Automatic Test Data Generation using Genetic Algorithm using Sequence Diagram

Transcription

1 Iteratioal Joural of Computer Systems (ISSN: ), Volume 03 Issue 02, February, 2016 Available at Automatic Test Data Geeratio usig Geetic Algorithm usig Sequece Diagram Aju Bala, Dr. Rajeder Sigh Chhillar Departmet of Computer Sciece & Applicatios, M.D. Uiversity, Rohtak, Idia Abstract The most strikig feature of SDLC is software testig. It is very labour-itesive ad expesive process i software developmet ad hadlig as well as maiteace of software. The mai objective of this paper is to exted the testig techique. Testig is to show the icorrectess ad is cosidered to succeed whe a error is detected [Myers79]. Today s automatic testig has replaced maual testig with a great extet. Automatig testig is very helpful i reducig huma efforts to geerate test cases or test data. Test data or test case is a very tiresome task i software testig. It has multiple set of values or data that are used to test the fuctioality of a particular feature. All degrees of the test values ad coditios maitaied i separate files ad stored as test data. Test case or data geeratio is a set of coditios or rules that are developed for fidig the failure poits i a developig software. Nowadays, may researches have paid cosiderable attetio, focusig o test data geeratio techiques. This paper adopts a case study ad proposes a techique for test data geeratio, based o geetic algorithm usig critical path. Critical path testig is cosidered to solve the loopig problem ad improvig the testig efficiecy. Test data sceario is derived from sequece diagram. Sequece diagram reveals the sequece of calls i a system usig exchage of messages amog the objects of system. Keywords: Geetic Algorithm, UML, Sequece diagram ad graph, Test-data geeratio. I. INTRODUCTION Software testig is recogized as a key feature or a ievitable part of software developmet life cycle. It is the most exhaustive ad critical phase but uavoidable, i SDLC. Software testig is the pheomea of executig a program with the itet of fidig a error or fault to make the software error-free ad satisfy the ed-user. Testig is a very essetial phase, though very labour-itesive ad expesive process i software developmet ad hadlig as well as maiteace of software. It accouts for takig a large part of total cost, up to 50 percet for a etire software developmet [1]. Due to advacemet i techology, the software system as well as testig process has becomes very complex, to overcome from all these issues, testig process should be automated, proportioal to cost of developig software should be reduced sigificatly. So, a challegig part of this phase, etails to the geeratio of test cases or data. This geeratio is very crucial to the success of testig, because it is very hard part of software, to obtai a fully tested program, give that the large umber of test cases will be able, if eeded to fid maximum umber of faults i less umber of iteratios. Software program is ifiite, ad a suitable desig of test cases will be able to detect a maximum umber of faults. For the requiremet of good ad effective software quality assurace, the techique for automatic geeratio of test data, make efforts efficietly to systematically ucover the differet types of errors with a miimum amout of time ad efforts. A test data is a set of data performed i a sequece ad related to a test objective, which will produce a umber of tests iput values, observed output, expected output or test oracle eeded for the test to ru, such as eviromet prerequisites [2]. A good test case should have the quality to cover every aspect of test objective ad high rate of fault detectio. This method is used for automatic geeratio of test case usually based o efficietly searchig or fidig small set of test cases with high probability of detectig as-yet udiscovered error [1]. There have bee few efforts o represetig a cosiderable attetio, which covers all existig automatic test case geeratio approaches. I this paper, we exted a geeral classificatio for automatic test data geeratio approaches with compariso betwee existig approaches, to show that the existig approaches are resource-itesive as well as limited budget. So, this paper presets a geeric approach i.e. Geetic Algorithm, is the most acceptable approach to geerate automatic test data or test case. This paper is preseted as follows: Sectio 2 review the related work of test-data geeratio techiques. May testdata geeratio techiques have bee developed. Sectio 3 gives a overview o itroductio of UML ad its diagram, otatio ad termiology of sequece diagram. Sectio 4 gives the itroductio of GA. Sectio 5 describes the proposed approach for geeratio of test data. Sectio 6 presets case study of o-lie appoitmet i hospital. Sectio 7 presets coclusio ad future work of this paper. II. RELATED WORK May researches have bee doe i the field of test data geeratio techology. Recetly, may techiques have bee proposed, all related to Geetic Algorithm. Parvee Raja ad T.H. Kim [5] developed variable legth geetic algorithm that optimized ad selected the path testig coverage criteria. Geetic algorithm used i Cotrol Flow Graph (CFG). Testig covers every possible 131 Iteratioal Joural of Computer Systems, ISSN-( ), Vol. 03, Issue 02, February, 2016

