Research Paper A UNIFIED FRAMEWORK FOR MULTI-OBJECTIVE TEST CASE PRIORITIZATION IN REGRESSION TESTING Lilly Raamesh

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1 Research Paper A UNIFIED FRAMEWORK FOR MULTI-OBJECTIVE TEST CASE PRIORITIZATION IN REGRESSION TESTING Llly Raamesh Aress for Corresponence Department of I.T, St. Joseph s College of Engneerng, Ol Mamallapuram Roa, Chenna, Taml Nau 600 9, Ina ABSTRACT Testng s an ntegral part of any software evelopment. Ths paper focuses on prortzng the mult-objectve test cases n a system whle conuctng the regresson testng so as to reuce the number of test cases n a test sute an prortze them whle retanng the hghest percentage of the orgnal test sute s fault etecton effectveness. Regresson testng s an mportant process as t s to be ensure that changes that are mae have not ntrouce new faults. Regresson testng s expensve f t allows the re executon of all test cases. In ths stuaton, test case prortzaton technques am to mprove the effectveness of regresson testng by orerng the test cases n an orer that attempts to maxmze the objectve functon. In ths paper, n orer to maxmze the effectveness of the system the reucton, optmzaton an prortzaton of test cases are combne usng mnmum reunancy an maxmum relevance (mrmr) feature selecton an genetc algorthm. So, a unfe framework s ntrouce for mult objectve test case prortzaton for regresson testng. Ths paper focuses on prortzng the mult objectve test cases n a system whle conuctng the regresson testng by usng feature selecton an genetc algorthm. Feature selecton removes reunancy an selects maxmum relevance test cases. A genetc algorthm s a robust search technque, often utlze to entfy the accurate or approxmate solutons for optmzaton an search problems KEYWORDS: Genetc algorthm; mnmum reunancy an maxmum relevance; Relevancy threshol; Reunancy threshol; Mutual nformaton; Mutaton probablty; Eltst selecton genetc algorthm.. INTRODUCTION The rap evelopment of software mae testng a challengng role n software evelopment. Whle performng testng the effectveness of testng epens on the test cases that are use. A test case s a set of contons or varables uner whch a tester wll etermne f a requrement upon an applcaton s partally or fully satsfe. Usually the testng can be one wth all possble test cases to fn all the faults. But as the number of test cases s ncrease the project tme an cost s also ncrease. It s a must to reuce the number of test cases so as to have an effcent system. Regresson testng can be use to ensure that changes to a program for the purposes of ncreasng functonalty or removng faults oes not have any negatve mpact on the correctness of the software.a varety of objectve functons are applcable; one such functon nvolves rate of fault etecton-a measure of how quckly faults are etecte wthn the testng process. An mprove rate of fault etecton urng regresson testng can prove faster feeback on a system uner regresson test an let ebuggers begn ther work earler than mght otherwse be possble. To reuce the cost of regresson testng, software testers may prortze ther test cases so that those whch are more mportant, by some measure, are run earler n the regresson testng process. One potental goal of such prortzaton s to ncrease a test sutes rate of fault etecton. Ths paper focuses on prortzng the mult objectve test cases n a system whle conuctng the regresson testng so as to reuce the number of test cases n a test sute an prortze them whle retanng the hghest percentage of the orgnal test sute s fault etecton effectveness. In ths work, n orer to maxmze the effectveness of the system the reucton an prortzaton of test cases are combne usng mnmum reunancy an maxmum relevance (mrmr) feature selecton an genetc algorthm. So, a unfe framework s ntrouce for mult objectve test case prortzaton for regresson testng. Int J Av Engg Tech/Vol. VII/Issue I/Jan.