Discretization of Problem Domain

2021 ◽  
pp. 61-80
Author(s):  
Michael Schäfer
Keyword(s):  
Problem Domain

Mining Bug Report Repositories to Identify Significant Information for Software Bug Fixing

2021 ◽  
Author(s):  
Bancha Luaphol ◽  
Jantima Polpinij ◽  
Manasawee Kaenampornpan
Keyword(s):  
The Other ◽  
Problem Domain ◽  
Significant Information ◽  
Bug Reports ◽  
Bug Fixing ◽  
Classification Technique ◽  
Bug Report ◽  
Multiple Issues ◽  
Improved Accuracy ◽  
Software Bug

Most studies relating to bug reports aims to automatically identify necessary information from bug reports for software bug fixing. Unfortunately, the study of bug reports focuses only on one issue, but more complete and comprehensive software bug fixing would be facilitated by assessing multiple issues concurrently. This becomes a challenge in this study, where it aims to present a method of identifying bug reports at severe level from a bug report repository, together with assembling their related bug reports to visualize the overall picture of a software problem domain. The proposed method is called “mining bug report repositories”. Two techniques of text mining are applied as the main mechanisms in this method. First, classification is applied for identifying severe bug reports, called “bug severity classification”, while “threshold-based similarity analysis” is then applied to assemble bug reports that are related to a bug report at severe level. Our datasets are from three opensource namely SeaMonkey, Firefox, and Core:Layout downloaded from the Bugzilla. Finally, the best models from the proposed method are selected and compared with two baseline methods. For identifying severe bug reports using classification technique, the results show that our method improved accuracy, F1, and AUC scores over the baseline by 11.39, 11.63, and 19% respectively. Meanwhile, for assembling related bug reports using threshold-based similarity technique, the results show that our method improved precision, and likelihood scores over the other baseline by 15.76, and 9.14% respectively. This demonstrate that our proposed method may help increasing chance to fix bugs completely.

Requirements Engineering in the Problem Domain

2017 ◽  
pp. 113-134
Author(s):  
Jeremy Dick ◽  
Elizabeth Hull ◽  
Ken Jackson
Keyword(s):  
Requirements Engineering ◽  
Problem Domain

Informatics for cultural heritage instruction: an ontological framework

2019 ◽  
Vol 75 (2) ◽  
pp. 230-246 ◽  
Author(s):  
Sonia Yaco ◽  
Arkalgud Ramaprasad
Keyword(s):  
Cultural Heritage ◽  
Research Literature ◽  
Educational Process ◽  
Instructional Materials ◽  
Primary Sources ◽  
Problem Domain ◽  
Content Type ◽  
The Past ◽  
Ontological Framework ◽  
Content Semantic

PurposeThe purpose of this paper is to suggest a framework that creates a common language to enhance the connection between the domains of cultural heritage (CH) artifacts and instruction.Design/methodology/approachThe CH and instruction domains are logically deconstructed into dimensions of functions, semiotics, CH, teaching/instructional materials, agents and outcomes. The elements within those dimensions can be concatenated to create natural-English sentences that describe aspects of the problem domain.FindingsThe framework is valid using traditional social sciences content, semantic, practical and systemic validity constructs.Research limitations/implicationsThe framework can be used to map current research literature to discover areas of heavy, light and no research.Originality/valueThe framework provides a new way for CH and education stakeholders to describe and visualize the problem domain, which could allow for significant enhancements of each. Better understanding the problem domain would serve to enhance instruction informed from collections and vice versa. The educational process would have more depth due to better access to primary sources. Increased use of collections would reveal more ways through which they could be used in instruction. The framework can help visualize the past and present of the domain, and envisage its future.

scholarly journals A Positive Project Outcome: Lessons from a Non-Dominant Government University-Based Program

2020 ◽  
Vol 7 (2) ◽  
pp. 3
Author(s):  
Anne Namatasi Lutomia ◽  
Julia Bello-Bravo ◽  
John Medendorp ◽  
Barry Pittendrigh
Keyword(s):  
Decision Making ◽  
International Development ◽  
Problem Domain ◽  
Governmental Organization ◽  
Development Agency ◽  
Project Outcome ◽  
Organizational Autonomy ◽  
History Of ◽  
Shared Problem

This article explores factors contributing to a non-dominant collaboration paradigm in a partnership between a government-based international development agency and a university-based non-governmental organization. Anchored in Wood’s and Gray’s collaborative framework, this article describes how the steeply hierarchical partnership navigated the elements of collaboration – organizational autonomy; shared problem domain; interactive processes; shared rules, norms, and structures; and decision making – to produce non-dominant values and practices deriving from negotiated processes, rules, norms, and structures that produced positive collaboration outcomes. In particular, a history of prior mutually beneficial interactions emerges as a critical precondition for achieving a non-dominant collaboration in this case study’s steeply hierarchical organizational relationship, one in which egalitarianism and equal decision-making regarding the agenda and the goals of the collaboration could have been highly constrained.

An Algorithm of Parallel Programming Design Based on Problem Domain Model

2012 ◽  
pp. 130-135
Author(s):  
Xin Zhang ◽  
Guangnan Guo ◽  
Lu Bai ◽  
Yu Zhang ◽  
Hao Wang
Keyword(s):  
Parallel Programming ◽  
Domain Model ◽  
Problem Domain

An Experimental Analysis of the Effectiveness and Efficiency of Teams with Partial Problem Domain Knowledge

2011 ◽  
pp. 104-114
Author(s):  
Dinesh A. Mirchandani ◽  
Jaideep Motwani
Keyword(s):  
Knowledge Management ◽  
Experimental Analysis ◽  
Domain Knowledge ◽  
Specific Problem ◽  
Management Systems ◽  
Knowledge Management Systems ◽  
Problem Domain ◽  
Effectiveness And Efficiency ◽  
Knowledge Content ◽  
Area Of Application

Knowledge Management Systems are increasingly becoming important to both practitioners and researchers. One area of application of such systems is the formation of organizational teams with appropriate knowledge content to solve complex and novel problems. A common predicament, however, is that teams are often formed with only partial problem domain knowledge. This study examines if teams that have partial problem domain knowledge are more effective and efficient than teams that do not have specific problem domain knowledge. It finds that partial problem domain knowledge may in fact be worse than no problem domain knowledge. Several implications for researchers and practitioners are derived from this result.

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