A Novel Approach for an Early Test Case Generation using Genetic Algorithm and Dominance Concept based on Use cases

Transcription

1 Alekhya Varkut et al, / (IJCSIT) Internatonal Journal of Computer Scence and Informaton Technologes, Vol. 3 (3), 2012, A Novel Approach for an Early Test Case Generaton usng Genetc Algorthm and Domnance Concept based on Use cases Alekhya Varkut,Deepka Puvvula Department of Computer Scence and Engneerng, GITAM Unversty, Vsakhapatnam, A.P., Inda. Abstract Ths paper presents an ntegrated approach for generatng test cases usng Genetc Algorthm and domnance relaton wth ftness functon. We nvestgated early generaton of test cases usng use cases and scenaros. In the process of generatng test cases frst we constructed control flow graph based on that domnance tree s generated. Test cases are generated by applyng Genetc algorthm on domnance tree wth concepts of crossover and mutaton. Keywords Genetc Algorthms, Domnance, Usecase, Scenaros, Testng; I. INTRODUCTION Software testng s used to mprove the qualty and ncrease the relablty of software. Software testng s a complex, labor-ntensve, and tme consumng task that accounts for approxmately 50% of the cost of a software system development [1]. Test case generaton s the process of dentfyng nput that satsfes test data adequacy crteron. We used a GA-based technque and domnance concept wth a new ftness functon that reduces the test requrements and overcomes the exstng problems. Here we are consderng use cases and ther correspondng scenaros for generatng test cases. The goal of testng s to obtan several test cases to check that all the nformaton ncluded n the use cases have been successfully mplemented under the test. Test cases are optmzed by usng genetc algorthm wth domnance relaton concepts. A control-flow graph n whch the nodes represent actons (actvty, method executon) and the arcs ndcate the flow of control from one acton to another. Control flow graph conssts of start node and stop node where each node represents statement and anode wth two or more outgong arc s called branch node. In domnance tree, a node d domnates a node n f every path from the start node to n must go through d. Ths s wrtten as d dom n. A node x n a control flow graph domnates another node y (x dom y) f every path from r to y contans x. A domnance relaton s sad to be strct f x y. II. ANALYSIS There has been much prevous work n automatcally generatng test data. Most commonly encountered are random test-data generaton, symbolc test-data generaton, dynamc test-data generaton and test data generaton based on GA. Random test data generaton conssts of generatng nputs at random untl useful nputs are found [2,3]. The problem wth ths approach s clear wth complex programs or complex adequacy crtera, an adequate test nput may have to satsfy very specfc requrements. Symbolc test data generaton conssts of assgnng symbolc values to varables to create an abstract, mathematcal characterzaton of the program s functonalty. Wth ths approach, test-data generaton can be reduced to a problem of solvng an algebrac expresson. Many test-data generaton methods that use symbolc executon to fnd nputs that satsfy a test requrement proposed [4,5,6,7]. A number of problems arse n symbolc executon lke ndefnte loops, where the number of teratons depends on non constant expresson and the ndex of array where data s referenced ndrectly. Dynamc test-data generaton s based on the dea that f some desred test requrement s not satsfed, data collected durng executon can be used to determne whch tests come closest to satsfyng the requrement. Two lmtatons are commonly found n dynamc test-data generaton systems. Frst many systems make t dffcult to generate tests for large programs because they work only on smplfed programmng languages. Second, many systems use gradent descent technques to perform functon mnmzaton. Several search based test data generaton technques have been developed [8,9,10].These technques are focused on fndng test data to satsfy a number of control flow testng and data flow testng. Genetc Algorthm has been the most wdely employed n search based optmzaton technques. Test data generaton methods based on genetc algorthms have many problems due to use of ftness functon. To solve these problems we proposed a new GA-based technque and apply the concepts of domnance wth a new ftness functon that reduces the test requrements and overcomes the problems of the prevous GA-based test-data generaton methods. III. PROPOSED METHODOLOGY In the proposed algorthm, the followng concepts were ntroduced for an early test case generaton and test case optmzaton 4218

