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analysis_plan
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README.md
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conference_slides.pdf
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README.md

Learning from Difference: An Automated Approach for Learning Family Models from Software Product Lines

This folder contains the laboratory package from the paper entitled Learning from difference: An automated approach for learning family models from software product lines that has been published as full paper in the research track of the 23rd International Systems and Software Product Line Conference (SPLC 2019).

The paper is available for download at https://doi.org/10.1145/3336294.3336307

This repository is organized as follows:

In the root folder FFSM_diff, we have a Java project that can be opened using the Eclipse IDE and JDK version 1.8.

In folder FFSM_diff/lib, we find some of the Java libraries used in our Eclipse project. These include FeatureIDE and Apache Commons Math. Other libraries, such as LearnLib are included using Apache Maven.

In folder FFSM_diff/src, we find the source-code of the FFSM_Diff project. Essentially, there is one main package: uk.le.ac, that includes code artifacts we developed in our study inspired by the ideas of the work of Fragal et al. on Featured Finite State Machines.

In experiments/splc2019/data, you find the data used in the experiment of this paper. This includes the FM, FSM and FFSM models. PNG files of the state machines and feature models are available as well.

In experiments/splc2019/data/learningFFSMs.Rproj, there is a RStudio project for the experiment data.

The experiments/splc2019/data/scripts/run_.py files are the test scripts that we designed run our experiments for recovering the complete FFSM of each SPL.

The experiments/splc2019/data/scripts/run__pairs.py files are the test scripts that we designed run our experiments for learning FFSMs from all pairs of product FSMs.

For more details about the scripts used in this study, please check the folder experiments/splc2019/analysis_plan.

To run the learnFFSM.jar program, use the following parameters:

 usage: LearnFFSM
  -clean <arg>   Simplify FFSM labels
  -fm <arg>      Feature model
  -fref <arg>    FFSM reference
  -h             Help menu
  -mref <arg>    Mealy reference
  -out <arg>     Output file
  -updt <arg>    Mealy update

If you have any questions, feel free to contact me via [email protected]