The success in topdown design of recent huge system LSIs is in a seamless transfer of the information resulted from the high level design to the lower level of floorplanning. For the purpose, we introduce a new concept abstract floorplan which is included in the output of high level design. From the abstract floorplan, the pillar blocks are derived which are critical sets of blocks that are expected to determine the width and height of the chip, named the frame. Since the frame and pillar blocks are obtained in the high level stage, they are useful to keep the consistency in the low level physical design if we apply optimization regarding them as constraints. Experiments to MCNC benchmarks showed that abstract floorplanning by pillar blocks output a placement faithful to the one physically optimized block placement with respect to the chip area and the wire-length.

The shape-based routing needs a routing architecture with a geometrical computation framework on it. This paper introduces a novel routing architecture, Oct-Touched Tile (OTT), with a geometrical computation method along the horizontal- and vertical-constraints. The architecture is represented by the tiles spreading over the 2-D plane. Each tile is flexible to satisfy the constraints imposed for non-overlapping and sizing request. In this framework, path finding and shape-based sizing are executed on the same architecture. In experiments, our system demonstrates the performance comparable to a commercial tool. In addition, we show potential of OTT by introducing several ideas of extensions to analog layout constraints.

With the in-depth development of service computing, it has become clear that when constructing service applications in an open dynamic network environment, greater attention must be paid to trustworthiness under the premise of functions' realization. Trustworthy computing requires theories for business process modeling in terms of both behavior and trustworthiness. In this paper, a calculus for ensuring the satisfaction of trustworthiness requirements in service-oriented systems is proposed. We investigate a calculus called QPi, for representing both the behavior and the trustworthiness property of concurrent systems. QPi is the combination of pi-calculus and a constraint semiring, which has a feature when problems with multi-dimensional properties must be tackled. The concept of the quantified bisimulation of processes provides us a measure of the degree of equivalence of processes based on the bisimulation distance. The QPi related properties of bisimulation and bisimilarity are also discussed. A specific modeling example is given to illustrate the effectiveness of the algebraic method.

In the software testing phase, software reliability growth models (SRGMs) are commonly used to evaluate the reliability of software systems. Traditional SRGMs are restricted by their assumption of a continuous growth pattern for the failure detection rate (FDR) throughout the testing phase. However, the assumption is compromised by Change-Point phenomena, where FDR fluctuations stem from variations in testing personnel or procedural modifications, leading to reduced prediction accuracy and compromised software reliability assessments. Therefore, the objective of this study is to improve software reliability prediction using a novel approach that combines genetic algorithm (GA) and deep learning-based SRGMs to account for the Change-point phenomenon. The proposed approach uses a GA to dynamically combine activation functions from various deep learning-based SRGMs into a new mutated SRGM called MuSRGM. The MuSRGM captures the advantages of both concave and S-shaped SRGMs and is better suited to capture the change-point phenomenon during testing and more accurately reflect actual testing situations. Additionally, failure data is treated as a time series and analyzed using a combination of Long Short-Term Memory (LSTM) and Attention mechanisms. To assess the performance of MuSRGM, we conducted experiments on three distinct failure datasets. The results indicate that MuSRGM outperformed the baseline method, exhibiting low prediction error (MSE) on all three datasets. Furthermore, MuSRGM demonstrated remarkable generalization ability on these datasets, remaining unaffected by uneven data distribution. Therefore, MuSRGM represents a highly promising advanced solution that can provide increased accuracy and applicability for software reliability assessment during the testing phase.