Michael, C., McGraw, G., Schatz, M., Walton C. Genetic Algorithms for Dynamic Test Data Generation. 12h IEEE International Conference on Automated Software Engineering (ASE'97), Tahoe NV, November 1997.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....to find test cases that result in different output, we must first figure out how to reach the syntactic changes to the program. This subproblem is itself undecidable, but incomplete solutions have been proposed for it using search techniques such as simulated annealing [6] or genetic algorithms [3] [5] these techniques use fitness functions to generate test inputs that reach particular parts of a program. Symbolic execution and constraint solving is also a possible approach. Once the syntactic changes in the program have been reached, it is also necessary for a difference in state to ....

C. C. Michael, G. E. McGraw, M. A. Schatz, and C. C. Walton. Genetic algorithms for dynamic test data generation. Technical Report RSTR-003-97-11, RST Corporation, Sterling, VA, May 1997.

.... [1, 3, 6, 15, 19] Goal oriented test data generators select inputs to execute the selected goal, such as a statement, irrespective of the path taken (e.g. 7, 16] Intelligent test data generators often rely on sophisticated analyses of the code, to guide the search for new test data (e.g. [2, 17, 21]) The weaknesses of these techniques, however, have inhibited their widespread use for generation of test data. A random test data generator may create many test data; however, because information about the test requirement is not incorporated into the generation process, the test data generator ....

....node 5. Under the Jones et al. approach, t1 and t2 would be given the same low tness because neither test case executes the target or one of its siblings (in the control ow graph) the fact that t1 is closer to the target than t2 is not incorporated into the tness calculation. Michael et al. [17] present an approach that is an implementation of Korel s function minimization approach to test data generation [16] using a genetic algorithm. Korel applied gradient descent to nd test data that caused a branch function to take on a negative value; these branch functions are developed so ....

C. C. Michael, G. E. McGraw, M. A. Schatz, and C. C. Walton.. Genetic Algorithms for Dynamic Test Data Generations. Technical Report RSTR-003-97-11, May 1997. 18

....Laboratorio Nacional de Inform atica Avanzada (LANIA) 128] Leiden University, 234] Michigan State University, 67] Mitsubishi Electric Corp. 348] Mitsubishi Electric Research Laboratories, 347] National University of Singapore, 411] Naval Research Laboratory, 224] RST Corporation, [90, 113, 122] Ruhr Universit at Bochum, 49] Santa Fe Institute, 47, 360, 387, 388] Swiss Federal Institute of Technology (ETH) 218] Technische Universit at der Berlin, 211, 353, 354, 397] The University of Texas at Austin, 394] Tulane University, 52] Universidad de Granada, 183, 243, 244, 377] ....

....333, 337] M antykoski, Janne, 178, 184] Maouche, Salah, 249] Marks, Joe, 16] Mars, Phil, 96] Martin, Ralph R. 349, 350] Marvin, Nick, 269] Matila, Jukka, 333] Matou sek, Radomil, 327] Mayer, Helmut A. 265, 313] Mayley, G. 99] Mazer, Emmanuel, 417, 419] McGraw, Gary E. [90, 113, 122] McGregor, Douglas R. 390, 391, 392] Mergl, Attila K. 79] Michael, Christoph C. 90, 113, 122] Michalewicz, Zbigniew, 91] Middendorf, Martin, 250] Miglino, Orazio, 237] Miikkulainen, Risto, 393, 394] Miller, Brad L. 189, 217] Miller, Geo rey F. 159] Mills, Graham, 342, 343] ....

[Article contains additional citation context not shown here]

Christoph C. Michael, Gary E. McGraw, Michael A. Schatz, and Curtis C. Walton. Genetic algorithms for dynamic test data generation. Technical Report RSTR-003-97-11, RST Corporation, 1997. Key: ga97aCCMichael.

....Institute (ICSI) 310, 312, 313] Iowa State University, 337] Leiden University, 194] Michigan State University, 48] Mitsubishi Electric Corp. 303] Mitsubishi Electric Research Laboratories, 302] National University of Singapore, 366] Naval Research Laboratory, 184] RST Corporation, [68, 90, 94] Ruhr Universitat Bochum, 38] Santa Fe Institute, 168, 315, 342, 343] Swiss Federal Institute of Technology (ETH) 178] Technische Universitat der Berlin, 171, 308, 309, 352] The University of Texas at Austin, 349] Universidad de Granada, 142, 203, 204, 332] University of Bristol, 211] ....

....280, 288, 292, 294] Mantykoski, Janne, 137, 143] Maouche, Salah, 209] Marks, Joe, 15] Mars, Phil, 74] Martin, Ralph R. 304, 305] Marvin, Nick, 229] Matila, Jukka, 292] Matousek, Radomil, 286] Mayer, Helmut A. 225, 273] Mayley, G. 78] Mazer, Emmanuel, 372, 374] McGraw, Gary E. [68, 90, 94] McGregor, Douglas R. 345, 346, 347] Mergl, Attila K. 60] Michael, Christoph C. 68, 90, 94] Michalewicz, Zbigniew, 69] Middendorf, Martin, 210] Miglino, Orazio, 197] Miikkulainen, Risto, 348, 349] Miller, Brad L. 148, 177] Miller, Geoffrey F. 117] Mills, Graham, 297, 298] ....

[Article contains additional citation context not shown here]

Christoph C. Michael, Gary E. McGraw, Michael A. Schatz, and Curtis C. Walton. Genetic algorithms for dynamic test data generation. Technical Report RSTR-003-97-11, RST Corporation, 1997. Key: ga97aCCMichael.

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Michael, C., McGraw, G., Schatz, M., Walton C. Genetic Algorithms for Dynamic Test Data Generation. 12h IEEE International Conference on Automated Software Engineering (ASE'97), Tahoe NV, November 1997.

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