aaa

fuzzer.py

@@ -84,7 +84,7 @@ def random_params(arguments: list[Arg]) -> Params:
 pools: dict[tuple, set[tuple]] = {}
 
 
-def get_pool(arguments: list[Arg]) -> set[Params]:
+def get_pool(arguments: list[Arg]) -> list[Params]:
     arg_types = tuple([arg_type for _, arg_type in arguments])
     arg_names = [arg_name for arg_name, _ in arguments]
 
@@ -103,7 +103,7 @@ def get_pool(arguments: list[Arg]) -> set[Params]:
 
         pools[arg_types] = new_pool
 
-    return set([frozendict({arg_names[i]: p for i, p in enumerate(param)}) for param in pools[arg_types]])
+    return [frozendict({arg_names[i]: p for i, p in enumerate(param)}) for param in pools[arg_types]]
 
 
 def mutate(test_case: Params, arguments: list[Arg]) -> Params:
@@ -132,7 +132,7 @@ def crossover(chosen_test: Params, other_chosen_test: Params, arguments: list[Ar
 
 
 def generate_test_case(f_name: str, arguments: list[Arg]) -> Params:
-    pool: set[Params] = get_pool(arguments)
+    pool: list[Params] = get_pool(arguments)
 
     while True:
         test = sample(pool, 1)[0]

genetic.py

@@ -1,6 +1,8 @@
+import argparse
 import os
 import random
 import sys
+from functools import partial
 
 import frozendict
 import tqdm
@@ -8,6 +10,7 @@ from deap import creator, base, tools, algorithms
 
 import fuzzer
 import instrument
+import operators
 from fuzzer import generate_test_case, get_test_class
 
 INDMUPROB = 0.05
@@ -18,11 +21,32 @@ NPOP = 1000
 NGEN = 200
 REPS = 10
 
-to_test: str = ""
-
 OUT_DIR = os.path.join(os.path.dirname(__file__), "tests")
 
 
+class Archive:
+    true_branches: dict[int, any]
+    false_branches: dict[int, any]
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.true_branches = {}
+        self.false_branches = {}
+
+    def branches_covered(self) -> int:
+        return len(self.true_branches.keys()) + len(self.false_branches.keys())
+
+    def branches_str(self) -> str:
+        branch_ids = sorted([f"{branch:2d}T" for branch in self.true_branches.keys()] +
+                            [f"{branch:2d}F" for branch in self.false_branches.keys()])
+        return ' '.join([branch.strip() for branch in branch_ids])
+
+    def build_suite(self) -> set[instrument.Params]:
+        return set(list(self.true_branches.values()) + list(self.false_branches.values()))
+
+
 def normalize(x):
     return x / (1.0 + x)
 
@@ -32,25 +56,19 @@ def init_deap():
     creator.create("Individual", list, fitness=creator.Fitness)
 
 
-def taken_branches_descriptor() -> str:
-    branch_ids = sorted([f"{branch:2d}T" for branch in instrument.archive_true_branches.keys()] +
-                        [f"{branch:2d}F" for branch in instrument.archive_false_branches.keys()])
-    return ' '.join([branch.strip() for branch in branch_ids])
-
-
 def generate(f_name: str):
-    global to_test
-    to_test = f_name
-
     orig_name = instrument.BranchTransformer.to_original_name(f_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()
+
     toolbox = base.Toolbox()
     toolbox.register("attr_test_case", lambda: list(generate_test_case(f_name, args).items()))
     toolbox.register("individual", tools.initIterate, creator.Individual, lambda: toolbox.attr_test_case())
     toolbox.register("population", tools.initRepeat, list, toolbox.individual)
-    toolbox.register("evaluate", compute_fitness)
+    toolbox.register("evaluate", partial(compute_fitness, f_name, archive))
 
     def mate(tc1, tc2):
         t1, t2 = frozendict.frozendict(tc1), frozendict.frozendict(tc2)
@@ -71,44 +89,33 @@ def generate(f_name: str):
     top_result = set()
     top_coverage = 0
 
