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Knowledge-based software engineering : a software quality management expert system prototype

Knowledge-based software engineering : a software quality management expert system prototype
Knowledge-based software engineering : a software quality management expert system prototype

Expert system/Knowledge-based technology is a branch of the discipline of Artificial Intelligence. The purpose of this research is to examine whether this technology can be successfully applied to mainstream software engineering to improve the quality of software production. It is argued that software engineering is in crisis [1] and ES/KBS technology, though a relatively new technology, can assist in software engineering. The approach we take is to investigate the shortcomings of software engineering and outstanding characteristics of Expert/Knowledge-Based systems. Being a young discipline, software engineering provides a set of worthy research fronts amongst which is Knowledge-Based Software engineering. Reports of many successful knowledge-based systems, as applied to different domain, have encouraged us to examine software engineering issues which can be addressed by knowledge based techniques. Demands for high quality software systems are now on the increase, owing to the affordability of computer hardware created by breakthroughs in this technology. Currently, the software community cannot cope with this increased pressure. There is growing evidence to suggest that failed software systems are causing losses of revenue and competitiveness due to lack of quality.

In order to address the quality issue and incorporate the advantages of Knowledge-based technology we devised the Quality Optimiser, discussed in [2], as our initial commitment. The type of knowledge to be included in the Quality Optimiser expert system overlaps largely with the SEI's Capability Maturity Model which is one of the research avenues tackling software quality. As a result, we have decided to focus on CMM as a suitable application domain area.

University of Southampton
Karami, Daryoosh
Karami, Daryoosh

Karami, Daryoosh (1995) Knowledge-based software engineering : a software quality management expert system prototype. University of Southampton, Doctoral Thesis.

Record type: Thesis (Doctoral)

Abstract

Expert system/Knowledge-based technology is a branch of the discipline of Artificial Intelligence. The purpose of this research is to examine whether this technology can be successfully applied to mainstream software engineering to improve the quality of software production. It is argued that software engineering is in crisis [1] and ES/KBS technology, though a relatively new technology, can assist in software engineering. The approach we take is to investigate the shortcomings of software engineering and outstanding characteristics of Expert/Knowledge-Based systems. Being a young discipline, software engineering provides a set of worthy research fronts amongst which is Knowledge-Based Software engineering. Reports of many successful knowledge-based systems, as applied to different domain, have encouraged us to examine software engineering issues which can be addressed by knowledge based techniques. Demands for high quality software systems are now on the increase, owing to the affordability of computer hardware created by breakthroughs in this technology. Currently, the software community cannot cope with this increased pressure. There is growing evidence to suggest that failed software systems are causing losses of revenue and competitiveness due to lack of quality.

In order to address the quality issue and incorporate the advantages of Knowledge-based technology we devised the Quality Optimiser, discussed in [2], as our initial commitment. The type of knowledge to be included in the Quality Optimiser expert system overlaps largely with the SEI's Capability Maturity Model which is one of the research avenues tackling software quality. As a result, we have decided to focus on CMM as a suitable application domain area.

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Published date: 1995

Identifiers

Local EPrints ID: 463020
URI: http://eprints.soton.ac.uk/id/eprint/463020
PURE UUID: beb75657-bfd8-4d03-9127-b613ca6a9c2c

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Date deposited: 04 Jul 2022 20:38
Last modified: 04 Jul 2022 20:38

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Contributors

Author: Daryoosh Karami

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