-March,06/ In ths paper, the reucton, optmzaton an prortzaton are one base on the objectve functon whch consers the objectves lke memory usage, executon tme (cost), completeness (coverage) an fault coverage. The fnal objectve functon Z nclues 4 sub objectve functons {f, f, f 3, f 4} whch escrbes the objectves. The objectves set X s gven as {x, x, x 3, x 4}. Objectve functons Z = {f, f, f 3, f 4}. where the objectve functon (f ) for memory usage(x )s to select test cases from the test sute T whch satsfes () mn f (x,t), x = mu - mf for all = to n () where, mu memory use after the executon of th test case mf memory free before the executon of th test case The objectve functon (f ) for executon Tme(x ) s to select test cases from the test sute T whch satsfes () mn f (x,t), x = ft - st for all = to n () where,ft s the fnsh tme of the th test case st s the start tme of th test case The objectve functon (f 3 ) for Completeness (x 3) s to select test cases from the test sute T whch satsfes (3) max f 3 (x 3,T), x 3 = sc + pc + bc + fc for all = to n (3) where, sc s the statement coverage value of test case t pc s the path coverage value of test case t bc s the branch coverage value of test case t fc s the functon coverage value of test case t The objectve functon (f 4 ) for fault coverage (x 4) s to select test cases from the test sute T whch satsfes (4) max f 4 (x 4,T), x 4= f /N for all = to n (4) where, f s the faults etecte by the th test case N s the total number of faults n the system.. RELATED WORKS Numerous prortzaton technques have been escrbe n the lterature [3,7,8,0] have shown that at least some of these technques can sgnfcantly ncrease the rate of fault etecton of

2 test sutes. The rates of fault etecton prouce by prortzaton technques can vary sgnfcantly wth several factors relate to program attrbutes, change attrbutes, an test sute characterstcs []. Test Case Prortzaton (TC has been prmarly apple to mprove the effect of regresson testng []. Mutual Informaton has been perfectly utlze n the approach calle mrmr (mnmum Reunancy- Maxmum Relevance) [4] whch ams at obtanng maxmum classfcaton or precton performance wth a mnmal subset of varables by reucng the reunances among the selecte varables to a mnmum an to maxmze ther relevance. Even genetc algorthms were use for feature selecton []. Then mult objectve genetc algorthms were use [3] for feature selecton. The genetc algorthm performs well n test case prortzaton [,] an was expermentally teste [,3]. Hybr Genetc Algorthm (HGA) [8] was propose for obtanng the best possble number of test cases for the purpose of optmzaton. From the survey, t s entfe that the feature selecton algorthm fns a very lmte applcaton to test cases. From the test sute the relevant test cases are selecte an the reunant ones are omtte. GA s one such evolutonary algorthm. GA has emerge as a practcal, robust optmzaton technque an search metho. A GA s a search algorthm that s nspre by the way nature evolves speces usng a natural selecton of the fttest nvuals an t s prove that Genetc Algorthms performe well n test case prortzaton. Hence, base on the survey, a unfe mult objectve test case prortzaton system s evelope for regresson testng to ncrease the effectveness of the testng system. 3. Test Case Reucton Usng mnmum Reunancy Maxmum-Relevance (mrmr) Feature Selecton The mnmum Reunancy-Maxmum Relevance (mrmr) approach s use to select the maxmum relevant test cases from the test sute wth respect to the objectves whle avong the reunant ones. It uses mutual nformaton to analyze relevance an reunancy. Mutual nformaton s a basc concept n nformaton theory. The mutual nformaton (MI) of two ranom varables s a quantty that measures the mutual epenence of the two ranom varables. The mutual nformaton (MI) between the test cases an the objectve s calculate (the relevance term). Then the average MI between the test cases an the remanng test cases that are alreay selecte s compute (the reunancy term). The general feature selecton problem efne for mnmal-reunancy-maxmal-relevance (mrmr) s mofe accorng to test case reucton problem an s efne as Gven the nput ata D table as T test cases an X features (objectves) such as {x, x, x 3, x 4} an the target classfcaton varable c, the feature selecton problem s to fn from the 4-mensonal observaton space, R M, a subspace of x features, R x, that optmally characterzes c. The mrmr feature selecton algorthm for test case reucton s gven as Algorthm Int J Av Engg Tech/Vol. VII/Issue I/Jan.-March,06/ (Intalzaton): set X ntal set of 4 objectves, X={x,x,x 3,x 4 }, set T ntal set of test cases, T = { t, t,t 3 t n } an Intal test case set S { } an R { }. (Max-Relevance) Compute I (x, t ) as n (3.0)for all t ε T an x ε X select the test case t that maxmzes I(x, t ) an a t to S, S { t } repeat for all t ε T repeat for all x ε X 3. (Mn-Reunancy) Compute I(s, s j ) as n (3.)for all s, s j ε S select the test case s that maxmzes I(s, s j ) compare s an s j n terms of I(x, s ), I(x, s j ) select the s or s j wth max I an a R { s } repeat for all s ε S Repeat for all x ε X 4. Output the mrmr reuce test cases. 