Observations on using empirical studies on developing a knowledge-based software engineering tool

2002 ◽  
Author(s):  
D. Redmiles
Keyword(s):  
Software Engineering ◽  
Empirical Studies ◽  
Knowledge Based

Value co-creation in the B2B context: a diagnosis of knowledge management based on multiple case studies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Andrei Bonamigo ◽  
Camila Guimarães Frech ◽  
Ana Carolina Custódio Lopes
Keyword(s):  
Empirical Studies ◽  
Structured Interviews ◽  
Content Type ◽  
Knowledge Based ◽  
Multiple Case ◽  
Multiple Case Studies ◽  
Knowledge Asymmetry ◽  
Business To Business ◽  
Practical Implications

Purpose This study aims to empirically investigate how organizations delivering services in business-to-business relations deal with the boundary paradox and knowledge asymmetry in value co-creation. Design/methodology/approach This study adopted a qualitative multiple case study strategy. Datas were gathered through 13 semi-structured interviews that were then analyzed through the content analysis. Findings The authors identified three mechanisms that organizations use to deal with the boundary paradox and two strategies to handle the knowledge asymmetry. Research limitations/implications First, no opportunities were afforded to involve more participants. Second, owning to confidentiality reasons, not all organizations provided us documents to be analyzed. Practical implications The findings guide managers in balancing the use of contracts and trust in inter-firm collaborations and fostering the learning of customers. Also, insights to protect knowledge based on the paradox of openness in value co-creation. Originality/value This study’s findings address the gap in value co-creation literature concerning the lack of empirical studies.

The Business Transformation Framework and Enterprise Architecture Framework for Managers in Business Innovation

2019 ◽  
pp. 20-49
Author(s):  
Antoine Trad
Keyword(s):  
Knowledge Management ◽  
Software Engineering ◽  
Enterprise Architecture ◽  
Global Business ◽  
Business Transformation ◽  
Knowledge Based ◽  
Architecture Framework ◽  
Enterprise Architecture Framework ◽  
Global Software Engineering ◽  
Technical Recommendations

The KMGSE offers a real-life case for detecting and processing an enterprise knowledge management model for global business transformation, knowledge management systems, global software engineering, global business engineering and enterprise architecture recurrent problems solving. This global software engineering (GSE) subsystem is a driven development model that offers a set of possible solutions in the form of architecture, method, patterns, managerial and technical recommendations, coupled with an applicable framework. The proposed executive and technical recommendations are to be applied by the business environment's knowledge officers, architects, analysts and engineers to enable solutions to knowledge-based, global software engineering paradigms' development and maintenance.

Artificial Intelligence in Software Engineering

2012 ◽  
pp. 1215-1236 ◽  
Author(s):  
Farid Meziane ◽  
Sunil Vadera
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Software Engineering ◽  
Software Development ◽  
Language Processing ◽  
Knowledge Based ◽  
Development Life Cycle ◽  
Planning Methods ◽  
Software Development Life Cycle ◽  
Processing Techniques

Artificial intelligences techniques such as knowledge based systems, neural networks, fuzzy logic and data mining have been advocated by many researchers and developers as the way to improve many of the software development activities. As with many other disciplines, software development quality improves with the experience, knowledge of the developers, past projects and expertise. Software also evolves as it operates in changing and volatile environments. Hence, there is significant potential for using AI for improving all phases of the software development life cycle. This chapter provides a survey on the use of AI for software engineering that covers the main software development phases and AI methods such as natural language processing techniques, neural networks, genetic algorithms, fuzzy logic, ant colony optimization, and planning methods.

Artificial Intelligence in Software Engineering

2010 ◽  
pp. 278-299 ◽  
Author(s):  
Farid Meziane ◽  
Sunil Vadera
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Software Engineering ◽  
Software Development ◽  
Language Processing ◽  
Knowledge Based ◽  
Development Life Cycle ◽  
Planning Methods ◽  
Software Development Life Cycle ◽  
Processing Techniques

Artificial intelligences techniques such as knowledge based systems, neural networks, fuzzy logic and data mining have been advocated by many researchers and developers as the way to improve many of the software development activities. As with many other disciplines, software development quality improves with the experience, knowledge of the developers, past projects and expertise. Software also evolves as it operates in changing and volatile environments. Hence, there is significant potential for using AI for improving all phases of the software development life cycle. This chapter provides a survey on the use of AI for software engineering that covers the main software development phases and AI methods such as natural language processing techniques, neural networks, genetic algorithms, fuzzy logic, ant colony optimization, and planning methods.

scholarly journals Three empirical studies on the agreement of reviewers about the quality of software engineering experiments

2012 ◽  
Vol 54 (8) ◽  
pp. 804-819 ◽  
Author(s):  
Barbara Ann Kitchenham ◽  
Dag I.K. Sjøberg ◽  
Tore Dybå ◽  
Dietmar Pfahl ◽  
Pearl Brereton ◽  
...  
Keyword(s):  
Software Engineering ◽  
Empirical Studies
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