2 path i Software uder Test (SUT). Weights are assiged to edge of CFG by applyig rules. Xathakis et al. [8] preset geetic algorithm to geerate the test data. Geetic algorithms are used for geeratio of test data for structure. A path is selected by the user ad the relevat braches are executed from program. Fitess fuctio is calculated by summatio of brach predicated. V. Mary Sumalatha, G.S.V.P. Raju [9] presets the test case geeratio by meas of UML sequece diagram usig geetic algorithm from which best test cases optimized. Geetic algorithm applied o sequece graph. All paths discover from source to destiatio with loops ad calculate the fitess value. Sageeta Sabherwal [7] proposed a techique for prioritizatio of test cases sceario derived from activity diagram ad state chart diagram usig geetic algorithm cocepts, stack ad iformatio flow (IF) diagram. Stack based applicatio is adopted for assigig the weight to each ode of activity ad state chart diagram. Bo Zhag ad Che Wag [3] use simulated aealig algorithm ito geetic algorithm to geerate the test data for path testig. A simulated aealig algorithm is ispired by the aealig of metals. I this method, solid is heated from high temperature ad cooled dow slowly to maitai thermodyamic equilibrium of system. The adaptive geetic simulated aealig algorithm is proposed by Zhag to automatically geerate the test data. The steps if this algorithm is show i his paper. The fitess value, crossover, mutatio aother modificatio are applied i geetic algorithm procedure. M. Harma [4] focused o automated test data geeratio usig search based software egieerig. Automated test data geeratio usig geetic algorithm is based o search based software egieerig. Sulta H. Aljahdali [8] preseted the limitatio of geetic algorithm i software testig. The majority of software test-data geeratio techiques are based o geetic algorithm. It attempts to compare ad classify the combiatorial problems accordig to geetic algorithm feature ad parameters. III. UML The Uified Modelig Laguage (UML) is stadard modelig laguage, is widely used to visualize, specify, costruct ad documet the mai artifacts of software system. UML is used i busiess modelig ad may architectural modelig, but here maily i software developmet modelig to desig ad implemet for compoet-based applicatios. I software developmet, it is very popular to geerate test cases or data with the help of graphical otatio i.e. rectagles, lies, ellipses etc provided by UML. It has ow sytax ad sematics. Apart from all of the aforemetioed techiques adoptio of UML, there is ievitable ature of UML is, oce the model is developed, the a variety of problems solved i.e. aalysis, specificatio, code ad test case or data geeratio, visualize ad uderstad the problem ad workig of the software testig. It is also helpful i bridgig the gaps betwee the desigers ad testers. The most importat modelig laguage has bee iveted by Grady Booch, Jim Rumbaugh i 1994 to combie the diagrammig otatios of two most popular methods of Booch ad OMT (Object Modelig Techiques).The UML ad its diagrams are widely used to visually depict the static structure ad more importatly, the dyamic behavior of applicatios. UML costructs differet types of diagrams, categorized ito two groups-structural diagrams ad behavioral diagrams [Vaugha07]. There are may types of diagram to capture five differet behavior of the system i.e. Class diagram, Compoet diagram ad Deploymet diagram are to represet the static behavior of system. Activity diagram, Sequece diagram, State diagram are used to represet the dyamic behavior of the system. Here, the focus is o sequece diagram ad activity diagram because sequece diagram is most popular i UML diagram to geerate test data or cases. A. Sequece Diagram A sequece diagram is also called a iteractio diagram ad most commo behavior diagram. It is very popular artifact of UML for dyamic modelig. It focuses o behavior of your system. Sequece