Enhancing Software Engineering Curricula By Incorporating Open, Data-Driven Planning Methods

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.

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Artificial Intelligence in Software Engineering

Advances in Computational Intelligence and Robotics - Artificial Intelligence Applications for Improved Software Engineering Development ◽

10.4018/978-1-60566-758-4.ch014 ◽

2010 ◽

pp. 278-299 ◽

Cited By ~ 7

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.

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The Politics of Building Civic Tech

10.1332/policypress/9781529215144.003.0009 ◽

2021 ◽

pp. 69-78

Author(s):

Rob Kitchin

Keyword(s):

Open Data ◽

Urban Infrastructure ◽

Data Driven ◽

Trial And Error ◽

Institutional Landscape ◽

Control Rooms ◽

Political Trials

This chapter evaluates the technical and political trials involved in building a suite of open data tools by charting the development of the Dublin Dashboard. Building a city dashboard is a good way to gain an in-depth knowledge of how civic tech can be created using open data, and the politics and praxes involved. Like the process for creating the original city dashboard, the redevelopment of the Dublin Dashboard and production of the Cork Dashboard involved a significant amount of planning, negotiation, and trial and error. Just as these processes and institutional landscape have an effect on how a dashboard is created, the collective manufacture of dashboards reshapes institutions and their practices. How we design dashboards, and what data are included and how they are displayed, influences what knowledge is learned and how it is applied. Importantly, given that dashboards are a key means by which operators monitor urban infrastructure within control rooms, this mutability directly shapes the nature of data driven urbanism and how our cities are managed and run.

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