Supporting Defect Causal Analysis in Practice with Cross-Company Data on Causes of Requirements Engineering Problems
This work addresses the problem of reducing effort in defect analysis for software engineering practitioners, but it is incremental as it builds on existing DCA practices with new data integration.
The paper tackled the challenge of supporting Defect Causal Analysis (DCA) in software processes by proposing an approach using cross-company data from 74 Brazilian organizations, which received positive feedback in evaluations and was considered helpful for determining main causes.
[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.