Abstract
In this paper, it is emphasized that taking into consideration of imperfection of knowledge, of the team of the designers/developers, about the problem domains and environments is essential in order to develop robust software metrics and systems. In this respect, first various possible types of imperfections in knowledge are discussed and then various available formal/mathematical models for representing and handling these imperfections are discussed. The discussion of knowledge classification & representation is from computational perspective and that also within the context of software development enterprise, and not necessarily from organizational management, from library & information science, or from psychological perspectives.
Highlights
The 1930’s theoretical work in computer science proved, not just claimed, that most of the problems, human beings may encounter, will be either unsolvable or infeasible by using only computational/algorithmic means, even by the most advanced computer to be ever developed in future
One significant consequence of the theoretical work is recognition of the fact that man-machine combination is essential for attempting solutions of difficult problems
The human aspect in designing software, and software metrics has been emphasized by psychologists and others including [2]
Summary
The 1930’s theoretical work in computer science proved, not just claimed, that most of the problems, human beings may encounter, will be either unsolvable or infeasible (together to be called “difficult”) by using only computational/algorithmic means, even by the most advanced computer to be ever developed in future. The significance of human intelligence in solving complex problems through software means and the need for modeling human intelligence through AI and other relevant disciplines has appropriately been expressed by [3] Another consequence of the 1930’s theoretical work and of the emphasis on requirement of the human intelligence essentially in solving difficult problems and in developing complex software is the birth of a number of disciplines including Software Engineering (SE) and Artificial Intelligence (AI). A robust system (man or machine) has to be an intelligent system in the sense that it should be able to find an optimal, if not the best, solution when the knowledge of the problem domain is uncertain/incomplete/imperfect/unpredictable. Possible imperfections in knowledge and development of appropriate conceptual frameworks for designing robust software systems & metrics for imperfect knowledge domains form the basis of the reported research work. First, various forms of human knowledge are enumerated, and sources and types of imperfections in knowledge are discussed
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