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Fast search (sampling) technique for search-based software engineering problems
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LICENSE update Feb 1, 2017


Fast search (sampling) technique for search-based software engineering problems


This repo concludes experiments for paper "Sampling as a Baseline Optimizer for Search-based Software Engineering". SWAY is a sampling technique for solving search-based software engineering problems. For more information, please check out our paper!

Folders Organaization

  • Algorithms: source code for different optimizers (NSGA-II, SATIBEA and SWAY)
  • Benchmarks: source code for models tested in the paper.
  • Experiments: entrance for different experiements
  • Metrics: source code for measuring results (See Section 5.3 of our paper)

Other files

  • .gitignore: untracked files in this repo
  • LICENSE: the MIT license
  • We are assuming that current project path has been added to PYTHONPATH. If not, please run this script.
  • If you include this file inside main function, program will enter debug mode when error arises.
  • including auxiliary functions to plot results

Run experiments

To run the experiments, one should go to Folder "Experiments". Each file there contains one experiements. For example, to run NSGA-II for POM3 mode, one should execute

# jump to project folder first
cd Experiments

In this repo, godview = GroundTruth. Project was developed under python2.7. Python3 should be compatible but not tested.

All results are piped to one folder tse_rs. Please make sure you've created such folder within execute path.

To get multi-objective metrics(HV,GD,PFS or GS), go to "Metrics" and run

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