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1 ISSN , CODEN RUXUEW Journal of Software, Vol17, No8, August 2006, pp DOI: /os Tel/Fax: by Journal of Software All rights reserved Messy GA 1,2+, 1,2, 1,3, 1, 1 1 (,100080) 2 (,100049) 3 (,100080) An Automated Approach for Structural Test Data Generation Based on Messy GA XUE Yun-Zhi 1,2+, CHEN Wei 1,2, WANG Yong-Ji 1,3, ZHAO Chen 1, WANG Qing 1 1 (Laboratory for Internet Software Technologies, Institute of Software, The Chinese Academy of Sciences, Beiing , China) 2 (Graduate School, The Chinese Academy of Sciences, Beiing , China) 3 (Laboratory of Computer Science, Institute of Software, The Chinese Academy of Sciences, Beiing , China) + Corresponding author: Phn: , yunzhi@inteciscasaccn, Xue YZ, Chen W, Wang YJ, Zhao C, Wang Q An automated approach for structural test data generation based on Messy GA Journal of Software, 2006,17(8): Abstract: Structural testing is one of the basic approaches for identifying test cases Because of complexity of programming languages and variety of applications, an efficient approach to automated generation of structural test data is to breed search iteratively by profiling of program execution Based on Messy GA, an automated approach for generating such data is proposed in this paper by solving F(X), a test coverage function of test data set It utilizes Messy GA s prominent feature that can optimize complicated problems without prior knowledge about schema arrangement in chromosomes, so that it can improve concurrency level of searching and test coverage Compared with other approaches based on GA, the experimental results for several typical programs and real-world applications show that it can generate higher quality test data more efficiently, and can be applied to larger applications Key words: : structural test test data test case automatic generation genetic algorithm sizable chromosome Messy GA,,, Messy GA, X F(X), Messy GA F(X),,, Supported by the National Hi-Tech Research and Development Program of China under Grant No2004AA1Z2100 ( (863)) the Program of the Chinese Academy of Sciences for One Hundred Talents under Grant NoBCK35873 ( ) Received Accepted

2 : Messy GA 1689,, : Messy GA : TP311 : A [1 3], [4],,,,, [4],,, [5 7] [8,9], [10], [7] [8],, :,, [3,4,10 13],,, [4],,,, [3],, [13], [14],,, [15],, [4,13], [16],,, [17] (formal concept analysis), [18],,,,, [2],, F(X), F(X), F(X),,F(X), ( ), F(X), F(X), F(X), [2] F(X), Messy GA F(X),Messy GA F(X),, X,,,

3 1690 Journal of Software Vol17, No8, August 2006, 1 Messy GA 2 Messy GA, 3, 4 1 Messy GA Messy GA [16],, [19],Messy GA,,,, [20], 11,, Messy GA {(i 1,v 1 ) (i 2,v 2 ) (i k,v k ) (i n,v n )},,n,i k,v k,i k,,, {(1,0) (2,0) (4,0) (4,1)}, 1 0, 2 0, 3, 4 0 1, 00*0 00*1,,, [19], 1, : ( ), Messy GA, ( ) Messy GA, (cut operator) (splice operator) [19] : :,, :, 12 Messy GA : (1) µ, M, k, T, k P(0) (2) (primordial phase) P(0), (3) (uxtapositional phase) a) P(t), b) c) d), Messy GA :, ( ), ( [19] )

4 : Messy GA 1691,,, 2 Messy GA X, S, S(X), δ, Σ, Σ Σ=F(X,S,S(X),δ), S δ,,, Messy GA, [20], F(X,S,S(X),δ) TriType 3, 3, Java, Messy GA, ( ),, (, ), C C= V 1,V 2,,V k, :k V i (, ), I I= C 1,C 2,,C n = V 11,V 12,,V 1k, V 21,V 22,,V 2k,, V n1,v n2,,v nk,,n, V 1,V 2, V n 1 TriType : 23,78,34,5, 87,, 2, 46,456,345,40,, 34,76,320,83,94, Arg A, 23,78,34,5, 23 87, a ,456, :1), ( ),, [4] Chromosome 2 Chromosome 1 Chromosome 3, 2) Fig1 Individuals expression based on Messy GA 1 Messy GA, Arg B Arg C Test input, QUEST/Ada [21], 1

5 1692 Journal of Software Vol17, No8, August 2006, ( ) TRUE FALSE,,, -, 1 -,,, Table 1 A possible coverage table as test criteria is C-D coverage 1 -, No Type TRUE FALSE 1 Condition 2 Condition 3 Decision 4 Condition : -, c, d, m, TRUE a, FALSE b, f a+ b+ m f = 2 c+ d ( ),, [4,21], [4] 23,, ( ) 2 Fig2 Employ cut operator & splice operator to produce new individual for TriType 2 TriType,, :,, α I i = C i1,c i2,,c in = V i11,v i12,,v i1k, V i21,v i22,,v i2k,, V in1,v in2,,v ink