-    range_start, range_end = instrument.n_of_branches[to_test]
-    total_branches = (range_end - range_start) * 2  # *2 because of True and False
-
-    coverage = []
     for i in range(REPS):
-        instrument.archive_true_branches = {}
-        instrument.archive_false_branches = {}
+        archive.reset()
         population = toolbox.population(n=NPOP)
 
         algorithms.eaSimple(population, toolbox, CXPROB, MUPROB, NGEN, verbose=False)
 
-        true_covered = len(instrument.archive_true_branches.keys())
-        false_covered = len(instrument.archive_false_branches.keys())
-        tot_covered = true_covered + false_covered
+        tot_covered = archive.branches_covered()
 
         cov: float = (tot_covered / total_branches) * 100
-        coverage.append(cov)
 
-        branches = taken_branches_descriptor()
+        branches = archive.branches_str()
         print(f"{orig_name}: rep #{i:02d}: Cov: {cov:02.02f}% ({tot_covered}/{total_branches} branches): {branches}")
 
         if cov > top_coverage:
-            top_result = set(list(instrument.archive_true_branches.values()) +
-                             list(instrument.archive_false_branches.values()))
+            top_result = archive.build_suite()
             top_coverage = cov
 
-    print(coverage)
-
     return top_result
 
 
-def compute_fitness(individual: list) -> tuple[float]:
+def compute_fitness(f_name: str, archive: Archive, individual: list) -> tuple[float]:
     x = frozendict.frozendict(individual)
-    range_start, range_end = instrument.n_of_branches[to_test]
+    range_start, range_end = instrument.n_of_branches[f_name]
 
     # Reset any distance values from previous executions
-    instrument.distances_true = {}
-    instrument.distances_false = {}
+    operators.distances_true = {}
+    operators.distances_false = {}
 
     # the archive_true_branches and archive_false_branches are reset after
     # each generation. This is intentional as they are used to archive branches that
@@ -117,7 +124,7 @@ def compute_fitness(individual: list) -> tuple[float]:
 
     # Run the function under test
     try:
-        out = instrument.invoke(to_test, x)
+        instrument.invoke(f_name, x)
     except AssertionError:
         # print(to_test, x, "=", "[FAILS] fitness = 100.0")
         return 100.0,
@@ -126,18 +133,18 @@ def compute_fitness(individual: list) -> tuple[float]:
 
     # Sum up branch distances
     for branch in range(range_start, range_end):
-        if branch in instrument.distances_true:
-            if instrument.distances_true[branch] == 0 and branch not in instrument.archive_true_branches:
-                instrument.archive_true_branches[branch] = x
-            if branch not in instrument.archive_true_branches:
-                fitness += normalize(instrument.distances_true[branch])
+        if branch in operators.distances_true:
+            if operators.distances_true[branch] == 0 and branch not in archive.true_branches:
+                archive.true_branches[branch] = x
+            if branch not in archive.true_branches:
+                fitness += normalize(operators.distances_true[branch])
 
     for branch in range(range_start, range_end):
-        if branch in instrument.distances_false:
-            if instrument.distances_false[branch] == 0 and branch not in instrument.archive_false_branches:
-                instrument.archive_false_branches[branch] = x
-            if branch not in instrument.archive_false_branches:
-                fitness += normalize(instrument.distances_false[branch])
+        if branch in operators.distances_false:
+            if operators.distances_false[branch] == 0 and branch not in archive.false_branches:
+                archive.false_branches[branch] = x
+            if branch not in archive.false_branches:
+                fitness += normalize(operators.distances_false[branch])
 