2 Alekhya Varkut et al, / (IJCSIT) Internatonal Journal of Computer Scence and Informaton Technologes, Vol. 3 (3), 2012, A. Use Cases: A use case s a specfcaton of actons, ncludng varants, whch a system (or other entty) can perform, nteractng wth an actor of the system. A use case s a specfc way of usng the system by performng some part of the functonalty [11]. B. Scenaros: Scenaros represent dfferent run types and operatons of a use case. Each use case there wll be one or more scenaros. C. Control Flow Graph: A control-flow graph (n short flow graph) s a drected graph. The nodes n the graph represent actons (actvty, method executon) and the arcs ndcate the flow of control from one acton to another. A flow graph has two specal nodes: the start node and the stop node. The stop node has no outgong arcs and every node n a flow graph les on some path from the start node to the stop node (the oneentry one-ext property). A node wth one outgong arc s called an acton node. A node wth two or more outgong arcs s called a branch node. A drected graph or dgraph G = (V, E) conssts of a set V of nodes or vertces, where each node represents a statement, and a set E of drected edges or arcs, where a drected edge e = (n, m) s an ordered par of adjacent nodes, called Tal and Head of e, respectvely. For a node n n V, n degree (n) s the number of arcs enterng and out degree (n) the number of arcs leavng t. Fg 1.a represents control flow graph G for ATM applcaton based on use case and scenaros. D. Domnance: Let G = (V, E) be a dgraph wth two dstngushed nodes n0 and nk. A node n domnates a node m f every path P from the entry node n0 to m contans n. Several algorthms are gven n the lterature to fnd the domnator nodes n a dgraph[12]. By applyng the domnance relatons between the nodes of a dgraph G, we can obtan a tree (whose nodes represent the dgraph nodes) rooted at n0. Ths tree s called the domnator tree Fg 1. (b) show domnance tree DT(G) for ATM applcaton based on Control Flow Graph. A (rooted) tree DT (G) = (V, E) s a dgraph n whch one dstngushed node n0, called the root, s the Head of no arcs; every node n except the root n0 s a Head of just one arc and there exsts a (unque) path (domnance path) from the root n0 to each node n; we denote ths path by dom(n). Tree nodes of out degree zero are called leaves. Here nput varables are the functonalty of an applcaton. E. Prncples of Genetc Algorthms: The basc concepts of GAs are commonly appled to a varety of problems nvolvng search and optmzaton. GAs search methods are rooted n the mechansms of evoluton and natural genetcs. GAs generates a sequence of populatons by usng a selecton mechansm, and use crossover and mutaton as search mechansms. The prncple behnd GAs s that they create and mantan a populaton of ndvduals represented by chromosomes (essentally a character strng analogous to the chromosomes appearng n DNA). These chromosomes are typcally encoded solutons to a problem. The chromosomes then undergo a process of evoluton accordng to rules of selecton, mutaton and reproducton. Each ndvdual n the envronment (represented by a chromosome) receves a measure of ts ftness n the envronment. Reproducton selects ndvduals wth hgh ftness values n the populaton, and through crossover and mutaton of such ndvduals, a new populaton s derved n whch ndvduals may be even better ftted to ther envronment. The process of crossover nvolves two chromosomes swappng chunks of data (genetc nformaton) and s analogous to the process of sexual reproducton. Mutaton ntroduces slght changes nto a small proporton of the populaton and s representatve of an evolutonary step. The structure of a smple GA s gven below. Smple Genetc Algorthm () { Intalze populaton; Evaluate populaton; Whle termnaton crteron not reached { Select solutons for next populaton; Perform crossover and mutaton; Evaluate populaton; } } The algorthm wll terate untl the populaton has evolved to form a soluton to the problem, or untl a maxmum number of teratons have occurred. F. GA-based Test-Case Generaton: The proposed GA for test case generaton, developed a new GA-based technque and apply the concepts of the domnance relatons between nodes of the control flow graph wth new ftness functon. The algorthm searches for test cases that satsfy the all-statements crteron. 