144 lines
4.4 KiB
Python
144 lines
4.4 KiB
Python
import argparse
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import os
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import random
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from functools import partial
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from typing import Tuple, List, Set
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import frozendict
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import tqdm
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from deap import creator, base, tools, algorithms
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import fuzzer
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import instrument
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import operators
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from fuzzer import generate_test_case, get_test_class
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from archive import Archive
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INDMUPROB = 0.05
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MUPROB = 0.33
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CXPROB = 0.33
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TOURNSIZE = 3
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NPOP = 1000
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NGEN = 200
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REPS = 10
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OUT_DIR = os.path.join(os.path.dirname(__file__), "tests")
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def normalize(x):
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return x / (1.0 + x)
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def init_deap():
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creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
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creator.create("Individual", list, fitness=creator.FitnessMin)
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def generate(orig_name: str) -> Set[instrument.Params]:
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f_name = instrument.BranchTransformer.to_instrumented_name(orig_name)
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args = instrument.functions[f_name]
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range_start, range_end = instrument.n_of_branches[f_name]
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total_branches = (range_end - range_start) * 2 # *2 because of True and False
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archive = Archive(f_name)
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toolbox = base.Toolbox()
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toolbox.register("attr_test_case", lambda: list(generate_test_case(f_name, args, archive).items()))
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toolbox.register("individual", tools.initIterate, creator.Individual, lambda: toolbox.attr_test_case())
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toolbox.register("population", tools.initRepeat, list, toolbox.individual)
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toolbox.register("evaluate", partial(compute_fitness, f_name, archive))
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def mate(tc1, tc2):
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t1, t2 = frozendict.frozendict(tc1), frozendict.frozendict(tc2)
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o1, o2 = fuzzer.crossover(t1, t2, args)
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i1, i2 = creator.Individual(o1.items()), creator.Individual(o2.items())
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return i1, i2
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def mutate(tc):
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t = frozendict.frozendict(tc)
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o = fuzzer.mutate(t, args)
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i1 = creator.Individual(o.items())
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return i1,
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toolbox.register("mate", mate)
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toolbox.register("mutate", mutate)
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toolbox.register("select", tools.selTournament, tournsize=TOURNSIZE)
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top_result = set()
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top_coverage = 0
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for i in range(REPS):
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population = toolbox.population(n=NPOP)
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# Create statistics object
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population, _ = algorithms.eaSimple(population, toolbox, CXPROB, MUPROB, NGEN, verbose=False)
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for member in population:
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archive.consider_test(frozendict.frozendict(member))
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tot_covered = archive.branches_covered()
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cov: float = (tot_covered / total_branches) * 100
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branches = archive.branches_str()
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print(f"{orig_name}: rep #{i:02d}: Cov: {cov:02.02f}% ({tot_covered}/{total_branches} branches): {branches}")
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print(archive.suite_str())
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if cov > top_coverage:
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top_result = archive.build_suite()
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top_coverage = cov
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if tot_covered == total_branches:
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break
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return top_result
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def compute_fitness(f_name: str, archive: Archive, individual: list) -> Tuple[float]:
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x = frozendict.frozendict(individual)
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range_start, range_end = instrument.n_of_branches[f_name]
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# Run the function under test
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try:
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out = instrument.invoke(f_name, x)
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except AssertionError:
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# print(f_name, x, "=", "[FAILS] fitness = 100.0")
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return 100.0,
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fitness = 0.0
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# Sum up branch distances
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for branch in range(range_start, range_end):
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if branch in operators.distances_true:
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if branch not in archive.true_branches:
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fitness += normalize(operators.distances_true[branch])
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else:
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fitness += 10
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for branch in range(range_start, range_end):
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if branch in operators.distances_false:
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if branch not in archive.false_branches:
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fitness += normalize(operators.distances_false[branch])
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else:
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fitness += 10
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# print(f_name, x, "=", out, "fitness =", fitness)
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return fitness,
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def main():
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parser = argparse.ArgumentParser(prog='genetic.py',
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description='Runs genetic algorithm for test case generation. Works on benchmark '
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'files situated in the \'benchmark\' directory.')
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parser.add_argument('file', type=str, help="File to test",
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nargs="*")
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parser.add_argument('-s', '--seed', type=int, help="Random generator seed",
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nargs="?", default=0)
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args = parser.parse_args()
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init_deap()
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fuzzer.generate_tests(args.file, args.seed, generate)
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if __name__ == '__main__':
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main()
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