     # print(to_test, x, "=", out, "fitness =", fitness)
     return fitness,
@@ -146,13 +153,20 @@ def compute_fitness(individual: list) -> tuple[float]:
 def build_suite(f_name: str):
     instr_name = instrument.BranchTransformer.to_instrumented_name(f_name)
     cases = generate(instr_name)
-    with open(os.path.join(OUT_DIR, f_name + ".py"), "w") as f:
+    with open(os.path.join(OUT_DIR, "test_" + f_name + ".py"), "w") as f:
         f.write(get_test_class(instr_name, cases))
 
 
 def main():
     random.seed(0)  # init random seed
-    instrument.load_benchmark(save_instrumented=False)  # instrument all files in benchmark
+
+    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="*")
+
+    instrument.load_benchmark(save_instrumented=False, files=parser.parse_args().file)
     init_deap()
 
     for instr_f in tqdm.tqdm(sorted(instrument.functions.keys()), desc="Generating tests"):

instrument.py

@@ -1,28 +1,18 @@
-from typing import Optional
+import ast
 import os.path
+import sys
+from typing import Optional
 
+import astunparse
 import tqdm
 from frozendict import frozendict
+from operators import evaluate_condition
 
-import ast
-import astunparse
-import sys
-import random
-from operators import compute_distances
-
-# hyperparameters
 ROOT_DIR: str = os.path.dirname(__file__)
 IN_DIR: str = os.path.join(ROOT_DIR, 'benchmark')
 OUT_DIR: str = os.path.join(ROOT_DIR, 'instrumented')
 SUFFIX: str = "_instrumented"
 
-distances_true: dict[int, int] = {}
-distances_false: dict[int, int] = {}
-
-# Archive of solutions
-archive_true_branches: dict[int, any] = {}
-archive_false_branches: dict[int, any] = {}
-
 
 class BranchTransformer(ast.NodeTransformer):
     branches_range: dict[str, tuple[int, int]]
@@ -78,7 +68,7 @@ class BranchTransformer(ast.NodeTransformer):
             return self.generic_visit(ast_node)
 
         self.branch_num += 1
-        return ast.Call(func=ast.Name("evaluate_condition", ast.Load()),
+        return ast.Call(func=ast.Name(evaluate_condition.__name__, ast.Load()),
                         args=[ast.Num(self.branch_num),
                               ast.Str(ast_node.ops[0].__class__.__name__),
                               ast_node.left,
@@ -88,41 +78,6 @@ class BranchTransformer(ast.NodeTransformer):
                         kwargs=None)
 
 
-def update_maps(condition_num, d_true, d_false):
-    global distances_true, distances_false
-
-    if condition_num in distances_true.keys():
-        distances_true[condition_num] = min(distances_true[condition_num], d_true)
-    else:
-        distances_true[condition_num] = d_true
-
-    if condition_num in distances_false.keys():
-        distances_false[condition_num] = min(distances_false[condition_num], d_false)
-    else:
-        distances_false[condition_num] = d_false
-
-
-def evaluate_condition(num, op, lhs, rhs):  # type: ignore
-    if op == "In":
-        if isinstance(lhs, str):
-            lhs = ord(lhs)
-
-        minimum = sys.maxsize
-        for elem in rhs.keys():
-            distance = abs(lhs - ord(elem))
-            if distance < minimum:
-                minimum = distance
-
-        distance_true, distance_false = minimum, 1 if minimum == 0 else 0
-    else:
-        distance_true, distance_false = compute_distances(op, lhs, rhs)
-
-    update_maps(num, distance_true, distance_false)
-
-    # distance == 0 equivalent to actual test by construction
-    return distance_true == 0
-
-
 ArgType = str
 Arg = tuple[str, ArgType]
 Params = frozendict[str, any]
@@ -203,9 +158,14 @@ def find_py_files(search_dir: str):
                 yield os.path.join(cwd, file)
 
