Artificial Intelligence in Software Engineering: Current Developments and Future Prospects

Artificial Intelligence in Software Engineering: Current Developments and Future Prospects

Farid Meziane (University of Salford, UK) and Sunil Vadera (University of Salford, UK)
DOI: 10.4018/978-1-60566-758-4.ch014
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Abstract

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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Use Of Ai In Planning And Project Effort Estimation

Good project planning involves many aspects: staff need to be assigned to tasks in a way that takes account of their experience and ability, the dependencies between tasks need to be determined, times of tasks need to be estimated in a way that meets the project completion date and the project plan will inevitably need revision as it progresses. AI has been proposed for most phases of planning software development projects, including assessing feasibility, estimation of cost and resource requirements, risk assessment and scheduling. This section provides pointers to some of the proposed uses of knowledge based systems, genetic algorithms, neural networks and case based reasoning, in project planning and summarizes their effectiveness.

Knowledge Based Systems

It seems reasonable to assume that as we gain experience with projects, our ability to plan new projects improves. There have been several studies that adopt this assumption and aim to capture this experience in a Knowledge Based System (KBS) and attempt to utilise it for planning future software development projects. Sathi, Fox & Greenberg (1985) argue that a well defined representation scheme, with clear semantics for the concepts associated with project planning, such as activity, causation, and time, is essential if attempts to utilise KBS for project planning are to succeed. Hence, they develop a representation scheme and theory based on a frame based language, known as SRL (Wright, Fox, & Adam, 1984). Their theory includes a language for representing project goals, milestones, activities, states, and time, and has all the nice properties one expects, such as completeness, clarity and preciseness. Surprisingly, this neat frame based language and the semantic primitives they develop have been overlooked by others and appear not to have been adopted since their development. Similarly, other proposals that aim to utilise a KBS approach for project management, such as the use of production rules and associative networks (Boardman & Marshall, 1990), which seemed promising at the time have not been widely adopted. When considering whether to adopt a KBS approach, the cost of representing the knowledge seems high and unless this can be done at a level of abstraction that allows reuse, one can imagine that it is unattractive to software developers who are keen and under pressure to commence their projects without delay.

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