1) Representaton: The proposed GA uses a bnary vector as a chromosome to represent values of the program nput varables. The length of the vector depends on the requred precson and the doman length for each nput varable. Suppose we wsh to generate test cases for a flow graph of k nput varables x1,, xk where each varable x can take values from a doman D = [a, b]. 2) Intal populaton: Each chromosome (as a test case) s represented by a bnary strng of length m. We randomly generate pop_sze m-bt strngs to represent the ntal populaton, where pop_sze s the populaton sze. The approprate value of pop_sze s expermentally determned. Each chromosome s converted to k decmal numbers representng values of k nput varables x1 xk. 3) Evaluaton functon: The algorthm uses a new evaluaton (ftness) functon to evaluate the generated test data. Ths new ftness functon depends on the concepts of the domnance relatons between nodes of the control flow graph. The algorthm uses ths new ftness functon to evaluate each test case by executng wth t as nput, and recordng the traversed nodes n the program that are covered by ths test case. We denote to the set of traversed nodes by exepath. Also, t fnds the domnance path dom(n) of the target node n. The ftness functon s the rato of the number of covered nodes of the domnance path of the 4219

3 Alekhya Varkut et al, / (IJCSIT) Internatonal Journal of Computer Scence and Informaton Technologes, Vol. 3 (3), 2012, target node to the total number of nodes of the domnance path of the target node. The ftness value ft(v) for each chromosome v ( = 1,, pop_sze) s calculated as follows: 1. Fnd exepath: the set of the traversed nodes n the program that are covered by a test case. 2. Fnd dom(n): domnance path of the target node n (the set of domnator nodes from the entry of the domnator tree to n). 3. Determne dom ( n ) exepath : uncovered nodes of the domnance path (the dfference between the domnance path and the traversed nodes). 4. Determne dom ( n ) exepath ' : covered nodes of the domnance path (the complement set of the dfference set between the domnance path and the traversed nodes). 5. Calculate dom ( n ) exepath )' : number of covered nodes of the domnance path (cardnalty of the complement set). 6. Calculate dom ( n ) : number of nodes of the domnance path of the target node n (cardnalty of the domnance set). Then, ( dom ( n ) exepath )' ft ( v ) dom ( n ) The ftness value s the only feedback from the problem for the GA. A test case that s represented by the chromosome v s optmal f ts ftness value ft ( ) 1 v 4) Selecton: After computng the ftness of each test case n the current populaton, the algorthm selects test cases from all the members of the current populaton that wll be parents of the new populaton. In the selecton process, the GA uses the roulette wheel method. For the selecton of a new populaton wth respect to the probablty dstrbuton based on ftness values, a roulette wheel wth slots szed accordng to ftness s used. Such roulette wheel s constructed as follows: 1. Calculate the ftness value ft ( v ) for each chromosome (=1,...pop_sze). 2. Fnd the total ftness of the populaton pop _ sze F ft ( ). 1 v 3. Calculate the relatve ftness value rft for each chromosome ft ( v ) rft ( v ). F 4. Calculate the cumulatve ftness value cft for each chromosome rft ( v ) cft ( v ) cft ( v 1 ) rft ( v ) The selecton process s based on spnnng the roulette wheel pop_sze tmes; each tme we select a sngle chromosome for a new populaton n the followng way: Generate a random (float) number r from the range [0..1]. If r cft ( v ) then select the frst chromosome otherwse select the -th chromosome ( 2 pop _ sze ) such that v cft ( v ) r cft ( v 1 ). 