 
-def load_benchmark(save_instrumented=True):
+def load_benchmark(save_instrumented=True, files: list[str] = ()):
+    to_load = set([os.path.splitext(os.path.basename(file))[0] + ".py" for file in files])
+    do_all = len(to_load) == 0
+
     for file in tqdm.tqdm(find_py_files(IN_DIR), desc="Instrumenting"):
-        instrument(file, os.path.join(OUT_DIR, os.path.basename(file)), save_instrumented=save_instrumented)
+        filename = os.path.basename(file)
+        if do_all or filename in to_load:
+            instrument(file, os.path.join(OUT_DIR, filename), save_instrumented=save_instrumented)
 
 
 def call_statement(f_name: str, f_args: Params) -> str:

operators.py

@@ -1,9 +1,13 @@
+import sys
 from dataclasses import dataclass
-from typing import TypeVar, Callable
 from typing import Generic
+from typing import TypeVar, Callable
 
 from nltk import edit_distance
 
+distances_true: dict[int, int] = {}
+distances_false: dict[int, int] = {}
+
 T = TypeVar('T')
 U = TypeVar('U')
 
@@ -100,7 +104,7 @@ def str_check(a: any, b: any) -> bool:
     return type(a) == str and type(b) == str
 
 
-def compute_distances(name: str, lhs: any, rhs: any) -> tuple[int, int]:
+def compute_distances(name: str, lhs: any, rhs: any) -> tuple[int, int, bool]:
     if int_str_check(lhs, rhs):
         lhs_int = int_str_convert(lhs)
         rhs_int = int_str_convert(rhs)
@@ -109,13 +113,50 @@ def compute_distances(name: str, lhs: any, rhs: any) -> tuple[int, int]:
             raise ValueError(f"'{name}' is not a valid CmpOp name for 'int_str' operators")
 
         op = int_str_by_name[name]
-        return op.true_dist(lhs_int, rhs_int), op.false_dist(lhs_int, rhs_int)
+        return op.true_dist(lhs_int, rhs_int), op.false_dist(lhs_int, rhs_int), op.test(lhs_int, rhs_int)
 
     if str_check(lhs, rhs):
         if name not in str_by_name:
             raise ValueError(f"'{name}' is not a valid CmpOp name for 'str' operators")
 
         op = str_by_name[name]
-        return op.true_dist(lhs, rhs), op.false_dist(lhs, rhs)
+        return op.true_dist(lhs, rhs), op.false_dist(lhs, rhs), op.test(lhs, rhs)
 
     raise ValueError(f"'{lhs}' and '{rhs}' are not suitable for both 'int_str' and 'str' operators")
+
+
+def update_map(the_map: dict[int, int], condition_num: int, distance: int):
+    if condition_num in the_map.keys():
+        the_map[condition_num] = min(the_map[condition_num], distance)
+    else:
+        the_map[condition_num] = distance
+
+
+def update_maps(condition_num, d_true, d_false):
+    global distances_true, distances_false
+    update_map(distances_true, condition_num, d_true)
+    update_map(distances_false, condition_num, d_false)
+
+
+def in_op(num, lhs, rhs):
+    if isinstance(lhs, str):
+        lhs = ord(lhs)
+
+    minimum = sys.maxsize
+    for elem in rhs.keys():
+        distance = abs(lhs - ord(elem))
+        if distance < minimum:
+            minimum = distance
+
+    distance_true, distance_false = minimum, 1 if minimum == 0 else 0
+    update_maps(num, distance_true, distance_false)
+    return distance_true == 0  # distance == 0 equivalent to actual test by construction
+
+
+def evaluate_condition(num, op, lhs, rhs):
+    if op == "In":
+        return in_op(num, lhs, rhs)
+
+    distance_true, distance_false, test = compute_distances(op, lhs, rhs)
+    update_maps(num, distance_true, distance_false)
+    return test

