@inproceedings{KalinowskiCPFSF17,
  author    = {Marcos Kalinowski and
               Pablo Curty and
               Aline Paes and
               Alexandre Ferreira and
               Rodrigo O. Sp{\'{\i}}nola and
               Daniel M{\'{e}}ndez Fern{\'{a}}ndez and
               Michael Felderer and
               Stefan Wagner},
  title     = {Supporting Defect Causal Analysis in Practice with Cross-Company Data
               on Causes of Requirements Engineering Problems},
  abstract  = {[Context] Defect Causal Analysis (DCA) represents an efficient practice to improve software processes. While knowledge on cause-effect relations is helpful to support DCA, collecting cause-effect data may require significant effort and time. [Goal] We propose and evaluate a new DCA approach that uses cross-company data to support the practical application of DCA. [Method] We collected cross-company data on causes of requirements engineering problems from 74 Brazilian organizations and built a Bayesian network. Our DCA approach uses the diagnostic inference of the Bayesian network to support DCA sessions. We evaluated our approach by applying a model for technology transfer to industry and conducted three consecutive evaluations: (i) in academia, (ii) with industry representatives of the Fraunhofer Project Center at UFBA, and (iii) in an industrial case study at the Brazilian National Development Bank (BNDES). [Results] We received positive feedback in all three evaluations and the cross-company data was considered helpful for determining main causes. [Conclusions] Our results strengthen our confidence in that supporting DCA with cross-company data is promising and should be further investigated. © 2017 IEEE.},			   
  booktitle = {39th {IEEE/ACM} International Conference on Software Engineering:
               Software Engineering in Practice Track, {ICSE-SEIP} 2017, Buenos Aires,
               Argentina, May 20-28, 2017},
  pages     = {223-232},
  year      = {2017},
  urlAuthor_version       = {http://www.inf.puc-rio.br/~kalinowski/publications/KalinowskiCPFSF17.pdf},
  doi       = {10.1109/ICSE-SEIP.2017.14},
}

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M.","Felderer, M.","Wagner, S."],"year":2017,"bibtype":"inproceedings","biburl":"https://bibbase.org/f/2Gq6bNPQ845THHiMW/KalinowskiPapers.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Marcos"],"propositions":[],"lastnames":["Kalinowski"],"suffixes":[]},{"firstnames":["Pablo"],"propositions":[],"lastnames":["Curty"],"suffixes":[]},{"firstnames":["Aline"],"propositions":[],"lastnames":["Paes"],"suffixes":[]},{"firstnames":["Alexandre"],"propositions":[],"lastnames":["Ferreira"],"suffixes":[]},{"firstnames":["Rodrigo","O."],"propositions":[],"lastnames":["Spínola"],"suffixes":[]},{"firstnames":["Daniel","Méndez"],"propositions":[],"lastnames":["Fernández"],"suffixes":[]},{"firstnames":["Michael"],"propositions":[],"lastnames":["Felderer"],"suffixes":[]},{"firstnames":["Stefan"],"propositions":[],"lastnames":["Wagner"],"suffixes":[]}],"title":"Supporting Defect Causal Analysis in Practice with Cross-Company Data on Causes of Requirements Engineering Problems","abstract":"[Context] Defect Causal Analysis (DCA) represents an efficient practice to improve software processes. While knowledge on cause-effect relations is helpful to support DCA, collecting cause-effect data may require significant effort and time. [Goal] We propose and evaluate a new DCA approach that uses cross-company data to support the practical application of DCA. [Method] We collected cross-company data on causes of requirements engineering problems from 74 Brazilian organizations and built a Bayesian network. Our DCA approach uses the diagnostic inference of the Bayesian network to support DCA sessions. We evaluated our approach by applying a model for technology transfer to industry and conducted three consecutive evaluations: (i) in academia, (ii) with industry representatives of the Fraunhofer Project Center at UFBA, and (iii) in an industrial case study at the Brazilian National Development Bank (BNDES). [Results] We received positive feedback in all three evaluations and the cross-company data was considered helpful for determining main causes. [Conclusions] Our results strengthen our confidence in that supporting DCA with cross-company data is promising and should be further investigated. © 2017 IEEE.","booktitle":"39th IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice Track, ICSE-SEIP 2017, Buenos Aires, Argentina, May 20-28, 2017","pages":"223-232","year":"2017","urlauthor_version":"http://www.inf.puc-rio.br/~kalinowski/publications/KalinowskiCPFSF17.pdf","doi":"10.1109/ICSE-SEIP.2017.14","bibtex":"@inproceedings{KalinowskiCPFSF17,\r\n author = {Marcos Kalinowski and\r\n Pablo Curty and\r\n Aline Paes and\r\n Alexandre Ferreira and\r\n Rodrigo O. Sp{\\'{\\i}}nola and\r\n Daniel M{\\'{e}}ndez Fern{\\'{a}}ndez and\r\n Michael Felderer and\r\n Stefan Wagner},\r\n title = {Supporting Defect Causal Analysis in Practice with Cross-Company Data\r\n on Causes of Requirements Engineering Problems},\r\n abstract = {[Context] Defect Causal Analysis (DCA) represents an efficient practice to improve software processes. While knowledge on cause-effect relations is helpful to support DCA, collecting cause-effect data may require significant effort and time. [Goal] We propose and evaluate a new DCA approach that uses cross-company data to support the practical application of DCA. [Method] We collected cross-company data on causes of requirements engineering problems from 74 Brazilian organizations and built a Bayesian network. Our DCA approach uses the diagnostic inference of the Bayesian network to support DCA sessions. We evaluated our approach by applying a model for technology transfer to industry and conducted three consecutive evaluations: (i) in academia, (ii) with industry representatives of the Fraunhofer Project Center at UFBA, and (iii) in an industrial case study at the Brazilian National Development Bank (BNDES). [Results] We received positive feedback in all three evaluations and the cross-company data was considered helpful for determining main causes. [Conclusions] Our results strengthen our confidence in that supporting DCA with cross-company data is promising and should be further investigated. © 2017 IEEE.},\t\t\t \r\n booktitle = {39th {IEEE/ACM} International Conference on Software Engineering:\r\n Software Engineering in Practice Track, {ICSE-SEIP} 2017, Buenos Aires,\r\n Argentina, May 20-28, 2017},\r\n pages = {223-232},\r\n year = {2017},\r\n urlAuthor_version = {http://www.inf.puc-rio.br/~kalinowski/publications/KalinowskiCPFSF17.pdf},\r\n doi = {10.1109/ICSE-SEIP.2017.14},\r\n}\r\n\r\n","author_short":["Kalinowski, M.","Curty, P.","Paes, A.","Ferreira, A.","Spínola, R. O.","Fernández, D. 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