3.. Stages n mrmr Feature Selecton The stages n feature selecton are Input Evaluator Output 3... Input stage In the nput stage, the test sute T an the objectve set X are gven to the Evaluator. The objectve set X has 4 objectves such as memory usage, executon tme, completeness an fault Evaluator stage The evaluator stage n feature selecton uses two technques. Max-Relevance Mn-Reunancy () Max-Relevance The frst technque Max-Relevance s use to select the test cases wth relevant objectves such as maxmum fault coverage, mnmum executon tme, maxmum completeness an mnmum memory usage from the test sute. The test cases wth the most relevant features to the objectves are selecte from the test sute by the technque Maxmum Relevance. Max-Relevance (Hanchuan Peng et al 005) s to search test case wth features satsfyng Eq.(5), wth the mean value of all mutual nformaton values between nvual feature x an test sute T. max D ( X, T ), D x I ( x x T ) (5) x Ths frst technque acts as an rrelevancy flter, whch selects the subset of relevant test cases by removng rrelevant ones. The technque consers the value of the objectves that s avalable n each test case n the test sute. Then the test cases are ranke base on ther values of objectves. The rankng s one base on the mportance of the objectve to the system. In ths system the four objectves are ranke as fault coverage, executon tme, completeness an memory usage base on ther mportance to the test case prortzaton system. The objectve value s calculate base on MI between the test cases an the objectves usng Eq.(6). p( x, t ) I ( x, t ) p( x, t )log (6) p( x ) p( t )

3 P(x, t j ) s calculate as frequency of both x an t j / no of transactons P(x ) s calculate as frequency of P(x ) / no of transactons an P(t ) s calculate as frequency of P(t ) / no of transactons t j ncates the test cases n the test sute. The MI s calculate between each test case an the frst objectve. A hgher MI value between the test case an the objectve means more common nformaton content between them.if MI between the test case an the objectve larger than a threshol, enote by relevancy threshol or RLTH, these two that s the test case an the objectve are consere relevant to each other. The test cases wth the RLTH value larger than a threshol, enote by rrelevancy threshol, are selecte an the other test cases are fltere out. () Mn-Reunancy The output from the frst technque often contans test cases whch are relevant but reunant an mrmr attempts to aress ths problem by removng those reunant test cases by usng the secon technque Mn-Reunancy. The mnmal reunancy (Mn-Reunancy) conton (Hanchuanpeng 005) can be ae to select mutually exclusve features usng Eq. (7) mn R( x) x x I ( x x, x j ) (7), j x The selecte test cases by the rrelevancy flter may be reunant. The secon technque of the propose feature selecton technque flters out reunant test cases. The reunant test cases among the selecte subset of the frst technque are fltere out by the MI technque. Hence, the outcome of the propose feature selecton technque s the most relevant features wth mnmum reunancy. The reunancy flter uses the concept of MI technque whch s use to measure common nformaton between test cases. Usng Eq. (5) MI s calculate between par of test cases. Hgher MI value of two test cases means more common nformaton content between them. If MI between two tests cases larger than a threshol, enote by reunancy threshol or RTH, these two test cases are consere reunant test cases. If two test cases are reunant, the less relevant one wth lower relevance weght value, obtane from the frst stage s elmnate an the more relevant one wth hgher weght value s retane. Ths process s repeate untl no reunant test case s foun Output stage The selecte test cases by the rrelevancy flter are the fnal result of the propose feature selecton technque. The test cases from feature selecton are gven as the nput to genetc algorthm. 