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On this webpage, you will discover guidelines outlining the process for replicating the experiments conducted in the research paper titled "Automated Repair of Unrealisable LTL Specifications Guided by Model Counting." The paper was presented at the Genetic and Evolutionary Computation Conference (GECCO) in 2023. By following these instructions, you can recreate the same experiments and obtain results that can be compared to those in the paper. The guidelines provided here will walk you through the necessary steps required to set up the experiment environment, execute the experiments, and analyze the results.
The paper is in the following link: AuRUS (here's the pdf).
Installation Instructions
REQUIREMENTS
DOWNLOAD
You can download the tool from this link: unreal-repair.zip.
If you prefer, you can clone or fork the up-to-date repository :)
INSTALL
To compile our tool run: ant compile.
We provide an Ant script (build.xml file) and the libraries required into the lib folder.
Set JAVA_HOME environmental variable.
By default the tool will use the docker image, but you can use flag -no-docker if you have installed Strix on your computer
Notice that, in case of using -no-docker, the tool will run the script lib/strix_tlsf.sh, that will execute the binary in lib/new_strix/strix.
INSTALL DOCKER IMAGE
Follow these instructions to install the Docker image with the Strix installation.
Install Docker
Move to lib and run: docker build -t strix_image .
Run: docker-machine create default
Run: docker-machine env --shell cmd default
Running the Experiments
In the folder case-studies you can find the scripts with the specifications of each one of the case studies used in the paper.
RUN THE TOOL
The tool takes as input a specification in TLSF format.
To run our tool you have to use the script unreal-repair.sh.
For example, to run our motivating example Arbiter:
./unreal-repair.sh case-studies/arbiter/arbiter.tlsf
In our experimental evaluation we run the following command:
./unreal-repair.sh -Max=1000 -Gen=1000 -Pop=100 -k=20 -GATO=7200 -addA -out=result/arbiter/ case-studies/arbiter/arbiter.tlsf
In the example we indicate to the tool that 1000 in the maximum individuals to be generated, 100 is the population size per iteration, and assumptions can be added. The tool will save the realisable repairs in the directory that you indicate using -out flag, or by default in the same directory where is the input specification.
We can indicate to the tool what are (genuine) solutions of reference, that will be used at the end of analysis to study the quality of the learnt repairs,
by adding -ref=case-studies/arbiter/genuine/arbiter_fixed0.tlsf -ref=case-studies/arbiter/genuine/arbiter_fixed1.tlsf -ref=case-studies/arbiter/genuine/arbiter_fixed2.tlsf -ref=case-studies/arbiter/genuine/arbiter_fixed3.tlsf
REPRODUCE THE EXPERIMENTS
In order to reproduce the experimental evaluation of the paper, we provide several scripts:
run-literature.sh: it runs the case studies taken from the literature with the default configuration.
run-syntech.sh: it runs the case studies taken from the SynTech benchmark with the default configuration.
run-syntcomp.sh: it runs the case studies taken from the SyntComp benchmark with the default configuration.
run-benchmarks.sh: it runs 10 times each case study. You can use the same script to run the random evaluation.
run-sensitivity-analysis.sh: it runs the sensitivity analysis 10 times. Notice to configure script run-all-sensitivity.sh and run-all-sensitivity-syntcomp.sh first to enable/disable the different key properties that are part of the fitness function.
Reading the Results
You can download the results of our experimental evaluation from this link: Results.zip.
We ran the algorithm 10 times for each case study. All the results that we obtained are reported in the result section.
You can use script read-results.sh to extract a summary of results for each run.
Example:
./read-results.sh result/result-70-10-20/arbiter/arbiter-genuine
This command will show the results obtained for the Arbiter case study in the 10 runs with a configuration in which .7 was assigned to the realisability property, 0.1 to the syntactic distance, and .2 to the semantic distance.
Flags to Configure the GA
Our tool offers many flags to run the Genetic Algorithm under different configurations.
Use ./unreal-repair.sh [flags] input-file.tlsf
Flags:
-onlySAT: realisability checking is disabled in the fitness function. At the end the tool will check for the realisability of the candidate solutions.
-no-docker: the tool won't virtualise the realisability checking and will use the local installation of Strix.
-random: in will create Max number of individuals randomly and at the end will check for realisability. We use this flag in our comparison against random generation.
-GA_random_selector: use random selector instead of best selector.
-Max=max_num_of_individuals
-Gen=num_of_generations
-sol=THRESHOLD : it will discard solutions with fitness value smaller than the threshold.
-Pop=population_size
-COR=crossover_rate
-MR=mutation_rate : probability with which a specification is mutated.
-geneMR=gene_mutation_rate : probability with which a sub-formula (gene) is mutated.
-geneNUM=num_of_genes_to_mutate : number of mutations allowed per formula (how many sub-formulas can be mutated).
-removeGuarantees : it allows the GA to remove guarantees.
-addAssumptions : it allows the GA to add new assumptions.
-onlyInputsA : it restricts to the GA to use just input variables in the new assumptions generated.
-GPR=guarantee_preference_rate : it indicates with which probability the GA will prefer mutation guarantees from assumptions (50% default. Near 0% will focus on the assumptions. Near 100% will focus on the guarantees).
-k=bound : bound for the model counting approach.
-factors=STATUS_factor,MC_factor,SYN_factor : it is useful for defining the factors of importance of each kew property computed in the fitness.
-RTO=strix_timeout
-GATO=GA_timeout
-SatTO=sat_timeout
-MCTO=model_counting_timeout
-ref=TLSF_reference_solution: reference solutions to compare at the end of the analysis
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