{"title":"Artifact for \"GenTree: Using Decision Trees to Learn Interactions for Configurable Software\"","authors":"KimHao Nguyen, Thanhvu Nguyen","doi":"10.1109/ICSE-Companion52605.2021.00076","DOIUrl":null,"url":null,"abstract":"This document describes the artifact package accompanying the ICSE'21 paper \"GenTree: Using Decision Trees to Learn Interactions for Configurable Software\". The artifact includes GenTree source code, pre-built binaries, benchmark program specifications, and scripts to replicate the data presented in the paper. Furthermore, GenTree is applicable to new programs written in supported languages (C, C++, Python, Perl, Ocaml), or can be extended to support new languages easily. GenTree implementation is highly modular and optimized, hence, it can also be used as a framework for developing and testing new interaction inference algorithms. We hope the artifact will be useful for researchers who are interested in interaction learning, especially iterative and data-driven approaches.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSE-Companion52605.2021.00076","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This document describes the artifact package accompanying the ICSE'21 paper "GenTree: Using Decision Trees to Learn Interactions for Configurable Software". The artifact includes GenTree source code, pre-built binaries, benchmark program specifications, and scripts to replicate the data presented in the paper. Furthermore, GenTree is applicable to new programs written in supported languages (C, C++, Python, Perl, Ocaml), or can be extended to support new languages easily. GenTree implementation is highly modular and optimized, hence, it can also be used as a framework for developing and testing new interaction inference algorithms. We hope the artifact will be useful for researchers who are interested in interaction learning, especially iterative and data-driven approaches.