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< < | 1. Download software from https://git.io/MLSA 2. Run mlsapath.bash in the mlsa folder (adds mlcg.py to PATH) with command: $source mlsapath.bash 3. On the terminal, run the mlcg command with the desired folder or programs as arguments: $mlcg.py [list of args] . A good way to test if your MLSA installation is operating correctly is to test the code on the test programs in the test folder 1. Run the command: $python testcode.py 0 2. A document titled 'stats' will show if there are any differences between the code you generated and the ideal code 3. Delete the generated code by running the command: $python testcode.py -1 2. Repeat with numbers 1-5 | |||||||
> > | On the terminal, run the mlcg command with the desired folder or programs as arguments:
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< < | 1. Copy all the source files to be analyzed into the mlsa/bin/ folder (including additional .h and .c files) | |||||||
> > | 1. Download software from https://git.io/MLSA | |||||||
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< < | 2. On the terminal, run the command: | |||||||
> > | 2. Run mlsapath.bash in the mlsa folder (adds mlcg.py to PATH) with command: $source mlsapath.bash | |||||||
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< < | ./mlsa_run [list of args] | |||||||
> > | 3. On the terminal, run the mlcg command with the desired folder or programs as arguments: $mlcg.py [list of args] | |||||||
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< < | list of args is the set of source programs to be combined in the final call graph. | |||||||
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> > | A good way to test if your MLSA installation is operating correctly is to test the code on the test programs in the test folder 1. Run the command: $python testcode.py 0 2. A document titled 'stats' will show if there are any differences between the code you generated and the ideal code 3. Delete the generated code by running the command: $python testcode.py -1 2. Repeat with numbers 1-5 | |||||||
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ExecutionTo run the software: 1. Copy all the source files to be analyzed into the mlsa/bin/ folder (including additional .h and .c files) 2. On the terminal, run the command: ./mlsa_run [list of args]
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