apache / incubator-devlake-playground - Conditional Complexity

Intro

Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
Conditional complexity is measured at the unit level (methods, functions...).
Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).

Learn more...

Conditional Complexity Overall

There are 26 units with 307 lines of code in units (44.7% of code).

0 very complex units (0 lines of code)
0 complex units (0 lines of code)
0 medium complex units (0 lines of code)
1 simple units (16 lines of code)
25 very simple units (291 lines of code)

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

3D view of all units

Conditional Complexity per Extension

51+

26-50

11-25

6-10

1-5

Conditional Complexity per Logical Component

primary logical decomposition

51+

26-50

11-25

6-10

1-5

Most Complex Units

Top 26 most complex units

Unit	# lines	McCabe index	# params
def create_db_engine() in playground/db_engine.py	16	6	1
def generate_random_status_changes() in playground/process_analysis/utils/status_transition_data_generator.py	28	5	1
def apply() in playground/process_analysis/issue_filter.py	11	5	2
def __update_nodes() in playground/process_analysis/status_transition_graph.py	10	4	3
def from_data_frame() in playground/process_analysis/status_transition_graph.py	14	4	3
def __edge_label() in playground/process_analysis/status_transition_graph_vistualizer.py	16	3	3
def __edge_tooltip() in playground/process_analysis/status_transition_graph_vistualizer.py	11	3	4
def __init__() in playground/process_analysis/status_transition_graph_vistualizer.py	4	2	2
def is_dot_executable_available() in playground/process_analysis/status_transition_graph_vistualizer.py	6	2	0
def boxplot() in playground/process_analysis/status_transition_graph_stats_plot.py	35	2	2
def _color_for_edge() in playground/process_analysis/status_transition_graph_stats_plot.py	9	2	3
def _create_next_status_change() in playground/process_analysis/utils/status_transition_data_generator.py	76	2	1
def __update_edges() in playground/process_analysis/status_transition_graph.py	8	2	3
def _for_same_issue() in playground/process_analysis/status_transition_graph.py	4	2	2
def _days_between() in playground/process_analysis/status_transition_graph.py	4	2	2
def visualize() in playground/process_analysis/status_transition_graph_vistualizer.py	5	1	0
def __category_rank() in playground/process_analysis/status_transition_graph_vistualizer.py	8	1	2
def __category_color() in playground/process_analysis/status_transition_graph_vistualizer.py	2	1	2
def __node_label() in playground/process_analysis/status_transition_graph_vistualizer.py	2	1	3
def __count_label() in playground/process_analysis/status_transition_graph_vistualizer.py	2	1	2
def __default_attrs() in playground/process_analysis/status_transition_graph_vistualizer.py	6	1	1
def _create_first_status_change() in playground/process_analysis/utils/status_transition_data_generator.py	11	1	0
def __init__() in playground/process_analysis/status_transition_graph.py	3	1	1
def add_status_change() in playground/process_analysis/status_transition_graph.py	4	1	3
def from_database() in playground/process_analysis/status_transition_graph.py	10	1	3
def _timedelta_between() in playground/process_analysis/status_transition_graph.py	2	1	2