diagram oly focuses o processes how they iteract to oe aother ad i what order. It costructs the message sequece chart. It shows all iteractio of objects i timely maer. It reveals the exchage of iformatio betwee objects ad classes i a sceario. It shows the fuctioality of a sceario. Sometimes, it is called evet diagram or evet sceario. Structure of sequece diagram represeted by followig otatios i.e. class ame is writte i boxes with colo, parallel vertical lies, differet processes, horizotal arrow represets message exchaged betwee objects, this sceario also shows i graphical ru time sceario. Sequece diagram is better tha other dyamic diagram. I this paper, a brief idea is give o compariso of sequece diagram to activity diagram with the itet of showig that the sequece diagram is better to geerate test data. Activity diagram focuses o a particular operatio of a object. But sequece diagram focuses o the way of process executio i sequece, operatios ad its parameters but activity diagram reveals the workflow of operatios. Sequece diagram keeps track o the iteractio betwee objects, this feature exteds its usability for dyamic modelig. But i activity diagram, it focuses o how the process flow of a object. Fig1. Basic otatio of sequece diagram Sequece diagram expresses most formal level of refiemet. I research field, most of the software developer cosider sequece diagram because it is very helpful i documetig or predictig, how a system should behave i future or i other words, it is very useful i preset ad future level. From the orgaizatioal poit of 132 Iteratioal Joural of Computer Systems, ISSN-( ), Vol. 03, Issue 02, February, 2016

3 view, it shows how the busiess curretly workig by showig the iteractio betwee objects or how will be performed i future. Sequece diagram looks like as show i Figure 1. B. Basic Notatio of Sequece Diagram The basic otatios that are used i sequece diagram are described below: I. Actor: A participat or etity that iteracts with the system. II. Lifelie: A vertical lie reveals the sequece of evets, participate durig a iteractio. III. Uit: Represet a module or subsystem, compoet, uit or ru-time etity i the system. IV. Separator: Represet a iterface or boudary betwee subsystem, compoets or uits. V. Group: Header elemets ito subsystem or compoets. VI. Sychroous Message: The seder waits for a respose to a sychroous message before all cotiuous. VII. Asychroous message: A message that does ot require a respose before seder cotiuous. A asychroous message shows oly a call from the seder. VIII. Executio occurrece: A vertical shaded rectagle that appears o a participat s lifelie ad represets the period whe participates executig the operatio. IX. Callback message: A message returs to a participat that is waitig for a retur back. The resultig executio occurrece shows o top of existig oe. X. Self message: A message from participats to itself. The resultig executio occurrece appears o the top of sedig executio. XI. Create message: create a message to ivoke the first participat. If it receives it should be first. XII. Destroy message: Represets the destructio of a header elemets as a result of a call for aother elemet. XIII. Commet: A commet is additioal iformatio ca be attached to ay poit o a lifelie. XIV. Time start: it is a startig poit.. XV. Time expiratio: it shows the expiry time. IV. GENETIC ALGORITHM A. INTRODUCTION Geetic Algorithm is based o atural pheomea. The fudametal cocept behid GAs is atural selectio ad geetic iheritace. The fouder of GA is Joh Hollad, uiversity of Michiga (1970) i U.S.A developed a remarkable idea i the field of heuristic search. It is based o the evolutioary priciple. GA belogs to the class of probabilistic algorithm. GA empirically provides directed search algorithms rely o the mechaics of biological evolutio. The best part behid to use GA tha other searchig algorithm is that GA performs a multi-directioal search by maitaiig the optimized solutios ot a sigle poit. Mai applicatio areas of GA are- AI, busiess, scietific ad egieerig circle etc. But these ca be used i may forms like to geerate automatic test cases etc. GA is a class of probabilistic optimizatio algorithm. Geetic Algorithms are categorized as global heuristic search or GA is good heuristic search for combiatorial problems [20]. Ex.TSP, pe movemet of a plotter, measure real world routig of school, prisoer s dialemma. The simple form of GA is give by followig. This algorithm is stopped whe populatio covers all the optimal solutio. Simple geetic algorithm: Simple_Geetic_Algorithm( ) { Iitialize the populatio; /*geerate the iitial populatio*/ Calculate fitess fuctio; /* calculate fitess value for each idividual */ While(Fitess Value!= Optimal Value) { Selectio; /* radomly select idividual for matig*/ Crossover; /* apply crossover ad mutatio for gettig best idividual */ Mutatio; /* Calculate fitess value of ew idividual ad discard old idividual by maitaiig the fitess criteria of idividual. If populatio reaches to stoppig criteria or reach best idividual or populatio has coverged tha fiish = true. */ Ed Ed The basic steps of geetic algorithm a show i this step: Fig 2. Flow chart of geetic algorithm 133 Iteratioal Joural of Computer Systems, ISSN-( ), Vol. 03, Issue 02, February, 2016