tests/output.txt

@@ -1,6 +1,3 @@
-/usr/local/bin/python3.10 /Volumes/Data/git/kse/project-02-python-test-generator-maggicl/genetic.py 
-Instrumenting: 10it [00:00, 722.61it/s]
-Generating tests:   0%|          | 0/12 [00:00<?, ?it/s]
 anagram_check: rep #00: Cov: 87.50% (7/8 branches): 1F 1T 2F 2T 3F 3T 4F
 anagram_check: rep #01: Cov: 87.50% (7/8 branches): 1F 1T 2F 2T 3F 3T 4F
 anagram_check: rep #02: Cov: 100.00% (8/8 branches): 1F 1T 2F 2T 3F 3T 4F 4T
@@ -11,8 +8,6 @@ anagram_check: rep #06: Cov: 100.00% (8/8 branches): 1F 1T 2F 2T 3F 3T 4F 4T
 anagram_check: rep #07: Cov: 100.00% (8/8 branches): 1F 1T 2F 2T 3F 3T 4F 4T
 anagram_check: rep #08: Cov: 87.50% (7/8 branches): 1F 1T 2F 2T 3F 3T 4F
 anagram_check: rep #09: Cov: 100.00% (8/8 branches): 1F 1T 2F 2T 3F 3T 4F 4T
-[87.5, 87.5, 100.0, 87.5, 87.5, 87.5, 100.0, 100.0, 87.5, 100.0]
-Generating tests:   8%|▊         | 1/12 [01:05<12:02, 65.64s/it]
 cd_count: rep #00: Cov: 100.00% (14/14 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6F 6T 7F 7T
 cd_count: rep #01: Cov: 100.00% (14/14 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6F 6T 7F 7T
 cd_count: rep #02: Cov: 100.00% (14/14 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6F 6T 7F 7T
@@ -23,8 +18,6 @@ cd_count: rep #06: Cov: 100.00% (14/14 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T
 cd_count: rep #07: Cov: 100.00% (14/14 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6F 6T 7F 7T
 cd_count: rep #08: Cov: 100.00% (14/14 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6F 6T 7F 7T
 cd_count: rep #09: Cov: 100.00% (14/14 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6F 6T 7F 7T
-[100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0]
-Generating tests:  17%|█▋        | 2/12 [02:30<12:50, 77.04s/it]
 check_armstrong: rep #00: Cov: 100.00% (10/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T
 check_armstrong: rep #01: Cov: 100.00% (10/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T
 check_armstrong: rep #02: Cov: 100.00% (10/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T
@@ -35,8 +28,6 @@ check_armstrong: rep #06: Cov: 100.00% (10/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T
 check_armstrong: rep #07: Cov: 100.00% (10/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T
 check_armstrong: rep #08: Cov: 100.00% (10/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T
 check_armstrong: rep #09: Cov: 100.00% (10/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T
-[100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0]
-Generating tests:  25%|██▌       | 3/12 [03:36<10:45, 71.73s/it]
 decrypt: rep #00: Cov: 100.00% (2/2 branches): 2F 2T
 decrypt: rep #01: Cov: 100.00% (2/2 branches): 2F 2T
 decrypt: rep #02: Cov: 100.00% (2/2 branches): 2F 2T
@@ -47,8 +38,6 @@ decrypt: rep #06: Cov: 100.00% (2/2 branches): 2F 2T
 decrypt: rep #07: Cov: 100.00% (2/2 branches): 2F 2T
 decrypt: rep #08: Cov: 100.00% (2/2 branches): 2F 2T
 decrypt: rep #09: Cov: 100.00% (2/2 branches): 2F 2T
-[100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0]
-Generating tests:  33%|███▎      | 4/12 [04:49<09:40, 72.50s/it]
 encrypt: rep #00: Cov: 100.00% (2/2 branches): 1F 1T
 encrypt: rep #01: Cov: 100.00% (2/2 branches): 1F 1T
 encrypt: rep #02: Cov: 100.00% (2/2 branches): 1F 1T
@@ -59,8 +48,6 @@ encrypt: rep #06: Cov: 100.00% (2/2 branches): 1F 1T
 encrypt: rep #07: Cov: 100.00% (2/2 branches): 1F 1T
 encrypt: rep #08: Cov: 100.00% (2/2 branches): 1F 1T
 encrypt: rep #09: Cov: 100.00% (2/2 branches): 1F 1T
-[100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0]
-Generating tests:  42%|████▏     | 5/12 [06:04<08:31, 73.13s/it]
 exponentiation: rep #00: Cov: 62.50% (5/8 branches): 1F 1T 2F 2T 3T
 exponentiation: rep #01: Cov: 62.50% (5/8 branches): 1F 1T 2F 2T 3T
 exponentiation: rep #02: Cov: 62.50% (5/8 branches): 1F 1T 2F 2T 3T
@@ -70,9 +57,6 @@ exponentiation: rep #05: Cov: 62.50% (5/8 branches): 1F 1T 2F 2T 3T
 exponentiation: rep #06: Cov: 62.50% (5/8 branches): 1F 1T 2F 2T 3T
 exponentiation: rep #07: Cov: 62.50% (5/8 branches): 1F 1T 2F 2T 3T
 exponentiation: rep #08: Cov: 62.50% (5/8 branches): 1F 1T 2F 2T 3T
-Generating tests:  50%|█████     | 6/12 [07:50<08:26, 84.43s/it]
-exponentiation: rep #09: Cov: 62.50% (5/8 branches): 1F 1T 2F 2T 3T
-[62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 62.5]
 gcd: rep #00: Cov: 100.00% (10/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T
