244,626 research outputs found
Towards guidelines for building a business case and gathering evidence of software reference architectures in industry
Background: Software reference architectures are becoming widely adopted by organizations that need to support the design and maintenance of software applications of a shared domain. For organizations that plan to adopt this architecture-centric approach, it becomes fundamental to know the return on investment and to understand how software reference architectures are designed, maintained, and used. Unfortunately, there is little evidence-based support to help organizations with these challenges.
Methods: We have conducted action research in an industry-academia collaboration between the GESSI research group and everis, a multinational IT consulting firm based in Spain.
Results: The results from such collaboration are being packaged in order to create guidelines that could be used in similar contexts as the one of everis. The main result of this paper is the construction of empirically-grounded guidelines that support organizations to decide on the adoption of software reference architectures and to gather evidence to improve RA-related practices.
Conclusions: The created guidelines could be used by other organizations outside of our industry-academia collaboration. With this goal in mind, we describe the guidelines in detail for their use.Peer ReviewedPostprint (published version
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
Data DNA: The Next Generation of Statistical Metadata
Describes the components of a complete statistical metadata system and suggests ways to create and structure metadata for better access and understanding of data sets by diverse users
Cloud engineering is search based software engineering too
Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; âSBSE in the cloudâ. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of âSBSE for the cloudâ, formulating cloud computing challenges in ways that can be addressed using SBSE
An evaluation of the effectiveness of the Minnesota Safety Grant Program
Includes bibliographical references
Recommended from our members
Information Flow to Front-line Employees
This research is a case study within a large bureaucracy; the physical plant operations of a Tier-one university in the United States. The organization of study received low scores for internal communication in their all-employee surveys in 2012 and 2014 and was cited for âlack of information flow to front-line employeesâ in a peer audit conducted in 2011. Root causes for these deficiencies are investigated through (1) Activity Theory analysis, (2) Leader-Member Exchange Theory and (3) linear regression analysis of all-employee survey data. No formal initiatives addressing internal communications had been initiated when this study was launched so the research was used to identify areas for improvement within the organization.Human Dimensions of Organization
Can We Agree on What Robots Should be Allowed to Do? An Exercise in Rule Selection for Ethical Care Robots
Future Care Robots (CRs) should be able to balance a patientâs, often conflicting, rights without ongoing supervision. Many of the trade-offs faced by such a robot will require a degree of moral judgment. Some progress has been made on methods to guarantee robots comply with a predefined set of ethical rules. In contrast, methods for selecting these rules are lacking. Approaches departing from existing philosophical frameworks, often do not result in implementable robotic control rules. Machine learning approaches are sensitive to biases in the training data and suffer from opacity. Here, we propose an alternative, empirical, survey-based approach to rule selection. We suggest this approach has several advantages, including transparency and legitimacy. The major challenge for this approach, however, is that a workable solution, or social compromise, has to be found: it must be possible to obtain a consistent and agreed-upon set of rules to govern robotic behavior. In this article, we present an exercise in rule selection for a hypothetical CR to assess the feasibility of our approach. We assume the role of robot developers using a survey to evaluate which robot behavior potential users deem appropriate in a practically relevant setting, i.e., patient non-compliance. We evaluate whether it is possible to find such behaviors through a consensus. Assessing a set of potential robot behaviors, we surveyed the acceptability of robot actions that potentially violate a patientâs autonomy or privacy. Our data support the empirical approach as a promising and cost-effective way to query ethical intuitions, allowing us to select behavior for the hypothetical CR
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