scholarly journals The Software Engineering Silver Bullet Conundrum

IEEE Software ◽  
2008 ◽  
Vol 25 (2) ◽  
pp. 18-19 ◽  
Author(s):  
Daniel M. Berry
Keyword(s):  
Software Engineering ◽  
Silver Bullet

No Silver Bullet: Software Engineering Reloaded

IEEE Software ◽  
2008 ◽  
Vol 25 (1) ◽  
pp. 91-94 ◽  
Author(s):  
Steven Fraser ◽  
Dennis Mancl
Keyword(s):  
Software Engineering ◽  
Silver Bullet

Object-Oriented Software Reuse in Business Systems

2011 ◽  
pp. 2855-2861
Author(s):  
Daniel Brandon Jr.
Keyword(s):  
Software Engineering ◽  
Software Reuse ◽  
Object Oriented ◽  
Business Systems ◽  
Holy Grail ◽  
Code Reuse ◽  
The Future ◽  
Architectural Principles ◽  
Silver Bullet

“Reuse [software] engineering is a process where a technology asset is designed and developed following architectural principles, and with the intent of being reused in the future” (Bean, 1999). “If programming has a Holy Grail, widespread code reuse is it with a silver bullet. While IT has made and continues to make laudable progress in our reuse, we never seem to make great strides in this area” (Grinzo, 1998). “The quest for that Holy Grail has taken many developers over many years down unproductive paths” (Bowen, 1997). This article is an overview of software reuse methods, particularly object oriented, that have been found effective in business systems over the years.

Object-Oriented Software Reuse in Business Systems

2005 ◽  
pp. 2156-2160
Author(s):  
Daniel Brandon
Keyword(s):  
Software Engineering ◽  
Software Reuse ◽  
Object Oriented ◽  
Wide Spread ◽  
Business Systems ◽  
Holy Grail ◽  
Code Reuse ◽  
The Future ◽  
Architectural Principles ◽  
Silver Bullet

“Reuse [software] engineering is a process where a technology asset is designed and developed following architectural principles, and with the intent of being reused in the future” (Bean, 1999). “If programming has a Holy Grail, wide-spread code reuse is it with a silver bullet. While IT has made and continues to make laudable progress in our reuse, we never seem to make great strides in this area” (Grinzo, 1998). “The quest for that Holy Grail has taken many developers over many years down unproductive paths” (Bowen, 1997). This article is an overview of software reuse methods, particularly object oriented, that have been found effective in business systems over the years.

ON ISSUES IN SOFTWARE ENGINEERING AND ARTIFICIAL INTELLIGENCE

1991 ◽  
Vol 01 (01) ◽  
pp. 9-20 ◽  
Author(s):  
C. V. RAMAMOORTHY ◽  
LUIS MIGUEL ◽  
YOUNG-CHUL SHIM
Keyword(s):  
Artificial Intelligence ◽  
Software Engineering ◽  
Database Management System ◽  
Divide And Conquer ◽  
Research Approach ◽  
Test Bed ◽  
Order Of Magnitude ◽  
Definition Of ◽  
Silver Bullet ◽  
Server Architecture

Software engineering (SE) needs to make substantial breakthroughs in many different areas to allow order of magnitude improvements in software development times, software quality, and system cost. Artificial intelligence (AI) is uniquely positioned to help the SE research community in many of these areas, and we examine issues in AI for SE research. Given the fuzzy definition of AI, we provide a list of AI techniques to identify how much AI there is in specific AI for SE research. We recommend using the divide and conquer approach for SE automation and provide criteria for dividing the SE problems. We provide a vision of the future CASE environment, a knowledge and database management system at the center in a client-server architecture, and argue that it constitutes an ideal test-bed for research in AI for SE. We recommend an AI for SE research approach that includes dividing the problem up, using protocol analysis, implementing on a realistic CASE environment, and evaluating in industrial settings. We give criteria to evaluate applications of AI to SE including generality, scalability, and combinability. We conclude that AI will help SE to make slow and steady progress, but that it constitutes no silver bullet.

scholarly journals Agile Workplace Innovation

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Thomas Papke ◽  
Dirk Nicolas Wagner
Keyword(s):  
Content Analysis ◽  
Project Management ◽  
Software Engineering ◽  
Significant Influence ◽  
Qualitative Content Analysis ◽  
Suitable Method ◽  
Agile Methods ◽  
Complex Adaptive ◽  
Workplace Innovation ◽  
Silver Bullet

Agile methods, such as Scrum, first re-invented project management in software engineering. It then quickly spread into all kinds of areas and industries. Scrum is “a framework within which people can address complex adaptive problems” (Schwaber and Sutherland 2013), and it changes the way work is done. It can be shown that Scrum is a suitable method to support Workplace Innovation. The concept of Workplace Innovation, described here with the ‘Fifth Element Concept’ (Totterdill 2015), comprises practices that empower and enable employees and that are beneficial for organisations. To obtain an understanding of how Scrum supports Workplace Innovation, guided interviews were conducted and analysed. Five practitioners of Scrum were interviewed, and the analysis was carried out using Mayring’s qualitative content analysis with an inductive coding. This article aims to gain insights into how and where Scrum can support Workplace Innovation, and what other factors have a significant influence. Scrum may not be a silver bullet, but it can be instrumental in supporting many elements of Workplace Innovation.

Abstraction: An alternative neurocognitive account of recognition, prediction, and decision making

2020 ◽  
Vol 43 ◽  
Author(s):  
Valerie F. Reyna ◽  
David A. Broniatowski
Keyword(s):  
Decision Making ◽  
Software Engineering ◽  
Systems Engineering ◽  
Evidence Based ◽  
Extensive Literature ◽  
Trace Theory ◽  
Fuzzy Trace Theory ◽  
Abstract Representations ◽  
Technical Systems

Abstract Gilead et al. offer a thoughtful and much-needed treatment of abstraction. However, it fails to build on an extensive literature on abstraction, representational diversity, neurocognition, and psychopathology that provides important constraints and alternative evidence-based conceptions. We draw on conceptions in software engineering, socio-technical systems engineering, and a neurocognitive theory with abstract representations of gist at its core, fuzzy-trace theory.

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