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Supporting Defect Causal Analysis in Practice with Cross-company Data on Causes of Requirements Engineering Problems

, , , , , , , und . Proceedings of the 39th International Conference on Software Engineering: Software Engineering in Practice Track, Seite 223--232. Piscataway, NJ, USA, IEEE Press, (2017)

Zusammenfassung

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.

Beschreibung

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

Links und Ressourcen

DOI:
10.1109/ICSE-SEIP.2017.14
URL:
Weitere Links:
BibTeX-Schlüssel:
Kalinowski:2017:SDC:3103112.3103141
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