4 Geetic Algorithm has basically three operators used for populatio to geerate ew offsprig. Iitializatio is the very first operator of geetic algorithm, it creates a iitial populatio of chromosomes, at the begiig of executio. Selectio of populatio is radom i iitializatio. Selectio operator is a process to select two chromosome from the populatio for reproductio. There should be a selectio mechaism, how these chromosomes will be selected. Maily expectatio is to select, such chromosomes that gives the better idividual. So, selectig such chromosomes at higher probability will produce better populatio after each iteratio of algorithm. Selectio must be balaced by maitaiig the diversity of populatio eeded for exploratio. So, too strog selectio ad so weak selectio will reach to slow evolutio. Roulette- Wheel, Rak-based, Touramet, Uiform ad Elitism is the classic selectio method i GA.[Goldberg89], [Hollad92]. Crossover is a very importat process to geerate ew idividual. Crossover probability should be more tha mutatio probability to get better idividual. It is a very practical method of sharig the iformatio betwee two chromosomes. I GAs operator, crossover is most valuable feature especially where buildig blocks (i.e. schemas) exchage is ecessary [Arabas94]. Its implemetatio ad represetatio is closely related to problem domai or problem depedet. Sigle - poit crossover is most importat crossover operator. I this type of operator positio is chose by radomly ad elemet of two parets before ad after mutually exchaged to produce a better idividual. Mutatio is a process to trap the local optima ad pruig o dead state. Its probability is always less tha crossover to give better outcome. Mutatio operator alters oe or more bit value i the chromosomes to ehace the structural probability. Mutatio operator play the best role to protect the populatio agaist pre-mature covergece at ay particular area of the etire search space[mitchell96]. It works oly o oe chromosome at a time ot more tha oe like i crossover. Most commo methods of mutatio are as follow: 1. Bit-flip mutatio: chromosome s bit value is chaged with a mutatio probability. 2. Uiform-mutatio: bit select radomly ad chage its value. Evaluate fuctio or fitess fuctio is also called objective fuctio, it calculates the quality of cadidate solutio. Based o fitess fuctio, measuremet of sigle chromosome to rest of the populatio. This fuctio gives the value of specific chromosome. It is ot ecessary that it will reach to its fitess value. The fitess value is typically obtaied by a trasformatio fuctio called scalig ad it is worthwhile to simplify the evaluatio fuctio as much as possible because the evaluatio process itself has bee foud to be very expesive due to the time ad resources it cosumes [Arabas94]. Upo completio of crossover ad mutatio operatio, there will be origial paret populatio ad ew idividual populatio. A fitess fuctio determies which of these parets ad offsprig will survive i ext geeratio. These operatios are iterated util the expected goal is achieved. V. PROPOSED APPROACH To geerate appropriate test data for testig process. We will have to follow these steps: I. Draw the sequece diagram. II. Covert the sequece diagram ito sequece graph. For every coditioal message two edges takes place. Oe edge for true coditio ad aother edge for false coditio. III. Geerate set of path from sequece diagram with the itet to cover every brach of graph. Suppose some loop occurs i the diagram, it becomes very difficult ad puzzlig. So we give our best to try to fid critical path i graph usig the stack weight assigmet approach by this algorithm: Algorithm: 1. For every ode of graph i =1.; 2. By DFS approach, Push ode of graph o stack. 3. Determie smax of stack( the maximum size); 4. For = 1 to smax, assig weight w=smax-k to each ode of graph, where smax is maximum size of stack ad k is path followed by odes of graph. 5. For each decisio ode d. 6. Assig the same weight to brachig odes. 7. Updates loops by Isertig the ext eighbor ode of brachig odes. 8. Ed IV. Fitess value of each ode is calculatig of graph by usig additio of flow of iformatio ad poppig operatio require to a ode. Flow of iformatio is determied by usig this equatio: Flow of iformatio = icomig (a) * outgoig (b) Icomig meas how may edges are isertig ito a ode ad outgoig meas how may edges are leavig a ode. Fitess Value = Stack based weight approach + Flow of iformatio 134 Iteratioal Joural of Computer Systems, ISSN-( ), Vol. 03, Issue 02, February, 2016