4. Test Case Prortzaton Usng Genetc Algorthm Genetc algorthms are use for prortzng the test cases wth mult objectves. The populaton s forme wth mult objectve test cases. The ftter soluton s foun to prortze the test cases. Only the test cases wth strong ftness values are selecte an prortze accorng to ther ftness value. The output from genetc algorthm gves the prortze set of test cases. These test cases are use for testng the target system. The basc unt n GA s the chromosome. To prortze the test cases, the steps to be followe are Chromosome generaton Chromosome selecton Chromosome crossover Chromosome mutaton Termnaton Here chromosome represents the test cases. 4.. Chromosome Generaton The mrmr feature selecton algorthm outputs the reuce test sute. In orer to obtan the esrable test case, the output s subject to genetc algorthm. The output from mrmr feature selecton algorthm forms the chromosome. 4.. Chromosome representaton In GA, the chromosomes are represente by the bnary alphabet {0, } an sometmes, epenng on the applcaton, ntegers or real numbers are use. Whatever may be the representaton, t can be use to form a soluton as a fnte length strng. A populaton T = {t, t, t 3.. t n} s forme from a set of chromosomes. Where T s the test sute contanng the test cases t, t, t 3..t n. The test cases whch forms the chromosome are represente as a strng of 4 tuple an s gven as (x,x,x 3,x 4 ). Here x means the memory usage value of the test case, x s executon tme, x 3 s coverage an x 4 s fault coverage Chromosome Selecton In the selecton stage of genetc algorthm, the test cases are chosen from a populaton for subsequent procreaton. The genetc algorthm solves the optmzaton problem by creatng a populaton of chromosomes, whch s a set of possble solutons for the problem. Frst of all, several nvual solutons are ranomly create n orer to form ntal populaton. The output from feature selecton algorthm s taken as the populaton nput for genetc algorthm. The populaton gves better solutons as the search evolves an t eventually converges. To form a new generaton, a proporton of the exstng populaton s chosen urng each consecutve generaton. A ftness base process s performe to select the ftter soluton. The ftter soluton s a measure of ftness functon. Weghte Sum Approach s use for formng ftness functon. The approach use here to solve the mult-objectve optmzaton problem s to allot a weght w to each normalze objectve functon as Eq (8) f (8) hence, the problem s change nto sngle objectve problem wth a scalar objectve functon as Eq. (9). mn z w f w f... w p f p (9) where, f s the normalze objectve functon, f an w. Here, the user s expecte to gve the weghts. Solvng a problem wth an objectve functon as n Equaton (9) for a gven weght vector w { w, w,..., wp} prouces a sngle soluton. Int J Av Engg Tech/Vol. VII/Issue I/Jan.-March,06/

4 But n real tme multple solutons are esre. Then the problem nees to be solve multple tmes wth verse weght combnatons. The man problem n ths metho s selectng a weght vector. In the propose work, n orer to calculate the ftness value for each chromosome n the populaton obtane from flockng algorthm, a number of ftness functons are use such as Eq. (0), () an (). ftness (0) E r where, E r s the vector whch s gven as, Er Er () where, E s the sum of error E r r n n ( j.e., () where, s the esre stance between the test cases an j, a s the actual stance between the test cases an j, an n s the number of test cases Ftness functon evaluaton by threshol factor Ftness evaluaton nvolves efnng an objectve or ftness functon aganst whch each test case s teste for sutablty for the envronment uner conseraton. As the genetc algorthm process precees the nvual ftness of the best test case ncreases as well as the total ftness of the test sute as a whole. The fnal ftness functon s gven by the followng Eq. (3). F ( S ) O( S ) P( S ) (3) where, O(S) s the objectve functon of genetc algorthm ftness, P(S) s a non negatve penalty functon, an s a penalty tme coeffcent, whch vares aaptvely urng the GA evoluton. The value of s selecte such that f the value s larger than the threshol value t whch s calculate as the average of all x, x, x 3 an x 4. Eltst selecton s use as a selecton metho to select the test cases for recombnaton. When usng genetc algorthm to solve complcate global optmzaton problems, only the eltst selecton genetc algorthm (ESGA) can converge to optmal global soluton. The man avantage of ths selecton s that t prouces best soluton n every generaton. Only the test cases wth strong ftness values are selecte for next generaton. In ths eltst selecton metho, the ftness value of all the test cases are calculate