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

TL;DR

This paper presents a Bayesian network-based approach using cross-company data to support defect causal analysis in requirements engineering, demonstrating its effectiveness through multiple industry evaluations.

Contribution

It introduces a novel DCA method leveraging cross-company data and Bayesian inference, validated across academic and industrial settings.

Findings

Positive feedback from industry evaluations

Cross-company data helped identify main causes

Approach shows promise for practical DCA support

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.