Citations: Generating software test data by evolution - McGraw, Michael, Schatz (ResearchIndex)

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C.C. Michael, G. McGraw, and M.A. Schatz, "Generating Software Test Data by Evolution," IEEE Trans. Software Eng., vol. 27, no. 12, pp. 1085-1110, Dec. 2001.

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Property-Based Validation of Component Systems - DSoS (2002) (Correct)

....representatives of these techniques, namely genetic algorithms and simulated annealing, have gained recent attention in the field of software testing. They have been used to automate the generation of test data for a number of testing problems: instruction and branch testing [Jones et al. 1996, Michael et al. 2001, Pargas et al. 1999, Tracey 2000] determination of Worst Case Execution Time [Gross et al. 2000, Tracey 2000] exception testing [Tracey et al. 2000] conformance testing [Tracey et al. 1998] and robustness testing [Schultz et al. 1995] Whatever the testing problem, the search process involves ....

C.C. Michael, G. McGraw and M.A. Schatz, "Generating Software Test Data by Evolution", IEEE Transactions on Software Engineering, 27(12), pp. 1085-110, 2001.

An Empirical Study of Predicate Dependence Levels and Trends - David Binkley Mark (2003) (Correct)

....Slices are constructed using dependence analysis. The lower the dependence level, the smaller the slice, and consequently, the greater the saving in human analysis effort. Automated analysis can be expensive too. For example, consider the automated search for branch coverage adequate test data [9, 11, 14, 15]. Here, the potential savings from dependence analysis are dramatic; linear dependence level reductions cause exponential search space reductions. This paper introduces empirical program dependence level and trend analysis. The results of which have significant impact upon dependence sensitive ....

C. Michael, G. McGraw, and M. Schatz. Generating software test data by evolution. IEEE Transactions on Software Engineering, (12):1085--1110, Dec. 2001.

Dealing with Software Testing via Estimation of.. - Sagarna, Lozano.. (2003) (1 citation) (Correct)

....in the conditional statements during execution of the input represented by the individual. A set of individuals is associated to each branch. This set is used as seed population for the EDA when the branch is selected as objective. This notion of seeding has also been exploited in other works [17] [21] Thus, although the objective branch is xed, each individual is evaluated according to every other branch. If the branch is exercised, its state is marked as covered and the input is stored, otherwise if the tness of the individual is better than the worst individual in the set associated ....

....type, if any, associated to the input. In the T1 version of the programme [21] parameters are integers which for the experiments took values in the interval [ 16384; 16383] T2 [21] is the same as T1 with oating point parameters instead; the interval for each was [ 32768; 32768] Both T3 [17] and T4 [19] versions are distinct implementations with integer parameters for which the interval [ 512; 511] was chosen. It is important to note that di erent implementations of the same algorithm can lead to distinct code structures and thus, a ect the results of test case generation [9] In ....

[Article contains additional citation context not shown here]

G. McGraw, C. Michael, M. Schatz, Generating software test data by evolution, Technical Report RSTR018 -97-01, RST Corporation, Sterling, Virginia, USA, 1998.

A Post-Placement Side-Effect Removal Algorithm - Harman, Hu, Hierons, Munro.. (2002) (Correct)

....therefore needs converters to transform a side affecting program into a side effect free equivalent. In software testing, one of the hardest problems is the automated construction of systematic test data which satisfies some adequacy criterion, such as branch or statement coverage. Recent work [15, 18, 19, 20, 23, 26, 27] has concerned the application of evolutionary algorithms to the problem of searching for good quality test data. This work represents part of a more general move within the software engineering community towards consider1 ing search based techniques as solutions to hard problems where there are a ....

MICHAEL, C., MCGRAW, G., AND SCHATZ, M. Generating software test data by evolution. IEEE Transactions on Software Engineering, 12 (Dec. 2001), 1085--1110.

VADA: A Transformation-based System for Variable.. - Harman, Fox.. (2002) (1 citation) (Correct)

....can be loop carried (the dependence of x upon y) and involves control dependence (the dependence of x upon a) as well as data dependence (all other dependences in this example) Variable dependence information can be used to augment the effectiveness of evolutionary testing. Evolutionary testing [26, 31, 32, 33, 37] uses search based techniques to find good quality test data by searching the set of possible inputs. Test data quality is defined by a test adequacy criterion, which underpins the fitness function that drives the search implemented by the evolutionary algorithm. The computational effort required ....

MICHAEL, C., MCGRAW, G., AND SCHATZ, M. Generating software test data by evolution. IEEE Transactions on Software Engineering, 12 (Dec. 2001), 1085--1110.

An Indexed Bibliography of Genetic Algorithms - Papers Available.. - Alander (1999) (1 citation) (Correct)

....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]

Gary E. McGraw, Christoph C. Michael, and Michael A. Schatz. Generating software test data by evolution. Technical Report RSTR-018-97-01, RST Corporation, 1998. Key: ga98aGMcGraw.

An Indexed Bibliography of Genetic Algorithms - Papers Available.. - Alander (1998) (1 citation) (Correct)

....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]

Gary E. McGraw, Christoph C. Michael, and Michael A. Schatz. Generating software test data by evolution. Technical Report RSTR-018-97-01, RST Corporation, 1998. Key: ga98aGMcGraw.

Automated Software Test Data Generation for Complex Programs - Michael, McGraw (1998) (1 citation) Self-citation (Mcgraw) (Correct)

....only allows programs written in a subset of the PASCAL language. The problem with such limitations is that they prevent one from studying complex programs. The unchallenging demands of simple programs can make naive schemes like random test generation appear to work better than they actually do [McGraw et al. 1997]. 2. The function minimization techniques applied are often overly simplistic. ADTEST and TESTGEN use gradient descent to perform function minimization, and this technique suffers when the objective function contains local minima. Although quantitative results were not reported on the performance ....

....are the most complex programs for which results have been reported. By contrast standard programs reported in the literature average 30 lines of code and have relatively simple conditionals [Yin et al. 1997,Korel, 1996,Chang et al. 1996] Results of GADGET runs on small programs can be found in [McGraw et al. 1997]. We take advantage of GADGET s capability for processing complex programs by examining the impact of program complexity on the problem of dynamic test data generation. Our gradient descent algorithm is quite simple. It begins with a seed input, and measures the objective function for all inputs ....

[Article contains additional citation context not shown here]

McGraw, G., Michael, C., and Schatz, M. (1997). Generating software test data by evolution. Technical report, Reliable Software Technologies, Sterling, VA. Submitted to IEEE Transactions on Software Engineering.

Analysis and Visualization of - Predicate Dependence On (Correct)

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C.C. Michael, G. McGraw, and M.A. Schatz, "Generating Software Test Data by Evolution," IEEE Trans. Software Eng., vol. 27, no. 12, pp. 1085-1110, Dec. 2001.

Metrics Are Fitness Functions Too - Mark Harman John (Correct)