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Repository for SPLC 2019 Challenge Solution : t-wise Coverage by Uniform Sampling

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Important Note

This repository for Smarch deprecated. Please go to https://github.com/jeho-oh/Smarch for the latest version.

Smarch_t_wise

This is the repository for SPLC 2019 challenge solution paper: "t-wise Coverage by Uniform Sampling"

Abstract:

A gigantic configuration space has over a trillion (10^12) configurations. Efficiently testing gigantic configuration spaces of Software Product Lines(SPLs) requires a sampling algorithm that is both scalable and provides good t-wise coverage. The 2019 SPLC Sampling Challenge provides real-world gigantic feature models and asks for a t-wise sampling algorithm that can work for those models.

We evaluate t-wise coverage with one of the provided gigantic feature models using the Smarch algorithm, that uniformly samples SPL configurations. While uniform sampling alone is not enough to produce 100% 1-wise and 2-wise coverage, we use standard probabilistic analysis to explain our experimental results and to conjecture how uniform sampling may enhance the scalability of existing t-wise sampling algorithms.

Repository structure

This repository has following structure.

  • Financial_2018_05_09: Data and results for FinancialServices01 product line, version 2018_05_09
  • Kclause_Smarch: Repository for the Smarch sampling tool
  • src: Source code used for evaluation

Further details are in the README.md file of each directory.

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