2 research outputs found
Preventing Incomplete/Hidden Requirements: Reflections on Survey Data from Austria and Brazil
Many software projects fail due to problems in requirements engineering (RE).
The goal of this paper is analyzing a specific and relevant RE problem in
detail: incomplete/hidden requirements. We replicated a global family of RE
surveys with representatives of software organizations in Austria and Brazil.
We used the data to (a) characterize the criticality of the selected RE
problem, and to (b) analyze the reported main causes and mitigation actions.
Based on the analysis, we discuss how to prevent the problem. The survey
includes 14 different organizations in Austria and 74 in Brazil, including
small, medium and large sized companies, conducting both, plan-driven and agile
development processes. Respondents from both countries cited the
incomplete/hidden requirements problem as one of the most critical RE problems.
We identified and graphically represented the main causes and documented
solution options to address these causes. Further, we compiled a list of
reported mitigation actions. From a practical point of view, this paper
provides further insights into common causes of incomplete/hidden requirements
and on how to prevent this problem.Comment: in Proceedings of the Software Quality Days, 201
Supporting Defect Causal Analysis in Practice with Cross-Company Data on Causes of Requirements Engineering Problems
[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.Comment: 10 pages, 8 figures, accepted for the 39th International Conference
on Software Engineering (ICSE'17