We present a scenario-aware bus functional modeling method which improves the accuracy of traditional methods without sacrificing the simulation run time. Existing methods focused on the behavior of individual IP (Intellectual Property) components and neglected the interplay effects among them, resulting in accuracy degradation from the system perspective. On the other hand, our method thoroughly considers such effects and increases the analysis accuracy by adopting control signal modeling and hierarchical stochastic modeling. Furthermore, our method minimizes the additional design time by reusing the simulation results of each IP component and an automated design flow. The experimental results show that the accuracy of our method is over 90% of RTL simulation in a multimedia SoC (System-on-Chip) design.

One approach to develop software efficiently is to reuse existing software by modifying a part of it. However, modifying software will often introduce unexpected side effects into other parts of it. As a result, it costs much time and care to modify the software. So, in order to modify software efficiently, we have proposed a functional model to represent information about side effects caused by modification and a model based supporting system for modifying software. So far, however, an expert software developer must describe the entire functional model of the target software through the analysis of practical modifying processes. This will be an unnecessary burden on him. Moreover, the larger target software becomes, the harder the model construction becomes. Therefore, an automatic constructing method of the functional model is needed in order to solve this problem. So, this paper considers a method of acquiring useful interaction information by learning from training examples of modification. However, in our application domain, it seems that it is impossible to make complete domain theory and to prepare a large number or training examples in advance. Therefore, our learning method involves an integration of explanation-based learning (EBL) from positive examples of modification generated by the user and Similarity-based learning (SBL) from positive or negative examples generated by the user and the learning system. As a result, our method can acquire valid knowledge about the interaction from not so many examples under incomplete theory. Then, this paper presents a constructing method, in which our proposed learning method is incorporated, of a functional model. Finally, this paper demonstrates construction of the functional model in the domain of an event-driven queueing simulation program according to our learning method.

A stepwise refinement method of communications service specifications is proposed to generate communications software that can conform to any network architecture. This method uses a two-layered language; one layer is a service specification description language (STR), and the other layer is a supplementary specification description language for implementing STR description on a communications system (STR/D). STR specifies terminal behaviors that can be recognized from a perspective outside of the communications systems. With STR, a communications service is defined by a set of rules that can be described without detailed knowledge of communications systems or communications network architectures. Each STR rule describes a global state transition of terminals. Supplementary specifications, such as terminal control and network control, are needed to implement communications services specified by STR rules. These supplementary specifications are described by STR/D rules. Communications services, such as UPT (Universal Personal Telecommunication), are standardized so that they can be provided on a given functional model consisting of functional entities. Specifications for each functional entity in a network are obtained from the two kinds of initially described specifications mentioned above. The obtained specifications are described by STR(L) and STR/D(L) rules, which specify local specifications of a functional entity. These specifications for functional entities are then transformed into software specifications, and finally communications software is generated from these software specifications. This stepwise refinement method makes it possible to generate communications software that can conform to any functional model from service specifications.