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Because highly reliable software is becoming an essential ingredient in many systems, software developers apply various techniques to discover faults early in development, such as more rigorous reviews, more extensive testing, and strategic assignment of key personnel. Our goal is to target reliability enhancement activities to those modules that are most likely to have problems. This paper presents a methodology that incorporates genetic programming for predicting the order of software modules based on the expected number of faults. This is the first application of genetic programming to software engineering that we know of. We found that genetic programming can be used to generate software quality models whose inputs are software metrics collected earlier in development, and whose output is a prediction of the number of faults that will be discovered later in development or during operations. We established ordinal evaluation criteria for models, and conducted an industrial case study of software from a military communications system. Case study results were sufficiently good to be useful to a project for choosing modules for extra reliability enhancement treatment.

We propose an approach to approximate reasoning by systems of intelligent agents based on the paradigm of rough mereology. In this approach, the knowledge of each agent is formalized as an information system (a data table) from which similarity measures on objects manipulated by this agent are inferred. These similarity measures are based on rough mereological inclusions which formally render degrees for one object to be a part of another. Each agent constructs in this way its own rough mereological logic in which it is possible to express approximate statements of the type: an object x satisfies a predicate Ψ in degree r. The agents communicate by means of mereological functors (connectives among distinct rough mereological logics) propagating similarity measures from simpler to more complex agents; establishing these connectives is the main goal of negotiations among agents. The presented model of approximate reasoning entails such models of approximate reasoning like fuzzy controllers, neural networks etc. Our approach may be termed analytic, in the sense that all basic constructs are inferred from data.