5) Recombnaton: In the recombnaton phase, we use two operators, crossover and mutaton, whch are the key to the power of GAs. These operators create new ndvduals from the selected parents to form a new populaton. : It operates at the ndvdual level. Durng crossover, two parents (chromosomes) exchange substrng nformaton (genetc materal) at a random poston n the chromosome to produce two new strngs (offsprng). The objectve here s to create better populaton over tme by combnng materal from pars of (ftter) members from the parent populaton. occurs accordng to a crossover probablty. The probablty of crossover PXOVER gves us the expected number PXOVER pop _ sze of chromosomes, whch undergo the crossover operaton. We proceed n the followng way: For each chromosome n the parent populaton: Generate a random (float) number r from the range [0..1]; If r < PXOVER then select gven chromosome for crossover. Now we mate selected chromosomes randomly: For each par of coupled chromosomes we generate a random nteger number pos from the range [1..m-1] (m s the number of bts n a chromosome). The number pos ndcate the poston of the crossng pont. Two chromosomes (b1 bposbpos+1 bm) and (c1 cposcpos+1 cm) are replaced by a par of ther offsprng (b1 bposcpos+1 cm) and (c1 cposbpos+1 bm). Mutaton: It s performed on a bt-by-bt bass. Mutaton always operates after the crossover operator, and flps each bt wth the pre-determned probablty. The probablty of mutaton PMUTATION, gves us the expected number of mutated bts PMUTATION m pop_ sze. Every bt (n all chromosomes n the whole populaton) has an equal chance to undergo mutaton (.e., change from 0 to 1 or vce versa). So we proceed n the followng way: For each chromosome n the current (.e., after crossover) populaton and for each bt wthn the chromosome: Generate a random (float) number r from the range [0..1]; If r < PMUTATION then mutate the bt. In the tradtonal GA approach the populaton would evolve untl one ndvdual from the whole set whch represents the soluton s found. In our case, ths condton would correspond to fndng groups of data tems achevng the test requrements (.e., coverng the set of leaves of the domnator tree) of the tested program. We let the populaton evolves untl a combned subset of the populaton acheves the desred test requrement. The evoluton stops when a set of ndvduals has traversed the domnance path of the test requrement and ts ftness value ft(v) = 1. The soluton s ths set. v 4220

4 Alekhya Varkut et al, / (IJCSIT) Internatonal Journal of Computer Scence and Informaton Technologes, Vol. 3 (3), 2012, ) Eltst: The eltst functon enhances the current populaton by storng the best member of the prevous populaton. If the best member of the current populaton s worse than the best member of the prevous populaton t exchanges them, and the best member of the current populaton would replace the worst member of the current populaton. After that, t stores the best member of the current populaton. 7) Algorthm: /* A GA algorthm to generate test cases */ Input: The use cases to be tested. Number of nput varable; Doman and precson; Populaton sze; Maxmum no. Of generaton (Max_Gen); Probablty of crossover; Probablty of mutaton; Output: Set of test cases and set of nodes covered by each test case; Lst of uncovered nodes,(f any); Begn Step0:Setup 1. Classfy the use cases and scenaros 2. Buld the control flow graph based on scenaros of each use case 3. Buld the domnator tree DT 4. Fnd the set of leaves L of the domnator tree. Step1: Intalzaton Intalze the score board to zero; nrun0; Set of test cases ncases0; Step2: Generate test cases For each uncovered node and selected before n the set of nodes to be tested Begn nrunnrun + 1; Create Intal_populaton Current _populaton Intal_populaton No_of_Generaton0; For each member of current populaton do Begn Convert the current chromosome to the correspondng set of decmal values Execute wth the data set as nput; If(the current node s covered) then Mark the current node as covered; End If End For Keep the best member of the current populaton; Whle (current node s not covered and