base on the ftness functon. Then the test cases are sorte by escenng ftness values. The sum S s calculate a ) whch s sum of all test cases ftness n populaton. The cut off value r s calculate as the average of all x, x, x 3 an x 4. When the ftness value of the test case s greater than r, the current test case s eclare as selecte, or else t s eclare as omtte. The ae penalty term s a functon of the egree of volaton of the constrants, n orer to prouce a graent towar val solutons. The penalty term for any soluton that breaches the constrants can be formulate by a quantty (s), whch measures the level of constrant volaton of soluton S. Thus, the penalty functon P epens on (S) s gven as Eq. (4). P( ( S)) A ( S) Bt (4) Where, A s a severty factor, whch efnes the slope of the penalty functon an Bt s a penalty threshol factor. So, by usng eltsm or eltst selecton, the best nvuals are retane n a generaton unchange n the next generaton Chromosome Crossover Cross over s the salent operator n GA. The two strngs partcpatng n the crossover operaton are known as parent strngs an the resultng strngs are known as chlren strngs. In crossover, two parent chromosomes are poole together to form new chromosomes calle offsprng. From the exstng chromosomes, the parents are chosen wth preference towars ftness so that offsprng s expecte to appear goo genes that make the parents ftter. The effect of cross over s usually benefcal. S {,,..., } Assume, s s sn an S { s, s,..., sn} be the two chromosomes. A ranom number s selecte from the ntegers 0 r n. S 3 an S 4 are the offsprng of crossover S S {, } an S, where 3 s f r s S S4 { s f r, s S}. Here n case of test case optmzaton t s T = {x, x, x 3, x 4 } an T = {x, x, x 3, x 4 }. By applyng the crossover operator, the genes of goo chromosomes are expecte to emerge more frequently n the populaton fnally leang to an excellent soluton. Many crossover operators exst n the GA. One pont crossover an two pont crossover are the most common ones aopte. In most crossover operators, two strngs are pcke from the pool at ranom an some porton of the strngs s exchange between the strngs. Here the unform crossover s use. The mxng rato use s 0.5, so approxmately half of the genes n the offsprng wll come from test case an the other half wll come from test case. Below s a possble set of offsprng after unform crossover. Before crossover Chromosome Chromosome Crossover pont Feature Feature Feature 3 Feature 4 Feature 5 Feature 6 Feature 7 Feature 8 Int J Av Engg Tech/Vol. VII/Issue I/Jan.-March,06/

5 After crossover: (the resultng offsprng) Chromosome 3 Feature Feature Feature 7 Feature 8 Chromosome 4 Feature 5 Feature 6 Feature 3 Feature 4 Each test case has 4 features. In case of unform cross over, snce the mxng rato use s 0.5, the frst two features from the frst test case s combne wth the last two feature of the secon test case to form the frst offsprng. Smlarly, the last two features from the frst test case s combne wth the frst two feature of the secon test case to form the secon offsprng. Thus approxmately half of the genes n the offsprng wll come from test case an the other half wll come from test case Chromosome Mutaton In mutaton, the characterstcs of chromosomes are change ranomly, thereby changng the structure of a chromosome. Normally, the mutaton s apple at the gene level. The mutaton operator s use to ntrouce change nto the chromosome populaton an t s apple to each new structure nvually. When the bts are beng cope from the current strng to the new strng, there s probablty that each bt may become mutate. A gven mutaton nvolves ranomly alterng each gene wth a small probablty calle mutaton probablty P m. Wth ths probablty, a ranom real value s generate whch s use to make a ranom change n the m-th element selecte ranomly of the chromosome. If ranom number s less than the mutaton probablty, then the bt s nverte. Ths s explane below usng an example. Here the test sute contans 6 test cases from T to T6 an the mutaton probablty P m = Test sute T T T3 T4 T5 T6 T7 T8 T9 T0 T T T3 T4 T5 T6 Mutaton probablty P m = The nvual test case ranom values are gven n Table. Table. Ranom values for test cases. Test cases Ranom Values R(T) R(T) 0.36 R(T3) R(T4) 0.00 R(T5) 0.67 R(T6) R(T7) 0.34 R(T8) 0.47 R(T9) R(T0) R(T) R(T) 0.87 R(T3) R(T4) 0.00 R(T5) R(T6) Here R(T4), R(T9) an R(T4) are havng P m the mutaton probablty value less than