5 V. Selectio of chromosome bits or test data depeds upo coditioal odes of graph. VI. Calculate the fitess value of each possible path that is followed by coditioal odes of graph. VII. Now, probability of selectio for each path is calculated by Roulette wheel selectio : Pi= F(xi) / F(xi) After probability, calculate cumulative probability Ci for each path: Ci = Pi VIII. Geerate radom umber for each test-data. IX. To select the chromosomes determie value of colum N where eed to fid how may test data umber that has cumulative probability is greater tha radom umber. X. The value of matig pool colum is determiig umber of times a test-data appear i N colum. XI. Select the correspodig chromosome or test data that has higher value appear i N colum. XII. Pair-wise sigle poit crossover is applied ad select those child that has maximum fitess value. The probability of crossover operator is 0.8. Crossover is carried out oly if the radom umber is less tha 0.8 otherwise mutatio operator s applied. XIII. Bit-flip mutatio is applied to iterchagig of sigle bit i test-data or chromosome. XIV. Recalculate the fitess value of ew geeratio. XV. Repeat this process util maximum fitess value is reached or all paths are covered i graph or o stoppig criteria is met. 7. If the patiet becomes ok after ext appoitmet, the patiet ca leave, otherwise ca take appoitmet agai. 8. If o eed of appoitmet ay more, the the appoitmet will be available for ext the patiet. Sequece diagram is costructed accordig to these steps. I this diagram, patiet, receptioist, doctor ad urse play the role of objects, they will exchage the messages amog themselves for givig ad takig olie appoitmets. Sequece diagram show i figure 3. Further, Sequece diagram is coverted ito sequece graph as show i figure 4. : Patiet : Receptioist : Doctor : Nurse Take Appoitmet Check Appoitmet Caot meet Cosult Doctor Check Patiet VI. CASE STUDY I this sectio, we study a approach by usig sequece diagram of o-lie appoitmet i hospital. I Olie appoitmet user will have to follow this step: 1. The patiet requests for appoitmet. 2. The receptioist checks the appoitmet, appoitmet is available or ot accordig to doctor s schedule. 3. If appoitmet is ot available, the system geerate message ad we ca t take the appoitmet. 4. If appoitmet is available, the the user ca cosult to the doctor. If patiet has a mior problem, the doctor suggests some prescriptio. 5. If the patiet has a major problem, the doctor checks his or her record. 6. Accordig to the patiet s record, the doctor suggests more prescriptio ad gives appoitmet agai. Patiet Ok Patiet eed more treatmet Check Patiet Record Patiet OK If ot OK appoit agai Treat agai Leave Fig.3 Sequece diagram of o-lie appoitmet 135 Iteratioal Joural of Computer Systems, ISSN-( ), Vol. 03, Issue 02, February, 2016

6 Fig 4. Sequece graph of olie-appoitmet. Now critical path is determie by stack weight assigmet approach as show below i this table: Table 1. Critical path is determied Node Path followed by ode, k Stack size, smax Weight= smax-k , , , , , ,12, , Now complexity of each ode is determied by usig this table: Table 2. Complexity of each ode calculated Node Poppig operatio, a Iformati o Flow, b Fitess value= a+b *1= ,8,6,4 1*2=2 13,10,8, *1= *2= *1= *1= *1= *2= *1= *2= ,3 1*1=1 7,4 12 7,5,3 2*2=4 11,9, *1= *1=1 2 Iitial possible populatios are: 1000, 1010, 1011, 1111, ad The path 1,2,3,4,6,7,8,9,10,12,13,14 is followed by the 1000 chromosome ad correspodig fitess value is =99. The ext chromosome 1010 follow the path 1,2,3,4,6,7,8,9,10,11,13,14 ad correspodig fitess value is =95. The chromosome 1011 follow the path is 1,2,3,4,5,12,13,14 ad correspodig fitess value is =72. The chromosome 1001 follow the path is 1,2,5,12,13,14 ad correspodig fitess value is =50. The chromosome 0000 follow the path is 1, 2, 3, 4, 6, 7,8 14 ad correspodig fitess value is =76. The chromosome 1111 follow the path 1,2,3,4,6,7,8.9,10,12 ad correspodig fitess value is =89. Selectio of ew geeratio are show i tables where, X is test data, F(X) is fitess value if test data, Pi is probability, Ci is cumulative probability, S is Selectio, C is Crossover ad M is Mutatio. Table 1. Fitess value of iitial populatio S. No. X F(x) Pi Ci R Iteratioal Joural of Computer Systems, ISSN-( ), Vol. 03, Issue 02, February, 2016