 gcd: rep #01: Cov: 100.00% (10/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T
 gcd: rep #02: Cov: 100.00% (10/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T
@@ -83,8 +67,6 @@ gcd: rep #06: Cov: 100.00% (10/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T
 gcd: rep #07: Cov: 100.00% (10/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T
 gcd: rep #08: Cov: 100.00% (10/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T
 gcd: rep #09: Cov: 100.00% (10/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T
-[100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0]
-Generating tests:  58%|█████▊    | 7/12 [09:09<06:52, 82.57s/it]
 longest_sorted_substr: rep #00: Cov: 100.00% (4/4 branches): 1F 1T 2F 2T
 longest_sorted_substr: rep #01: Cov: 100.00% (4/4 branches): 1F 1T 2F 2T
 longest_sorted_substr: rep #02: Cov: 100.00% (4/4 branches): 1F 1T 2F 2T
@@ -94,9 +76,7 @@ longest_sorted_substr: rep #05: Cov: 100.00% (4/4 branches): 1F 1T 2F 2T
 longest_sorted_substr: rep #06: Cov: 100.00% (4/4 branches): 1F 1T 2F 2T
 longest_sorted_substr: rep #07: Cov: 100.00% (4/4 branches): 1F 1T 2F 2T
 longest_sorted_substr: rep #08: Cov: 100.00% (4/4 branches): 1F 1T 2F 2T
-Generating tests:  67%|██████▋   | 8/12 [10:19<05:15, 78.78s/it]
 longest_sorted_substr: rep #09: Cov: 100.00% (4/4 branches): 1F 1T 2F 2T
-[100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0]
 rabin_karp_search: rep #00: Cov: 90.00% (9/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F
 rabin_karp_search: rep #01: Cov: 90.00% (9/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F
 rabin_karp_search: rep #02: Cov: 90.00% (9/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F
@@ -107,8 +87,6 @@ rabin_karp_search: rep #06: Cov: 90.00% (9/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T
 rabin_karp_search: rep #07: Cov: 90.00% (9/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F
 rabin_karp_search: rep #08: Cov: 90.00% (9/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F
 rabin_karp_search: rep #09: Cov: 90.00% (9/10 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F
-[90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 90.0]
-Generating tests:  75%|███████▌  | 9/12 [11:32<03:50, 76.96s/it]
 raildecrypt: rep #00: Cov: 87.50% (14/16 branches): 5F 5T 6F 6T 7F 8F 8T 9T 10F 10T 11F 11T 12F 12T
 raildecrypt: rep #01: Cov: 93.75% (15/16 branches): 5F 5T 6F 6T 7F 7T 8F 8T 9T 10F 10T 11F 11T 12F 12T
 raildecrypt: rep #02: Cov: 93.75% (15/16 branches): 5F 5T 6F 6T 7F 7T 8F 8T 9T 10F 10T 11F 11T 12F 12T
@@ -119,9 +97,17 @@ raildecrypt: rep #06: Cov: 93.75% (15/16 branches): 5F 5T 6F 6T 7F 7T 8F 8T 9T 1
 raildecrypt: rep #07: Cov: 93.75% (15/16 branches): 5F 5T 6F 6T 7F 7T 8F 8T 9T 10F 10T 11F 11T 12F 12T
 raildecrypt: rep #08: Cov: 93.75% (15/16 branches): 5F 5T 6F 6T 7F 7T 8F 8T 9T 10F 10T 11F 11T 12F 12T
 raildecrypt: rep #09: Cov: 68.75% (11/16 branches): 5T 6F 6T 8F 8T 9T 10F 10T 11F 11T 12T
-[87.5, 93.75, 93.75, 87.5, 87.5, 87.5, 93.75, 93.75, 93.75, 68.75]
-Generating tests:  83%|████████▎ | 10/12 [17:25<05:24, 162.00s/it]
 railencrypt: rep #00: Cov: 100.00% (8/8 branches): 1F 1T 2F 2T 3F 3T 4F 4T
 railencrypt: rep #01: Cov: 100.00% (8/8 branches): 1F 1T 2F 2T 3F 3T 4F 4T
 railencrypt: rep #02: Cov: 62.50% (5/8 branches): 1T 2F 2T 4F 4T
 railencrypt: rep #03: Cov: 100.00% (8/8 branches): 1F 1T 2F 2T 3F 3T 4F 4T
+zeller: rep #00: Cov: 93.75% (15/16 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6T 7F 7T 8F 8T
+zeller: rep #01: Cov: 93.75% (15/16 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6T 7F 7T 8F 8T
+zeller: rep #02: Cov: 93.75% (15/16 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6T 7F 7T 8F 8T
+zeller: rep #03: Cov: 93.75% (15/16 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6T 7F 7T 8F 8T
+zeller: rep #04: Cov: 93.75% (15/16 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6T 7F 7T 8F 8T
+zeller: rep #05: Cov: 93.75% (15/16 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6T 7F 7T 8F 8T
+zeller: rep #06: Cov: 93.75% (15/16 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6T 7F 7T 8F 8T
+zeller: rep #07: Cov: 93.75% (15/16 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6T 7F 7T 8F 8T
+zeller: rep #08: Cov: 93.75% (15/16 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6T 7F 7T 8F 8T
+zeller: rep #09: Cov: 93.75% (15/16 branches): 1F 1T 2F 2T 3F 3T 4F 4T 5F 5T 6T 7F 7T 8F 8T