no_of_generatons Max_Gen) do Begn Select set of parents of new populaton from members of current populaton usng roulette wheel method; Create New_populaton usng crossover and mutaton operators; Current_populaton New_populaton; For each member of Current_populaton do Begn Convert current chromosome to the correspondng set of decmal values; Execute wth the data set as nput; Evaluate the current test case; If(current node are covered) then Mark the current node as covered; End f; End for; Eltst functon: If the best member of the current populaton s worse than the best member of the prevous populaton then exchange them, and the best member of the current populaton would be replace the worst member of the populaton. Increment No_of_Generaton; End Whle; If (The current node s covered) Then ncasencases+1; Add ths test case to set of test cases for p; Update the score board; Check all uncovered nodes by ths test case. End If End For; Step3: Produce Output Return no of test cases for use cases and set of nodes covered by test case; Report on uncovered node, If say; IV. CASE STUDY We consder an ATM applcaton and appled the algorthm. Intally, we consder use case and scenaros of ATM applcaton as shown n Table 1. Based on use case and scenaros we construct the control flow graph. Based on control flow graph we construct domnance tree and apply genetc algorthm to generate test cases. The uses of GA parameters were as follows: Maxmum Generaton=100, Pxover=0.8, Pmutaton=0.15. Table 1:Usecases and Scenaros of ATM Applcaton. Usecases Insert card valdaton Language selecton process ATM valdaton Scenaros Wrongly nserted card card s not vald vald card language selected cancel PIN Vald PIN Invald PIN and check thrce Cancel Selecton Wth draw Menu Transfer Valdaton Balance Enqury Wth draw collect cash not suffcent amount n ATM not Suffcent amount n account lmt exceed per day Transfer vald destnaton not suffcent amount n source account Balance Balance enqury Detals Mn statement cancel Transacton possble Slp Paper not avalable 4221

5 Alekhya Varkut et al, / (IJCSIT) Internatonal Journal of Computer Scence and Informaton Technologes, Vol. 3 (3), 2012, Fg. 1. (a) Control Flow Graph G Fg 1 (b) Domnance tree DT(G) Here X represent Insert Card Valdaton. Y represent language selecton Z represent ATM Pn Valdaton P represents Selecton Menu Valdaton Q represent Wthdraw opton R represent Transfer S represent Destnaton Valdaton T Balance Detals U Transacton slp Input varables: X, Y, Z, P, Q, R, S, T, U. Doman: 0..3,0..1,0..2,0..3,0..3,0..1,0..1,0..2,0..1. Doman tree leaf nodes: Dom (7) =1, 2, 4, 7. Dom (8) =1, 2, 5, 8. Dom (37) =1, 2, 36, 37. Dom (10) =1, 2, 3,6,10. Dom (12) =1, 2, 3,6,9,11,12. Dom (14) =1, 2, 3,6,9,11,14. Dom (22) =1, 2,3,6,9,11,13,15,16,19,22. Dom(23)=1,2,3,6,9,11,13,15,16,19,23. Dom(24)=1,2,3,6,9,11,13,15,16,19,24. Dom(25)=1,2,3,6,9,11,13,15,16,19,25. Dom(26)=1,2,3,6,9,11,13,15,17,20,26. Dom (28) =1, 2, 3, 6,9,11,13,15,17,20,27,28. Dom (29) = 1, 2, 3, 6,9,11,13,15,17,20,27,29. Dom (33) = 1, 2, 3, 6,9,11,13,15,18,21,30,33. Dom (34) = 1, 2, 3, 6,9,11,13,15,18,21,30,34. Dom(31)= 1,2,3, 6,9,11,13,15,18,21,31. Dom(32)= 1,2,3, 6,9,11,13,15,18,21,32. Dom (35) = 1, 2, 3, 6,9,11,13,15,

6 Alekhya Varkut et al, / (IJCSIT) Internatonal Journal of Computer Scence and Informaton Technologes, Vol. 3 (3), 2012, A. Results: The fnal report of the result s gven n Table-2. The test requrement to be covered s shown and the number of generatons n whch the test requrement s covered s gven. And also the status n whch test requrement s covered or not s specfed and fnally the test cases are generated. Here nput varables represent functonalty of an applcaton. 