@inproceedings{BifflKESW14,
  author    = {Stefan Biffl and
               Marcos Kalinowski and
               Fajar J. Ekaputra and
               Estefan{\'{\i}}a Serral and
               Dietmar Winkler},
  title     = {Building Empirical Software Engineering Bodies of Knowledge with Systematic
               Knowledge Engineering},
  abstract  = {[Context] Empirical software engineering (EMSE) researchers conduct systematic literature reviews (SLRs) to build bodies of knowledge (BoKs). Unfortunately, valuable knowledge collected in the SLR process is publicly available only to a limited extent, which considerably slows down building BoKs incrementally. [Objective] In this paper, we introduce the Systematic Knowledge Engineering (SKE) process to support building up BoKs from empirical studies efficiently. [Method] SKE is based on the SLR process and on Knowledge Engineering (KE) practices to provide a Knowledge Base (KB) with semantic technologies that enable reusing intermediate data extraction results and querying of empirical evidence. We evaluated SKE by building a software inspection EMSE BoK KB from knowledge acquired by controlled experiments. We elicited relevant queries from EMSE researchers and systematically integrated information from 30 representative research papers into the KB. [Results] The resulting KB was effective in answering the queries, enabling knowledge reuse for analyses beyond the results from the SLR process. [Conclusion] SKE showed promising results in the software inspection context and should be evaluated in other contexts for building EMSE BoKs faster. Copyright © 2014 by Knowledge Systems Institute Graduate School.},			   
  booktitle = {The 26th International Conference on Software Engineering and Knowledge
               Engineering, Hyatt Regency, Vancouver, BC, Canada, July 1-3, 2013.},
  note      = {<font color="red">Best Paper Candidate (invited for journal extension)!</font>},  			   
  pages     = {552-559},
  year      = {2014},
  urlAuthor_version       = {http://www.inf.puc-rio.br/~kalinowski/publications/BifflKESW14.pdf}
}

{"_id":"fNCret6CGWrgKgSn4","bibbaseid":"biffl-kalinowski-ekaputra-serral-winkler-buildingempiricalsoftwareengineeringbodiesofknowledgewithsystematicknowledgeengineering-2014","downloads":0,"creationDate":"2017-08-28T19:58:01.554Z","title":"Building Empirical Software Engineering Bodies of Knowledge with Systematic Knowledge Engineering","author_short":["Biffl, S.","Kalinowski, M.","Ekaputra, F. J.","Serral, E.","Winkler, D."],"year":2014,"bibtype":"inproceedings","biburl":"https://bibbase.org/network/files/KuRSiZJF8A6EZiujE","bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Stefan"],"propositions":[],"lastnames":["Biffl"],"suffixes":[]},{"firstnames":["Marcos"],"propositions":[],"lastnames":["Kalinowski"],"suffixes":[]},{"firstnames":["Fajar","J."],"propositions":[],"lastnames":["Ekaputra"],"suffixes":[]},{"firstnames":["Estefanía"],"propositions":[],"lastnames":["Serral"],"suffixes":[]},{"firstnames":["Dietmar"],"propositions":[],"lastnames":["Winkler"],"suffixes":[]}],"title":"Building Empirical Software Engineering Bodies of Knowledge with Systematic Knowledge Engineering","abstract":"[Context] Empirical software engineering (EMSE) researchers conduct systematic literature reviews (SLRs) to build bodies of knowledge (BoKs). Unfortunately, valuable knowledge collected in the SLR process is publicly available only to a limited extent, which considerably slows down building BoKs incrementally. [Objective] In this paper, we introduce the Systematic Knowledge Engineering (SKE) process to support building up BoKs from empirical studies efficiently. [Method] SKE is based on the SLR process and on Knowledge Engineering (KE) practices to provide a Knowledge Base (KB) with semantic technologies that enable reusing intermediate data extraction results and querying of empirical evidence. We evaluated SKE by building a software inspection EMSE BoK KB from knowledge acquired by controlled experiments. We elicited relevant queries from EMSE researchers and systematically integrated information from 30 representative research papers into the KB. [Results] The resulting KB was effective in answering the queries, enabling knowledge reuse for analyses beyond the results from the SLR process. [Conclusion] SKE showed promising results in the software inspection context and should be evaluated in other contexts for building EMSE BoKs faster. Copyright © 2014 by Knowledge Systems Institute Graduate School.","booktitle":"The 26th International Conference on Software Engineering and Knowledge Engineering, Hyatt Regency, Vancouver, BC, Canada, July 1-3, 2013.","note":"<font color=\"red\">Best Paper Candidate (invited for journal extension)!</font>","pages":"552-559","year":"2014","urlauthor_version":"http://www.inf.puc-rio.br/~kalinowski/publications/BifflKESW14.pdf","bibtex":"@inproceedings{BifflKESW14,\r\n author = {Stefan Biffl and\r\n Marcos Kalinowski and\r\n Fajar J. Ekaputra and\r\n Estefan{\\'{\\i}}a Serral and\r\n Dietmar Winkler},\r\n title = {Building Empirical Software Engineering Bodies of Knowledge with Systematic\r\n Knowledge Engineering},\r\n abstract = {[Context] Empirical software engineering (EMSE) researchers conduct systematic literature reviews (SLRs) to build bodies of knowledge (BoKs). Unfortunately, valuable knowledge collected in the SLR process is publicly available only to a limited extent, which considerably slows down building BoKs incrementally. [Objective] In this paper, we introduce the Systematic Knowledge Engineering (SKE) process to support building up BoKs from empirical studies efficiently. [Method] SKE is based on the SLR process and on Knowledge Engineering (KE) practices to provide a Knowledge Base (KB) with semantic technologies that enable reusing intermediate data extraction results and querying of empirical evidence. We evaluated SKE by building a software inspection EMSE BoK KB from knowledge acquired by controlled experiments. We elicited relevant queries from EMSE researchers and systematically integrated information from 30 representative research papers into the KB. [Results] The resulting KB was effective in answering the queries, enabling knowledge reuse for analyses beyond the results from the SLR process. [Conclusion] SKE showed promising results in the software inspection context and should be evaluated in other contexts for building EMSE BoKs faster. Copyright © 2014 by Knowledge Systems Institute Graduate School.},\t\t\t \r\n booktitle = {The 26th International Conference on Software Engineering and Knowledge\r\n Engineering, Hyatt Regency, Vancouver, BC, Canada, July 1-3, 2013.},\r\n note = {<font color=\"red\">Best Paper Candidate (invited for journal extension)!</font>}, \t\t\t \r\n pages = {552-559},\r\n year = {2014},\r\n urlAuthor_version = {http://www.inf.puc-rio.br/~kalinowski/publications/BifflKESW14.pdf}\r\n}\r\n\r\n","author_short":["Biffl, S.","Kalinowski, M.","Ekaputra, F. 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