Example
Example: Software Failure Probability Evaluation
Run
conda activate bahamas_libs
cd /path/to/BAHAMAS/examples
python ../bahamas/main.py -i bbn.toml
BAHAMAS Input
[BBN]
[BBN.params]
samples = 40000
seed = 2
[BBN.files]
task = "../data/Task_List.xlsx"
defect = "../data/Defect_Data.xlsx"
approx = "../data/sdlc_macro.xlsx"
[BBN.analysis]
type = 'precise'
Screen Output
06-Aug-25 09:39:54 BAHAMAS INFO Welcome!
06-Aug-25 09:39:54 BAHAMAS INFO Input file: ../data/Task_List.xlsx
06-Aug-25 09:39:54 BAHAMAS WARNING Default output file ../data/out_Task_List.xlsx will be used
06-Aug-25 09:39:54 BAHAMAS.ODC INFO Construct ODC Conditional Distribution for each SDLC stage
06-Aug-25 09:39:54 BAHAMAS.UCA INFO Construct UCA ODC defect correlation distribution.
06-Aug-25 09:39:54 BAHAMAS.BBN INFO Sampling HEP and DCP
06-Aug-25 09:39:54 BAHAMAS.HEP INFO Calculate SDLC "Concept" stage HEP
06-Aug-25 09:39:54 BAHAMAS.DCP INFO Calculate DCP for SDLC "Concept" stage
06-Aug-25 09:39:54 BAHAMAS.HEP INFO Calculate SDLC "Requirement" stage HEP
06-Aug-25 09:39:54 BAHAMAS.DCP INFO Calculate DCP for SDLC "Requirement" stage
06-Aug-25 09:39:54 BAHAMAS.HEP INFO Calculate SDLC "Design" stage HEP
06-Aug-25 09:39:54 BAHAMAS.DCP INFO Calculate DCP for SDLC "Design" stage
06-Aug-25 09:39:54 BAHAMAS.HEP INFO Calculate SDLC "Implementation" stage HEP
06-Aug-25 09:39:54 BAHAMAS.DCP INFO Calculate DCP for SDLC "Implementation" stage
06-Aug-25 09:39:54 BAHAMAS.HEP INFO Calculate SDLC "Testing" stage HEP
06-Aug-25 09:39:54 BAHAMAS.DCP INFO Calculate DCP for SDLC "Testing" stage
06-Aug-25 09:39:54 BAHAMAS.HEP INFO Calculate SDLC "Install and Maintenance" stage HEP
06-Aug-25 09:39:54 BAHAMAS.DCP INFO Calculate DCP for SDLC "Install and Maintenance" stage
06-Aug-25 09:39:54 BAHAMAS.BBN INFO Sampling ODC
06-Aug-25 09:39:54 BAHAMAS.BBN INFO Sampling UCA
06-Aug-25 09:39:54 BAHAMAS.BBN INFO Compute marginal ODC
06-Aug-25 09:39:54 BAHAMAS.BBN INFO BBN Propagation
06-Aug-25 09:39:54 BAHAMAS.BBN INFO Compute UCA and total failure probabilities
06-Aug-25 09:39:56 BAHAMAS INFO Software total failure: 2.9825182468709206e-05 with std 1.4002568043296736e-05
06-Aug-25 09:39:56 BAHAMAS INFO UCA type: UCA-A, Mean: 7.127925281781246e-06, STD: 3.4908455837352817e-06
06-Aug-25 09:39:56 BAHAMAS INFO UCA type: UCA-B, Mean: 1.3451519498232246e-05, STD: 6.3548059571775254e-06
06-Aug-25 09:39:56 BAHAMAS INFO UCA type: UCA-C, Mean: 4.900300523766049e-06, STD: 2.4943170792606996e-06
06-Aug-25 09:39:56 BAHAMAS INFO UCA type: UCA-D, Mean: 4.345437164929662e-06, STD: 2.336590212125128e-06
06-Aug-25 09:39:56 BAHAMAS INFO ... Complete!
Plots
Figure 5 SDLC Stage Failure Probabilities Based on Human Error Propagation
Figure 6 Software Orthogonal Defect Classification Failure Probabilities
Figure 7 Software Unsafe Control Action Failure Probabilities
Figure 8 Total Software Failure Probability
Example: Common Cause Component Group Generation
Run
conda activate bahamas_libs
cd /path/to/BAHAMAS/examples
python ../bahamas/main.py -i ccf.toml
BAHAMAS Input
[CCF]
[CCF.files]
structure= "../data/Scenario_6.csv"
[CCF.generate]
output_file_base = "s6_cccg"
output_type = "csv"
final = true
single = true
double = true
triple = true
Screen Output
22-Oct-25 11:19:44 BAHAMAS INFO Welcome to use BAHAMAS!
22-Oct-25 11:19:44 BAHAMAS.Workflow INFO Initialization
22-Oct-25 11:19:44 BAHAMAS.validate INFO TOML input file is valid.
22-Oct-25 11:19:44 BAHAMAS.Workflow INFO Start CCCGs generation
22-Oct-25 11:19:44 BAHAMAS.CCCG INFO Generating
22-Oct-25 11:19:45 BAHAMAS.CCCG INFO Save CCCGs into s6_cccg_final.csv
22-Oct-25 11:19:45 BAHAMAS.CCCG INFO Save CCCGs into s6_cccg_single.csv
22-Oct-25 11:19:45 BAHAMAS.CCCG INFO Save CCCGs into s6_cccg_double.csv
22-Oct-25 11:19:45 BAHAMAS.CCCG INFO Save CCCGs into s6_cccg_triple.csv
22-Oct-25 11:19:45 BAHAMAS.Workflow INFO End CCCGs generation
22-Oct-25 11:19:45 BAHAMAS INFO ... Complete!