Supporting Defect Causal Analysis in Practice with Cross-Company Data on Causes of Requirements Engineering Problems
arXiv:1702.03851 · doi:10.1109/ICSE-SEIP.2017.14
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.
10 pages, 8 figures, accepted for the 39th International Conference on Software Engineering (ICSE'17)
References in corpus (4)
- Naming the Pain in Requirements Engineering: Contemporary Problems, Causes, and Effects in Practice
- Naming the Pain in Requirements Engineering: A Design for a Global Family of Surveys and First Results from Germany
- Naming the Pain in Requirements Engineering: Comparing Practices in Brazil and Germany
- Preventing Incomplete/Hidden Requirements: Reflections on Survey Data from Austria and Brazil