diff --git a/execution/README.md b/execution/README.md
index 3c666dc7740331d7590194151ed01a1931712db8..7a71cf0d73d6dd737c181dc138a1dd4a7fb0dc30 100644
--- a/execution/README.md
+++ b/execution/README.md
@@ -167,7 +167,7 @@ The `./theodolite.py` is the entrypoint for all benchmark executions. Is has to
 * `<commit-interval>`: Kafka Streams' commit interval in milliseconds. Optional. Default `100`.
 * `<duration>`: Duration in minutes subexperiments should be executed for. Optional. Default `5`.
 * `<domain-restriction>`: The domain restriction: `domain-restriction` to use domain restriction `no-domain-restriction` to not use domain restriction. Default `no-domain-restriction`. For more details see Section _Domain Restriction_.
-* `<search-strategy>`: The benchmarking search strategy. Can be set to `check_all`, `linear-search` or `binary-search`. Default `default`. For more details see Section _Benchmarking Search Strategies_.
+* `<search-strategy>`: The benchmarking search strategy. Can be set to `check-all`, `linear-search` or `binary-search`. Default `check-all`. For more details see Section _Benchmarking Search Strategies_.
 
 ### Domain Restriction
 For dimension value, we have a domain of the amounts of instances. As a consequence, for each dimension value the maximum number of lag experiments is equal to the size of the domain. How the domain is determined is defined by the following domain restriction strategies.
@@ -180,7 +180,7 @@ For dimension value, we have a domain of the amounts of instances. As a conseque
 ### Benchmarking Search Strategies
 There are the following benchmarking strategies:
 
-* `check_all`: For each dimension value, execute one lag experiment for all amounts of instances within the current domain.
+* `check-all`: For each dimension value, execute one lag experiment for all amounts of instances within the current domain.
 * `linear-search`: A heuristic which works as follows: For each dimension value, execute one lag experiment for all number of instances within the current domain. The execution order is from the lowest number of instances to the highest amount of instances and the execution for each dimension value is stopped, when a suitable amount of instances is found or if all lag experiments for the dimension value were not successful.
 * `binary-search`: A heuristic which works as follows: For each dimension value, execute one lag experiment for all number of instances within the current domain. The execution order is in a binary-search-like manner. The execution is stopped, when a suitable amount of instances is found or if all lag experiments for the dimension value were not successful.
 
diff --git a/execution/theodolite.py b/execution/theodolite.py
index b6e6246134e28771056ac498520aea3512a17b10..6049f3ffac61af46212f521a91870e57342be478 100755
--- a/execution/theodolite.py
+++ b/execution/theodolite.py
@@ -70,7 +70,7 @@ if domain_restriction:
             search_strategy=binary_search_strategy,
             subexperiment_executor=subexperiment_executor,
             subexperiment_evaluator=subexperiment_evaluator)
-    # domain restriction + check_all
+    # domain restriction + check-all
     else:
         print(f"Going to execute {len(dim_values)*len(replicas)} subexperiments in total..")
         experiment_config = ExperimentConfig(
@@ -122,7 +122,7 @@ else:
             search_strategy=binary_search_strategy,
             subexperiment_executor=subexperiment_executor,
             subexperiment_evaluator=subexperiment_evaluator)
-    # no domain restriction + check_all
+    # no domain restriction + check-all
     else:
         print(f"Going to execute {len(dim_values)*len(replicas)} subexperiments in total..")
         experiment_config = ExperimentConfig(