the specfe value So, they are omtte for the next generaton. So, the next generaton test sute contans T T T3 T5 T6 T7 T8 T0 T T T3 T5 T6 Wth ths a bt of versty to the populaton by scatterng the occasonal ponts s ntrouce resultng n better optma. In ths the weak nvual that wll never be selecte for further operatons. In mutaton urng the local search, a pont s create n the neghborhoo of the current pont aroun the current soluton. The man am of mutaton s to mantan versty n the populaton. Mutaton as new nformaton n a ranom way to the genetc search process an mutaton may cause the chromosomes of nvuals to be fferent from those of ther parent nvuals. The mutaton rate s usually less than % Termnaton Termnaton s the crteron use by the genetc algorthm to make a ecson about whether to contnue the process or to stop the process. Havng selecte the ntal populaton at ranom, the ftness functon valates the ftness of the chromosomes an selects those wth hgher potental for the proucton of new offsprng. After the applcaton of genetc operators, a new populaton s create from the current populaton. These operators are enhancng the present populaton. The whole process represents the completon of a sngle cycle by genetc algorthm. After many generatons of selecton for the ftter chromosomes, the resultant populaton s consere as ftter than the orgnal. Untl the stoppng crteron s met the steps are repeate. Here the number of generatons s use as a stoppng crteron. Fnally the genetc algorthm outputs the prortze test cases. 5. Evaluaton The esgne test case prortzaton system s teste wth 0 fferent projects. The samples of results Int J Av Engg Tech/Vol. VII/Issue I/Jan.-March,06/

6 obtane from two projects are use for performance analyss an s scusse here. In APFD metrc[7] the test cases are orere base on the test optmzaton nces an a test sutes performance are analyze. The effcacy of the orerng of the test sute s evaluate n orer to measure the performance of the optmzaton metho use n ths paper. Effcacy s measure by the rate of faults etecte. The followng metrcs s utlze to compute the level of effcacy. 5.. Average Percentage of Faults Detecte (APFD) By usng the weghte average of the number of faults entfe urng the executon of the test sute, the APFD s compute. Let T be the test sute uner evaluaton, F s the number of faults contane n the program uner test P, n s the total number of test cases, an reveal (, T ) s the poston of the frst test nt, whch reveals the fault. The formula for calculatng the APFD metrc s gven below. F reveal (, T ) APFD ( T, P ) - nf n In project, the number of test cases n=0 an the number of faults f=6. Ths can be represente n the followng Table, Table The Faults Detecte by the Test Sutes n Project Test Cases T T T3 T4 T5 T6 T7 T8 T9 T0 Faults F X X X X F X X X F3 X X X X F4 X X X F5 X X X F6 X X X Here the number of test cases s 0,.e., T, T, T3, T 4, T5, T6, T7, T8, T9. T0, an the number of faults occur urng the regresson testng s 6,.e., F, F, F3, F4, F5, F6.The optmze test suts wth test sequence T 0, T5, T9, T, T6, T, T 4, T3, T7, T8, then the APFD metrc after prortzaton s (647) Apf( T, - 0*6 *0 = 0.7 The APFD metrc before prortzaton s (5455) Apf( T, - 0*6 *0 =.68 For project the APFD metrc s calculate as follows. Number of test cases n=6 an the number of faults f=5. Ths can be represente n followng Table. Table The Faults Detecte by the Test Sutes n Project Test cases T T T3 T4 T5 T6 Faults F X X F X X F3 X X F4 X X F5 X the number of test cases s 6,.e., T, T, T 3, T 4, T 5, T 6, an the number of faults occur urng the regresson testng s 5,.e., F, F, F3, F 4, F5. The optmze test suts wth test sequencet 6, T, T 3, T 4, T 5, T, then the APFD metrc after prortzaton s ( 4 ) Apf( T, - 5*6 APFD metrc before prortzaton s ( 4 5) Apf( T, - 5*6 *6 *6 = = Fgure APFD Metrc for Both Project an Project Int J Av Engg Tech/Vol. VII/Issue I/Jan.-March,06/

