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

摘要

[Context] Defect Causal Analysis (DCA) represents an efficient practice toimprove software processes. While knowledge on cause-effect relations ishelpful to support DCA, collecting cause-effect data may require significanteffort and time. [Goal] We propose and evaluate a new DCA approach that usescross-company data to support the practical application of DCA. [Method] Wecollected cross-company data on causes of requirements engineering problemsfrom 74 Brazilian organizations and built a Bayesian network. Our DCA approachuses the diagnostic inference of the Bayesian network to support DCA sessions.We evaluated our approach by applying a model for technology transfer toindustry 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 andthe cross-company data was considered helpful for determining main causes.[Conclusions] Our results strengthen our confidence in that supporting DCA withcross-company data is promising and should be further investigated.