32,501 research outputs found
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
Towards Guidelines for Preventing Critical Requirements Engineering Problems
Context] Problems in Requirements Engineering (RE) can lead to serious
consequences during the software development lifecycle. [Goal] The goal of this
paper is to propose empirically-based guidelines that can be used by different
types of organisations according to their size (small, medium or large) and
process model (agile or plan-driven) to help them in preventing such problems.
[Method] We analysed data from a survey on RE problems answered by 228
organisations in 10 different countries. [Results] We identified the most
critical RE problems, their causes and mitigation actions, organizing this
information by clusters of size and process model. Finally, we analysed the
causes and mitigation actions of the critical problems of each cluster to get
further insights into how to prevent them. [Conclusions] Based on our results,
we suggest preliminary guidelines for preventing critical RE problems in
response to context characteristics of the companies.Comment: Proceedings of the 42th Euromicro Conference on Software Engineering
and Advanced Applications, 201
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
How do medical researchers make causal inferences?
Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it. These aspects are widely cited in the literature on epidemiological inference as justifying an inference to a causal claim, but the epistemological basis of the Hill aspects is not understood. We offer an explanatory coherentist interpretation, explicated by Thagard's ECHO model of explanatory coherence. The ECHO model captures the complexity of epidemiological inference and provides a tractable model for inferring disease causation. We apply this model to three cases: the inference of a causal connection between the Zika virus and birth defects, the classic inference that smoking causes cancer, and John Snowâs inference about the cause of cholera
Are Delayed Issues Harder to Resolve? Revisiting Cost-to-Fix of Defects throughout the Lifecycle
Many practitioners and academics believe in a delayed issue effect (DIE);
i.e. the longer an issue lingers in the system, the more effort it requires to
resolve. This belief is often used to justify major investments in new
development processes that promise to retire more issues sooner.
This paper tests for the delayed issue effect in 171 software projects
conducted around the world in the period from 2006--2014. To the best of our
knowledge, this is the largest study yet published on this effect. We found no
evidence for the delayed issue effect; i.e. the effort to resolve issues in a
later phase was not consistently or substantially greater than when issues were
resolved soon after their introduction.
This paper documents the above study and explores reasons for this mismatch
between this common rule of thumb and empirical data. In summary, DIE is not
some constant across all projects. Rather, DIE might be an historical relic
that occurs intermittently only in certain kinds of projects. This is a
significant result since it predicts that new development processes that
promise to faster retire more issues will not have a guaranteed return on
investment (depending on the context where applied), and that a long-held truth
in software engineering should not be considered a global truism.Comment: 31 pages. Accepted with minor revisions to Journal of Empirical
Software Engineering. Keywords: software economics, phase delay, cost to fi
A Path to Implement Precision Child Health Cardiovascular Medicine.
Congenital heart defects (CHDs) affect approximately 1% of live births and are a major source of childhood morbidity and mortality even in countries with advanced healthcare systems. Along with phenotypic heterogeneity, the underlying etiology of CHDs is multifactorial, involving genetic, epigenetic, and/or environmental contributors. Clear dissection of the underlying mechanism is a powerful step to establish individualized therapies. However, the majority of CHDs are yet to be clearly diagnosed for the underlying genetic and environmental factors, and even less with effective therapies. Although the survival rate for CHDs is steadily improving, there is still a significant unmet need for refining diagnostic precision and establishing targeted therapies to optimize life quality and to minimize future complications. In particular, proper identification of disease associated genetic variants in humans has been challenging, and this greatly impedes our ability to delineate gene-environment interactions that contribute to the pathogenesis of CHDs. Implementing a systematic multileveled approach can establish a continuum from phenotypic characterization in the clinic to molecular dissection using combined next-generation sequencing platforms and validation studies in suitable models at the bench. Key elements necessary to advance the field are: first, proper delineation of the phenotypic spectrum of CHDs; second, defining the molecular genotype/phenotype by combining whole-exome sequencing and transcriptome analysis; third, integration of phenotypic, genotypic, and molecular datasets to identify molecular network contributing to CHDs; fourth, generation of relevant disease models and multileveled experimental investigations. In order to achieve all these goals, access to high-quality biological specimens from well-defined patient cohorts is a crucial step. Therefore, establishing a CHD BioCore is an essential infrastructure and a critical step on the path toward precision child health cardiovascular medicine
On Evidence-based Risk Management in Requirements Engineering
Background: The sensitivity of Requirements Engineering (RE) to the context
makes it difficult to efficiently control problems therein, thus, hampering an
effective risk management devoted to allow for early corrective or even
preventive measures. Problem: There is still little empirical knowledge about
context-specific RE phenomena which would be necessary for an effective
context- sensitive risk management in RE. Goal: We propose and validate an
evidence-based approach to assess risks in RE using cross-company data about
problems, causes and effects. Research Method: We use survey data from 228
companies and build a probabilistic network that supports the forecast of
context-specific RE phenomena. We implement this approach using spreadsheets to
support a light-weight risk assessment. Results: Our results from an initial
validation in 6 companies strengthen our confidence that the approach increases
the awareness for individual risk factors in RE, and the feedback further
allows for disseminating our approach into practice.Comment: 20 pages, submitted to 10th Software Quality Days conference, 201
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