DMCA
Clarity guided belief revision for domain knowledge recovery in legacy systems (2000)
| Venue: | In: Proceedings of the 12th International Conference on Software Engineering and Knowledge Engineering |
| Citations: | 4 - 1 self |
BibTeX
@INPROCEEDINGS{Li00clarityguided,
author = {Yang Li and Hongji Yang and William Chu},
title = {Clarity guided belief revision for domain knowledge recovery in legacy systems},
booktitle = {In: Proceedings of the 12th International Conference on Software Engineering and Knowledge Engineering},
year = {2000},
pages = {248--255},
publisher = {Springer}
}
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Abstract
Program understanding is the process of acquiring knowledge from a computer program. Although research work utilising knowledge engineering techniques has been undertaken in this field, it is our observation that a thorough application of AI methodology has not been sufficiently explored. In this paper, we present a clarity guided belief revision approach to domain knowledge recovery in legacy software systems. Novel solutions are given to three key AI issues in the context of domain knowledge recovery from source code: knowledge representation, where concrete semantic network is separated from abstract semantic network to better accommodate uncertainty reasoning and propagation; uncertainty reasoning, which borrows ideas from confirmation theory and recasts them in the context of semantic network reasoning; heuristic search, which is designed on the principle of programming psychology. Our approach is light-weighted. It can be used stand-alone or as a complement to traditional heavy-weighted domain knowledge recovery methods.
Keyphrases
domain knowledge recovery legacy system belief revision uncertainty reasoning computer program research work program understanding key ai issue novel solution knowledge engineering technique abstract semantic network confirmation theory source code thorough application knowledge recovery belief revision approach heuristic search ai methodology semantic network reasoning legacy software system concrete semantic network knowledge representation