1) Fnal Report: Total number of requrement: 18 Number of covered requrement: 18 The Covered Requrement are:7,8,37,10,12,14,22,23,24,25,26,28,29,33,34,31,32,35. Number of uncovered requrement s: 0 Number of Runs: 18. Total Number of generaton: 25. Maxmum generaton: 100 Input Varables : X,Y,Z,P,Q,R,S,T,U. For example, n Table-2, the test requrement to be covered.e., dom(7) represents the leaf node of domnance tree. The number of generatons s performed untl t covers all nodes of dom(7). Based on the doman, we provde values to the nput varables (X, Y) that satsfes dom(7) and fnally that value becomes the test cases. V. CONCLUSION Ths paper explans a new GA-based technque and apples the concepts of domnance wth a new ftness functon that reduces the test requrements. In order to perform early stage test case generaton we consder use cases and scenaros. Early stage test case generaton help n fndng fault detecton and relablty of the software. Test Requrement to be covered Number of generatons Covered (Yes-Y/No-N) Table-2 Fnal Results Test Case X Y Z P Q R S T U 7 1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y VI. REFERENCES [1] B. Bezer (1990). Software Testng Technques. Second Edton,Van Nostrand Renhold, New york [2] H.D.Mlls,M.D.Dyer, and R.C.Lnger (1987).Clean room Software Engneerng.IEEE Software 4(5), pp [3] J. M. Voas, L. Morell, and K. W. Mller (1991). Predctng where Faults Can Hde From Testng. IEEE, 8(2), pp Informatca 34 (2010) A.S. Ghduk et al. [4] J. C. Kng (1976). Symbolc Executon and Program Testng. Communcatons of the ACM, 19 (7), pp [5] W. E. Howden (1977). Symbolc Testng and the DISSECT Symbolc Evaluaton System. IEEE Transactons on Software Engneerng, 3(4), pp [6] Mn Pe, E. D. Goodman, Zongy Gao, and Kaxang Zhong (1994). Automated Software Test Data Generaton Usng a Genetc Algorthm" Techncal Report GARAGe of Mchgan State Unversty. [7] M. Roper, I. Maclean, A. Brooks, J. Mller, and M. Wood (1995). Genetc Algorthms and the Automatc Generaton of Test Data. Techncal report RR/95/195[EFoCS-19-95]. [8] R. P. Pargas, M. J. Harrold, R. R. Peck (1999 ). Test Data Generaton Usng Genetc Algorthms Journal of Software Testng, Verfcatons, and Relablty, vol. 9, pp [9] Jn-Cherng Ln and Pu-Ln Yeh (2000). Usng Genetc Algorthms for Test Case Generaton n Path Testng. Proceedngs of the 9th Asan Test Symposum (ATS'00). [10] M. R. Grgs (2005). Automatc Test Data Generaton for Data Flow Testng Usng a Genetc Algorthm. Journal of Unversal computer Scence, vol. 11, no. 5, pp [11] Mehmet Akst, Klaas van den Berg, & Pm van den Broek(1995) Use Cases n object orented software development AMIDST/WP2/N003/V02 [12]T. Lengauer and R. E. Trajan (1979). A Fast Algorthm for Fndng Domnators n a Flowgraph. ACM Transactons on programmng Languages and Systems, vol. 1, pp APPENDIX A part of the result applyng the system to test requrement of dom(10) Test requrement of Dom (10) = Populaton Sze=4 Maxmum generaton=100 Probablty=0.80 Mutaton Probablty=0.15 Input Varables: 3 Doman: 0..3,0..1,0..2 Generaton

7 Alekhya Varkut et al, / (IJCSIT) Internatonal Journal of Computer Scence and Informaton Technologes, Vol. 3 (3), 2012, Input Varable: X Y Z Indvdual1 =0, 0, 0 = Indvdual2 =2, 1, 1 = Indvdual3 =3, 0, 1= Indvdual4 =1, 1, 0 = Evaluaton of populaton: Traversed Path= Uncovered domnator path=10 Ftness Value= 0.4 Traversed Path= Uncovered domnator path= Ftness Value= 0.4 Traversed Path= Uncovered domnator path= Ftness Value= 0.4 Traversed Path= Uncovered domnator path= Ftness Value= 0.4 Generaton-2 Selecton Selecton s performed by Roulette Wheel depended on ftness Selected cases of Parents of New Populaton Indvdual1 =0, 0, 0 = Indvdual2 =2, 1, 1 = Indvdual3 =1, 1, 1= Indvdual4 =1, 1, 0 = AUTHORS BIOGRAPHY Alekhya Varkut s pursung M.Tech n Software Engneerng from GITAM UNIVERSITY, Vsakhapatnam, A.P., INDIA. Her research areas nclude Software Testng, Software Qualty Assurance, and Software Relablty. Deepka Puvvula s pursung M.Tech n Software Engneerng from GITAM UNIVERSITY, Vsakhapatnam, A.P., INDIA. Her research areas nclude Software Qualty Assurance, Software Relablty and Software Estmaton Technques poston Selecton of ndvdual Before Before Mutaton Indvdual before Mutaton Mutaton bt After Mutaton New Populaton Indvdual 1= 0, 1,0= Indvdual 2= 2, 1, 1= Indvdual 3= 1, 1, 1= Indvdual 4= 3, 0, 0= Evaluaton Of Populaton Indvdual 1 Traversed Path= Uncovered Path= - Ftness Value= 1.0 Indvdual 2 Traversed Path= Uncovered Path= Ftness Value= 0.4 Indvdual 3 Traversed Path= Uncovered Path= Ftness Value= 0.4 Indvdual 4 Traversed Path= Best Ftness s: 1.0 Average of ftness: 0.55 No of Generatons=2 The Test Requrement s Satsfed and Generated test case s 0, 1, 0 at Indvdual