initiated in order to analyze the rik affect for each of the candidate olution. The node taking part in the each of the candidate olution are analyzed thoroughly. The analyi conider the following parameter, chance_of_rik, chance_of_acceptance and chance_of_denial. The chance_of_rik i baed on the evidence of likelihood and everity of event which trigger target goal. I.e. if the likelihood of the event i high, it will affect in achieving the goal. The event which provide the high likelihood i a rik then the target goal will reult in denial. Similarly, chance_of_acceptance i related to the SAT () value and the chance_of_denial i baed on the DEN () value. The total rik i calculated by auming Null=1, Partial=2, and Full=3 and umming up the DEN value for all top goal. Thi mean that for the acceptable rik level. A cot to rik graph i plotted for the aement of relevant candidate olution. The proceing model can be depicted a following. The figure 6, illutrate the working model of the cot to rik analyi of the propoed goal rik model. The above analyi eparate the candidate olution; thoe poe an acceptable rik meaure and cot effectivene. 5. Experimental Reult The experiment i conducted in Java runtime environment in ytem configured to a proceor of 2.1 GHz, 2 GB RAM and 500 GB hard dik. The experimental evaluation are provided in the following ection. The propoed goal rik model i baed on two analye and thoe analye are ued to judge the relevant candidate olution. The experiment ue the input data from a manually generated ource a the goal model of Software Development Company. 5.1 Cae tudy decription and input The propoed goal rik model ue a cae tudy of a oftware development company, which i targeted to a prime goal. The prime goal i aigned a Earn money n )] (G1) and a number of goal are aociated with G1 to make G1 achievable by the SDC. Thu a Tropo goal model i defined over the SDC and by conidering the SAT and DEN value a et of candidate olution are generated. The figure 7(Not Shown) repreent the candidate olution generated after the Tropo goal model. We have conidered olution of length 6 to 8 for the efficient evaluation of the cot and rik of the SDC. The aociated goal are aigned cot value by randomly generated program and the cot value are aociated with the event and upport defined in the Tropo goal model of the SDC. The aociation lit and cot value of different parameter are lited below. Event value Goal cot upport E1 7 G1 4 S1 8 E2 7 G2 5 S2 0 E3 4 G3 4 S3 2 E4 7 G4 6 S4 4 E5 5 G5 1 S5 4 E6 0 G6 5 S6 2 E7 0 G7 3 S7 7 E8 2 G8 6 S8 9 E9 4 G9 3 S9 5 aociation E10 1 G10 2 S10 5 Table.1. value table Thu, baed on the value of the table, the cot of the generated candidate olution are calculated. Now, the genetic algorithm i applied to the generated candidate olution. So, according to the genetic algorithm a number of olution are extracted from the initial population a the cot effective olution of the SDC. Candidate olution [G4, G7, G8, G9, G10] [G4, G6, G7, G8, G9] [G2, G7, G8, G9, G10] [ G3, G4, G5, G6, G7] [G2, G3, G4, G5, G6, G7, G8] [G2, G4, G5, G6, G7, G8, G9, G10] Table.2. olution obtained after GA Now, the cot to rik analyi defined in the propoed approach calculate the cot and relevant rik to the obtained candidate olution. In the cot to rik analyi phae, the cot and rik of the candidate olution for achieving the target goal are analye. The rik i calculated baed on the DEN() value of the candidate olution under conideration. The denial rate of the candidate olution i baed on the impact of event and upport node of the olution.

9 258 Filtered Candidate Solution Chance_of_rik Chance_of_accepta nce Rik prioritization Cot to Rik Analyi Cot+rik baed candidate olution Chance_of_denial T r DEN () Relevant olution Likelihood/everi ty SAT () Fig.6. Cot to rik analyi If the node are poeing high rik value or poeing high denial rate then the denial rate of the candidate olution will be higher. Conider the rik impact on the olution S3, S3 G2 G4 G7 G8 G10 G11, Where, G2, G7 and G11 having partial denial value. Thu the rik can be calculated a the um of the evidence DEN (S3). The rik value are ranging from 3, 2 and 1 for full, partial and null denial repectively. Thu the rik of S3 can be given by, Rik (S3) 2+2+2= 6, ince DEN (G2) = DEN (G7) =DEN (11) = P. Similarly, the rik regarding all the candidate olution are calculated and the graph i plotted baed on the rik and cot value. On the cot to rik analyi, we incorporate the rik priority value alo with the rik calculation. So, the incorporation of the rik priority value help in reducing the level of rik by it priority. i.e. if a rik i calculated a 3 and another rik i calculated a 4, but if the rik with value 3 ha a rik priority mapped a high and the rik with value 4 i mapped with rik priority low. Thu, according to the priority, we choe rik with value 3 a dominant rik a compared to the rik with Value 4. The cot and rik value of the candidate olution obtained after GA i plotted in the below table Candidate olution cot Rik [G4, G7, G8, G9, G10] 18 3 [G4, G6, G7, G8, G9] 23 6 [G2, G7, G8, G9, G10] 19 8 [G3, G4, G5, G6, G7] 19 5 [G2, G3, G4, G5, G6, G7, 31 4 G8] [G2, G4, G5, G6, G7, G8, 31 3 G9, G10] Table.3. cot to rik analyi The table 3 how the different candidate olution with cot and rik value and now the tak i to elect a candidate olution, which cot and rik effective. A rik priority mapping of the calculated rik value are ubjected to fix the above problem. A candidate olution i conidered after checking with the rik priority graph. The figure 8 repreent the rik priority graph generated from the propoed approach baed on the rik value of the candidate olution obtained after the GA. According to the analyi from the cot to rik table and rik priority graph, the candidate olution [G2, G3, G4, G5, G6, G7, and G8] i elected a the effective olution for the SDC. The olution i conidered becaue, it conider mot of