6 : Messy GA 1693 p,1 p k, p α β : α β = p,, α I i : V i11,v i12,,v i1p, V i21,v i22,,v i2p,, V in1,v in2,,v inp, β I i : V i1(p+1),v i1(p+2),,v i1k, V i2(p+1),v i2(p+2),,v i2k,, V in(p+1),v in(p+2),,v ink, α β, α β α β I, k m, p,, q, I, I, α β α β I = p,, I, i α β I = q, I, I i : V i11,v i12,,v i1p,v 1(q+1),,V 1m, V i21,v i22,,v i2p,v 2(q+1),,V 2m,, V in1,v in2,,v inp,v n(q+1),,v nm, I : V 11,V 12,,V 1q,V i1(p+1),,v i1k, V 21,V 22,,V 2q,V i2(p+1),,v i2k,, V n1,v n2,,v nq,v in(p+1),,v ink ( ), ( ),,,,, 24 : 1, A-op1 2, A-op2 A-op1,,,, C= V 1,V 2,,V k, p, 0989, C = V 1,V 2,,V p 0989,,V k A-op2, ( ), A-op1,,A-op2 I= V 11,V 12,,V 1k, V 21,V 22,,V 2k,, V n1,v n2,,v nk A-op2, I = V 11,V 12,,V 1k,V 1(k+1), V 21,V 22,,V 2k,V 2(k+1),, V n1,v n2,,v nk,v n(k+1) ( ),, :, ( ), ( ),, 25 Messy GA, Messy GA,, : (1), [22]

7 1694 Journal of Software Vol17, No8, August 2006 (2), (3), (4), (5), P (6) P, (3) (7), :, (8), SUB (9) SUB, (10) SUB A-op1, (11) A-op2, (12) P, (6),, ( ) ( ),,,, 3 31, :,, Web 3,, 100 : 100, 10, 1000, , 5, [4] 2( [4]) Table 2 Comparison of C-D coverage through data generated by various methods 2 C-D Algorithm Random (%) Simple GA (%) GADGET [4] (%) Messy GA (%) Binary search Computing the median Number of days between two dates Insertion sort Euclidean greatest common denominator Angle classification Sample program Sample program Sample program ,, C-D, Messy GA, [4] GADGET, 32,, Messy GA TriType,

8 : Messy GA , 10, ~200, 10, 5, 5 3(a) - 3(a) : ( 80), 100% ( 140), ([80,140]), 100%, ( [10,130] ),, (40,50,60,70,80), 933%, (554,468,424,326,261),, 100, , 00001, 5, 3(b) - 3(b), ( 00009), ( 00014), ([00009,00013]), : ([00001,00013]),, C-D coverage (%) Fig3 (a) Population size 33 (a) Best solution generation C-D coverage (%) (b) Aberrance ratio (10 3 ) (b) (10 3 ) The influence of population size and aberrance ratio on performance of the algorithm 3 TriType, 3, 31, [4] 4 C-D coverage (%) Messy GA Simple GA GADGET Random Best solution generation Fig Generation For TriType, comparison of generation needed by Messy GA, GADGET, simple GA and Random TriType,Messy GA,GADGET, 1000

9 1696 Journal of Software Vol17, No8, August , C-D :,,(434%) (867%),, 5, GADGET (933%),, Messy GA, 4 Messy GA,, Messy GA,,,, X,,,,,,,,, : 1), 2),,,, 3) :, References: [1] Jorgenson PC Software Testing: A Craftsman s Approach 2nd ed Beiing: Machine Press, (in Chinese) [2] Pargas RP, Harrold MJ Test data generation using genetic algorithms Journal of Software Testing, Verification and Reliability, 1999,9(4): [3] Korel B Automated software test data generation IEEE Trans on Software Engineering, 1990,16(8): [4] Michael CC, McGraw GE, Schatz MA Generating software test data by evolution IEEE Trans on Software Engineering, 2001,27(12): [5] Sy NT, Deville Y Consistency techniques for interprocedural test data generation ACM SIGSOFT Software Engineering Notes, 2003,28(5): [6] Chen T, Tse T, Zhouz Z Semi-Proving: An integrated method based on global symbolic evaluation and metamorphic testing ACM SIGSOFT Software Engineering Notes, 2002,27(4): [7] 10Offutt A, Jin Z, Pan J The dynamic domain reduction procedure for test data generation Software Practice and Experience, 1999, 29(2): [8] Zhang J, Xu C, Wang X Path-Oriented test data generation using symbolic execution and constraint solving techniques In: He JF, Yang FQ, eds Proc of the Int l Conf on Software Engineering and Formal Methods Beiing: IEEE Computer Society Press, [9] DeMillo RA, Offutt AJ Constraint-Based automatic test data generation IEEE Trans on Software Engineering, 1991,17(12):

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