SFL Simulator is used to to support research in spectrum-based fault localization. It simulates a component topology and its activations, creates an activity matrix, and calculates a diagnosis based on a similarity coefficient. The simulator is written in the ruby language (version 1.9.3).
- Ruby 1.9.3
- graphviz gem (plus graphviz installation)
- colorize gem
Create a new topology (please note, that all files should in the same directory).
require './sfl_actop.rb'
require './sfl_diagnosis.rb'
t = Topology.new()
Add components to the topology:
t.add("C0", 1.0, 1.0) # a 100% healthy component, C0, with 100% failure probability
t.add("C1", 1.0, 1.0) # a 100% healthy component, C1, with 100% failure probability
t.add("C2", 0.0, 0.0) # a 100% faulty component, C2, with 0% failure probability
t.add("C3", 0.5, 1.0) # a 50% intermittently faulty component, C3, with 100% failure probability
t.add("C4", 1.0, 1.0) # another healthy component
A component must have a name, a health value, and a failure probability. The name is an arbitrary but unique ruby string, the health value must be between 0.0 and 1.0. A health of 1.0 represents a 100% healthy component, a value of 0.0 represents a 100% faulty component. It determines the probability of a component to issue a fault if the component is activated. The failure probability (0.0..1.0) determines the probability that a fault is detectected, leading to a failure. If all failure probabilities are 0.0 a fault will be detected at the end of an activation.
Link the components to form a topology:
t.link("L0", "C0", "C1", 1.0) # link C0 -> C1 with 100% invocation probability
t.edge("L0", "C2") # link C0 -> C2 (in the same link)
t.link("L1", "C1", "C3", 0.8) # link C1 -> C1 with 80% invocation probability
t.link("L2", "C2", "C3", 0.5) # link C2 -> C3 with 50% invocation probability
t.link("L3", "C2", "C4", 0.3) # link C2 -> C4 with 30% invocation probability
A link between components must have a unique name (string), an originating component name (string), a destination component name (string), and an invocation probability. This determines the likelihood of a link being executed, and thus, a subsequent component being called.
Generate a picture of the topology:
TopologyOutput.graph(t, :png, "ex_readme.png")
This uses the graphviz library in order to generate a picture of the topology. The filetypes (e.g. :png) correspond to the file types provided by graphviz. This 
displays the topology described above.
The execution of the component topology can now be simulated, either with a fixed number of executions:
t.activate_often(["C0"], 20) # exercise Topology t 20 times, start in C0
Several components may be activated at the same time:
t.activate_often(["C0", "C4"]) {}
Components can also be activated until a component fails. This is useful when very low fault intermittency must be simulated:
t.activate_until_error(["C0"]) {}
Activation leads to traces, which can be shown:
TopologyOutput.traces(t)
This results in an output like the following. Each activation results in one line with the component executed, the link invoked, plus [fault, error, failure] information. The curly brackets {} indicate invocation nesting.
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]{L1[0,0,0]{C3[0,0,0]}}}{C2[1,1,0]{L2[0,1,0]{C3[1,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]{L1[0,0,0]{C3[1,1,1]}}}{C2[1,1,0]{L3[0,1,0]{C4[0,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]{L1[0,0,0]{C3[0,0,0]}}}{C2[1,1,0]{L2[0,1,0]{C3[0,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]}{C2[1,1,0]{L2[0,1,0]{C3[1,1,1]}}{L3[0,1,0]{C4[0,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]}{C2[1,1,0]{L2[0,1,0]{C3[0,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]{L1[0,0,0]{C3[1,1,1]}}}{C2[1,1,0]}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]{L1[0,0,0]{C3[0,0,0]}}}{C2[1,1,0]}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]}{C2[1,1,0]{L2[0,1,0]{C3[0,1,1]}}{L3[0,1,0]{C4[0,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]{L1[0,0,0]{C3[0,0,0]}}}{C2[1,1,0]{L2[0,1,0]{C3[1,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]{L1[0,0,0]{C3[0,0,0]}}}{C2[1,1,0]{L3[0,1,0]{C4[0,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]{L1[0,0,0]{C3[1,1,1]}}}{C2[1,1,0]{L2[0,1,0]{C3[1,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]{L1[0,0,0]{C3[0,0,0]}}}{C2[1,1,0]{L2[0,1,0]{C3[0,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]}{C2[1,1,0]{L3[0,1,0]{C4[0,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]}{C2[1,1,0]{L2[0,1,0]{C3[1,1,1]}}{L3[0,1,0]{C4[0,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]}{C2[1,1,0]}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]}{C2[1,1,0]{L2[0,1,0]{C3[0,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]{L1[0,0,0]{C3[0,0,0]}}}{C2[1,1,0]{L2[0,1,0]{C3[0,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]{L1[0,0,0]{C3[0,0,0]}}}{C2[1,1,0]}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]{L1[0,0,0]{C3[1,1,1]}}}{C2[1,1,0]{L2[0,1,0]{C3[1,1,1]}}}}[fail]
C0[0,0,0]{L0[0,0,0]{C1[0,0,0]{L1[0,0,0]{C3[1,1,1]}}}{C2[1,1,0]{L2[0,1,0]{C3[0,1,1]}}}}[fail]
An activated topology can be used for diagnosis experiments. First, the topology activation can be created ...
actop = Actop.new(t)
ActopOutput.screen(actop)
ActopOutput.graph(actop, :png, "ex_readme_actop.png")
... and then be shown.
C0 11111111111111111111
C1 11111111111111111111
C2 11111111111111111111
C3 01111110110010111111
C4 10100010001011101111
L0 11111111111111111111
L1 01111110010000111111
L2 01011000100010000111
L3 10100010001011101111
E 11111111111111111111
Finally, a diagnosis can be calculated ...
diagnosis = Diagnosis.new(actop)
DiagnosisOutput.screen(diagnosis, {:sort => :ochiai}, :ochiai, :jaccard)
... and shown. The similarity coefficients, e.g. :ochiai, :jaccard, etc., can be found in the file sfl_similarity.rb
| :ochiai | :jaccard |
C0 | 1.0 | 1.0 |
C1 | 1.0 | 1.0 |
C2 | 1.0 | 1.0 |
L0 | 1.0 | 1.0 |
C3 | 0.975 | 0.95 |
L1 | 0.806 | 0.65 |
L2 | 0.707 | 0.5 |
C4 | 0.387 | 0.15 |
L3 | 0.387 | 0.15 |