tests/test_railencrypt.py

@@ -0,0 +1,14 @@
+from unittest import TestCase
+
+from benchmark.railfence_cipher import railencrypt
+
+
+class Test_railencrypt(TestCase):
+    def test_railencrypt_1(self):
+        assert railencrypt(st=')x8ro;BVm', k=865) == ')x8ro;BVm'
+
+    def test_railencrypt_2(self):
+        assert railencrypt(st='q338K a{.', k=4) == 'qa3 {3K.8'
+
+    def test_railencrypt_3(self):
+        assert railencrypt(st='X61*8p', k=5) == 'X61*p8'

tests/test_zeller.py

@@ -0,0 +1,23 @@
+from unittest import TestCase
+
+from benchmark.zellers_birthday import zeller
+
+
+class Test_zeller(TestCase):
+    def test_zeller_1(self):
+        assert zeller(d=-134, m=797, y=67) == 'Sunday'
+
+    def test_zeller_2(self):
+        assert zeller(d=-156, m=10, y=-57) == 'Wednesday'
+
+    def test_zeller_3(self):
+        assert zeller(d=715, m=444, y=74) == 'Thursday'
+
+    def test_zeller_4(self):
+        assert zeller(d=-726, m=864, y=-16) == 'Thursday'
+
+    def test_zeller_5(self):
+        assert zeller(d=31, m=910, y=2) == 'Sunday'
+
+    def test_zeller_6(self):
+        assert zeller(d=369, m=-917, y=-1000) == 'Sunday'