7 From the Tables, an Fgure, t s observe that the optmze test cases entfy the faults at an early stage. The APFD measure of prortze test cases are hgher than the non prortze orer for both projects. In Tables an, the Fault entfe urng each test cases s lste. In Table, test case 0 can entfy more number of faults when compare to others. Thus Test case 5 wll be frst execute. From Tables an, t s observe that the propose metho entfes the severe fault n the early stage. 6. CONCLUSION Snce the sngle objectve test case system cannot solve complex problems base on the results obtane usng tratonal but latest test case prortzaton for regresson testng, the usage of mult objectve test case prortzaton system usng an unfe framework nvolvng mrmr Feature Selecton an Genetc Algorthm as one n the unfe framework valates the effcency an effcacy of the testng system by reucng the executon tme, memory usage an by ncreasng the fault etecton rate an coverage. REFERENCES. IEEE stanar glossary of software engneerng termnology, IEEE St , Aenlso a Slva Smao, Rorgo Fernanes e Mello an Lucano Jose Senger, A Technque to Reuce the Test Case Sutes for Regresson Testng Base on a Self-Organzng Neural Network Archtecture, COMPSAC 06, Proceengs of the 30th Annual Internatonal Computer Software an Applcatons Conference, Vol. 0, pp , AlrezaEnsan, EbrahmBagher, Mohsen Asa, DraganGasevc an YevgenBletsky, Goal-Orente Test Case Selecton an Prortzaton for Prouct Lne Feature Moels, pp. 9-98, Anrews, S. An Investgaton nto Mutaton Operators for Partcle Swarm Optmzaton, In Proc. Congr. Evol. Compt., pp , Arvner Kaur an Dvya Bhatt, Hybr Partcle Swarm Optmzaton for Regresson Testng, Internatonal Journal on Computer Scence an Engneerng, Vol. 3 No. 5, Bates, S. an Horwtz, S. Incremental Program Testng Usng Program Depenence Graphs, In Conference Recor of the Twenteth ACM SIGPLAN-SIGACT Symposum on Prncples of Programmng Languages, Charleston, South Carolna, ACM press, pp , Benot Baury, Franck Fleurey, Jean-Marc Jezequel an Yves Le Traon, Automatc Test Cases Optmzaton usng a Bacterologcal Aaptaton Moel: Applcaton to.net Components, 7th IEEE Internatonal Conference on Automate Software Engneerng, ASE Bergmann, K.P., Scheler, R. an Jacob, C. Cryptanalyss usng Genetc Algorthms, In: Proceengs of the 0th Annual Conference on Genetc an Evolutonary Computaton, GECCO 08, ACM New York, NY, USA, pp , Llly Raamesh an Uma, G.V. An Effcent Reucton Metho for Test Cases, Internatonal Journal of Engneerng Scence an Technology, Vol., No., pp , Llly Raamesh an Uma, G.V. Knowlege Mnng of Test Case System, Internatonal Journal on Computer Scence an Engneerng, Vol., No., pp , 009. Llly Raamesh an Uma, G.V. Relable Mnng of Automatcally Generate Test Cases from Software Requrements Specfcaton, IJCSI Internatonal Journal of Computer Scence Issues, Vol. 7, No., No. 3, 00.. Llly Raamesh an Uma, G.V. A Profcent Test Case Optmzaton System Base on Brs Flockng Algorthm an GA, European Journal of Scentfc Research, Vol. 84, No.3, pp S Elbaum, A Malshevsky, G Rothermel, 00, Incorporatng varyng test costs an fault severtes nto test case prortzaton Proceengs of the 3r Internatonal Conference on Software Engneerng. 4. Hanchuan Peng, Fuhu Long, an Chrs Dng, 005, "Feature selecton base on mutual nformaton: crtera of max-epenency, max-relevance, an mnreunancy", IEEE Transactons on Pattern Analyss an MachneIntellgence,Vol. 7, No. 8, pp C Emmanouls, A Hunter, J MacIntyre, 000, Amultobjectve evolutonary settng for feature selecton an a commonalty-base crossover operator Evolutonary Computaton, 000. Proceengs of the 000 Congress on, Changbng L, Changxu Cao, Ynguo L, Ybn Yu, 007. Hybr of genetc algorthm an partcle swarm optmzaton for multcast QoS routng. Proceengs of IEEE nternatonal conference on control an automaton, pp: Elbaum, S., Malshvesky, A.G., Rothermel, G., 00, Test case prortzaton: a famly of emprcal stues. IEEE Transactons on Software Engneerng 8 (), Gregg Rothermel, Rolan H. Untch, Chentun Chu an Mary Jean Harrol, 00, Prortzng Test Cases for Regresson Testng, IEEE Transactons on software Engneerng, VOL. 7 NO Zheng L, Mark Harman, an Robert M. Herons, 007, Search algorthm for Regresson Test Case Prortzaton, IEEE Transactons on Software Engneerng, Vol. 33, No.4. [ 0. S Elbaum, AG Malshevsky, G Rothermel,00, Test case prortzaton: A famly of emprcal stues Software Engneerng, IEEE Transactons on 8 (), S Elbaum, S Karre, G Rothermel, 003, Improvng web applcaton testng wth user sesson ata, Proceengs of the 5th Internatonal Conference on Software Engneerng, Yang, J. an Honavar, V., 998, Feature Subset Selecton Usng a Genetc Algorthm, IEEE Intellgent Systems, Vol. 3, No., pp Raymer, M.L., Punch, W.F., Gooman, E.D., Kuhn, L.A., an Jan, A.K., 000, Dmensonalty reucton usng genetc algorthms, IEEE Transactons on Evolutonary Computaton, Vol. 4, No., pp64-7. Int J Av Engg Tech/Vol. VII/Issue I/Jan.-March,06/