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Supporting defect causal analysis in practice with cross-company data on causes of requirements engineering problems

: Kalinowski, M.; Curty, P.; Paes, A.; Ferreira, A.; Spinola, R.; Fernandez, D.M.; Felderer, M.; Wagner, S.


Juristo, N. ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society; Association for Computing Machinery -ACM-:
IEEE/ACM 39th International Conference on Software Engineering: Software engineering in practice track, ICSE-SEIP 2017 : 20-28 May 2017, Buenos Aires, Argentina; Proceedings
Institute of Electrical and Electronics Engineers Inc., 2017
ISBN: 978-1-5386-2717-4
ISBN: 978-1-5386-2718-1
International Conference on Software Engineering (ICSE) <39, 2017, Buenos Aires>
Fraunhofer Project Center at UFBA ()

[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.