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kse-02/genetic.py

161 lines
5 KiB
Python

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