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Master's Thesis

A Systematic Literature Review Towards a Representative Feature-Model Benchmark

Vincenzo Francesco Brancaccio, October 2022,

Ulm University, Institute of Software Engineering and Programming Languages

This repository contains the artifacts from the systematic literature review in three folders:

fmb_publications

rep_papers.xlsx with the publications possibly relevant for our feature-model benchmark.

slr

data_extraction_form.xlsx with 3,382 documented papers during SLR database search.

bw_snowballing.xlsx with publications found during backward snowballing.

fw_snowballing.xlsx with publications found during forward snowballing.

slr_papers.xlsx with all 398 papers selected for our SLR analysis.

DEF_readme.txt with information about what the columns in data_extraction_form.xlsx contain.

Search_Metadata.txt with information about when and how the database search was conducted.

slr_analysis

slr_analysis.xlsx with analyses on the SLR papers in 4 different tables. Table 1 with the SLR papers and analysis on feature model publication and solvers. Table 2 contains more detailed statistics and diagrams on solvers used to evaluate feature models. In table 3 are analyses on feature model characteristics like domains, numbers of features and cross-tree constraints. Table 4 contains SLR papers and their sources of feature models with consistent numbering (i.e., the numbers in the Master's Thesis like A228 for paper 228, consistent for SLR papers with the numbering in slr_paper.xlsx) and analyses on sources of feature models.

Solver_analysis_remarks.txt with additional information about how usage of solvers was analysed.

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