7 S.No. Table 2. New geeratio selectio Selectio Crossov er poit Crossov er Mutatio F(x ) Table 7.Fitess Value of iitial Populatio S.No. X F(X) Pi Ci R Table3. Fitess Value of iitial populatio S.No X F(X) Pi Ci R S. N Table 8. New Geeratio Selectio Selecti o Crossover poit Crossover Mutatio F (x) S. o. Table 4. New Geeratio Selectio Selectio Crossove r poit Crossove r Mutatio F ( X) S.No S. No. Table 5. Fitess Value of iitial populatio X F(X) Pi Ci R Table 6.New Geeratio Selectio Selectio Crossove r poit Crossove r Mutatio F ( X) After 8 th iteratio as show i table 8, we fid test data 1010 has highest fitess value 85 amog all of them. So path correspodig to this chromosome is 1,2,3,4,6,7,8,9,10,12,13 tested first. This paper makes a efforts, to exted the previous work that is also related to test data geeratio by activity ad state chart diagram usig prioritizatio geetic algorithm. I this paper, we adopt sequece diagram for test-data geeratio with compariso to activity diagram. Sequece diagram is very coveiet, easy to draw ad uderstad ad gives fast result tha activity diagram. Sequece diagram has most challegig features to gives us all details i.e. order of message betwee objects, assig resposibilities ad timer. With the help of sequece diagram, chagig ca be made very easily ad efficietly with well uderstadig. Naïve user ca makes the correctio easily i real time eviromet. Sequece diagram is oe of the popular tha others UML diagrams because it is the most formal level of refiemet. So oe ca say that sequece diagram is more acceptable tha the activity or the state chart diagram. Besides i this article, selectio of test data for ext geeratio is based Roulette Wheel Selectio ( compariso betwee cumulative probability ad radom umber geeratio) which help i reducig the iteratios of tables. VII. CONCLUSION This paper presets the test data geeratio by usig UML sequece diagram. Geetic algorithm is used for selectio of those test data that have highest fitess value. Roulette Wheel Selectio is used to select ext test data. Stack based weight approach is used to calculate critical path i graph. Geetic Algorithm techique also helps i icreasig the software testig efficiecy ad GA also useful to solve trivial problem. Operators (mutatio ad crossover) are the best to pruig the populatio ad give best idividual. I future, we try our best to fid more coveiet way to make easy i geeratio of test data ad test cases. Operators of geetic algorithm that ca apply o 137 Iteratioal Joural of Computer Systems, ISSN-( ), Vol. 03, Issue 02, February, 2016

8 software testig ad help i improvig the more testig efficiecy. REFERENCES [1] R. Blaco, J.Tuya ad B. Adeso-Díaz, Automated test data geeratio usig scatter-search approach, Iformatio ad Software techology, vol. 51, Issue 4, (2009), pp [2] Xathakis S, Ellis C,Skourlas C, Le Gall A, Applicatio of Geetic algorithms to Software Testig. I 5th Iteratioal Coferece o Software Egieerig ad its Applicatios pp [3] T. Blickle, L. Thiele, A Compariso of Selectio Schemes used i Geetic Algorithms. TIK-Report, Zurich, [4] B. N. Biswal, S. S. Barpada ad D. P. Mohapatra, Iteratioal Joural of Computer Applicatios, vol. 1, Issue 14, (2010). [5] Bo Zhag, Che Wag, Automatic Geeratio of Test Data for Path Testig by Adaptive Geetic Simulated Aealig Algorithm, IEEE, 2011, pp [6] M. A. Ahmed, I. Hermadi, Geetic Algorithm based multiple paths test data geerator, Computer ad operatios Research (2007). [7] Parvee Raja Srivastava, Tai-hoo Kim, Applicatio of Geetic Algorithm i Software Testig, Iteratioal Joural of Software egieerig ad its Applicatio, Vol. 3, No.4, October 2009, pp [8] Sageeta Sabharwal, Ritu Sibal, Chayaika Sharma, Prioritizatio of test cases scearios derived from activity diagram usig geetic algorithm, ICCCT, IEEE, 2010, pp [9] Sageeta Sabharwal et al., Applyig Geetic algorithm for Prioritizatio of test cases Sceario derived from UML diagrams, Iteratioal joural of computer sciece, Vol.8, Issue 3, No.2, May [10] Sulta H. Alijahdali et al, The Limitatio of Geetic Algorithm i Software Testig, pp [11] V. Mary Sumalatha, G.S.V.P. Raju, Object Orieted Test Cases Geeratio Techiques usig Geetic Algorithms, Iteratioal Joural of Computer Applicatios Vol. 61- No.20, Jauary 2013, pp Iteratioal Joural of Computer Systems, ISSN-( ), Vol. 03, Issue 02, February, 2016