10 259 the aociate goal and it ha le rik priority a compared to the other candidate olution Concluion Rik and priority value Fig.8. rik priority graph Rik Priority The propoed requirement engineering model i baed on the Tropo goal model. A modified Tropo goal model i ued in the propoed goal rik model. The goal rik model conit of three layer, and in the top level goal to be achieved by the proce i plotted and in the econd level, the event that trigger the goal and in the bottom level, the upporting parameter for the goal and event are plotted. The propoed approach alo add an optimization technique with the propoed approach. A genetic algorithm driven candidate olution i incorporated with the goal model to get efficient candidate olution. The rik analyi of the propoed GR model i conducted baed on two analye, the cot analyi, rik priority calculation and the cot to rik analyi. The experimental evaluation i carried out on a cae tudy conidering a oftware development company. The reult howed that the propoed goal rik model ha attained olution with acceptable cot and rik Reference [1] Roy GG, Wooding TL (2000) A framework for rik analyi in oftware engineering. In: Proceeding of the eventh Aia- Pacific oftware engineering conference (APSEC 00), IEEE Computer Society Pre, Wahington, DC, USA, p 441 [2] Boehm BW (1991) Software rik management: principle and practice. IEEE Software, pp doi: / [3] B. W. Boehm. Software Rik Management: Principle and Practice. IEEE Software, pp.32 41, [4] Rik Analyi a part of the Requirement Engineering Proce YuditiraAnar Paolo Giorgini March 2007 [5] Focuing on the Importance and the Role of Requirement Engineering Mina Attarha and Naer ModiriAtta.mina@yahoo.com ; [6] H.F. Hofmann and F. Lehner, Requirement engineering a a ucce factor in oftware project, IEEE Software,vol 18, no 4, pp , [7] B.A. Nueibeh and S.M. Eaterbrook, Requirement engineering: A roadmap, Proc. of the 22nd Intl. Conf. on Software Enginnering (ICSE 00), IEEE Computer Society Pre, June 2000, pp [8] T. Hall, S. Beecham and A. Rainer, Requirement problem in twelve oftware companie: An empirical analyi, IEEE Software, vol 149, no. 5, pp , [9] M. Niazi and S. Shatry, Role of requirement engineering in oftware development proce: An empirical tudy, Proc. of the 7 th Intl. Multi Topic Conf. (INMIC2003), IEEE Computer Society Pre, Dec 2003, pp [10] JacKyAng, Sook Bing Leong, Chin Fei Lee, UmiKalomYuof, Requirement Engineering Technique in Developing Expert Sytem, School of Computer Science UniveritiSain Malayia, IEEE, ympoium on computer cience and informatic, 2011, pp.1-2 [11] Luka Pilat and Hermann Kaindl, A Knowledge Management Perpective of Requirement Engineering, Intitute of Computer Technology Vienna Univerity of Technology Vienna, Autria, IEEE Conference: May 2011, pp [12] Mina Attarha and Naer Modiri, Focuing on the Importance and the Role of Requirement Engineering, Interaction Science (ICIS), th International Conference, Aug. 2011, pp [13] YuditiraAnar, Paolo Giorgini, Rik Analyi a part of the Requirement Engineering Proce, Departmental Technical Report, [14] GolnazElahi, Eric Yu, Nicola Zannone, Security Rik Management by Qualitative Vulnerability Analyi, Third International Workhop on Security Meaurement and Metric, pp: 1-10, [15] K. Venkateh Sharma and Dr P V Kumar An Efficient Rik Analyi baed Rik Priority in Requirement Engineering uing Modified Goal Rik Model", International Journal of Computer Application, Vol. 73, No. 14, pp , 2013 [16] K. Venkateh Sharma and Dr P. V. Kumar. "An efficient goal oriented rik analyi in requirement engineering." Advance Computing Conference (IACC), 2013 IEEE 3rd International. IEEE, [17] K. Venkateh Sharma, and Dr P. V. Kumar. "An efficient rik analyi in requirement engineering." Engineering (NUiCONE), 2012 Nirma Univerity International Conference on. IEEE, Firt Author Mr K. Venkateh Sharma, i a Reearch Scholar in JNTU Kakinada, did hi B.E (Electronic) from Shivaji Univerity, Maharahtra, completed M.Tech in Information Technology from Punjabi Univerity, M.Tech in CSE from JNT Univerity, Hyderabad and PGDCA. He ha 14 year of teaching experience in variou engineering college. Currently he i working in CSE dept at TKR College of Engineering& Technology a Aoc Prof. Second Author Dr. P.V Kumar, Profeor of CSE in Omania Univerity Hyderabad, completed M.Tech (CSE) and PhD (CSE) degree from Omania Univerity, Hyderabad. He ha 30 year of Teaching & R&D experience. Many tudent are working under him for PhD.He ha to hi credit around 56 paper in variou field of Engineering, which include Indian,international journal, National and International conference, He worked a Chairman BOS in OUCE and conducted variou taff development program and workhop. He i Life Member of ISTE, and CSI ocietie.