{"title":"“GenTree:使用决策树学习可配置软件的交互”的工件","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":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"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\":null,\"pages\":null},\"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}","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}
Artifact for "GenTree: Using Decision Trees to Learn Interactions for Configurable Software"
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.
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