Software Module Clustering Algorithm Using Probability Selection

Software module clustering problem is an important and challenging problem in software reverse engineering whose main goal is to obtain a good modular structure of the software system. The large complex software system can be divided into some subsystems that are easy to understand and maintain through the software module clustering. Aiming at solving the problem of slow convergence speed, the poor clustering result, and the complex algorithm, a software module clustering algorithm using probability selection is proposed. Firstly, we convert the software system into complex network diagram, and then we use the operation of merger, adjustment and optimization to get the software module clustering scheme. To evaluate the effectiveness of the algorithm, a set of experiments was performed on 5 real-world module clustering problems. The comparison of the experimental results proves the simplicity of the algorithm as well as the low time complexity and fast convergence speed. This algorithm provides a simple and effective engineering method for software module clustering problem.

Density PSO-based software module clustering algorithm

Software module clustering is to divide the complex software system into many subsystems to enhance the intelligibility and maintainability of software systems. To increase convergence speed and optimize clustering solution,density PSO-based( DPSO) software module clustering algorithm is proposed. Firstly,the software system is converted into complex network diagram,and then the particle swarm optimization( PSO) algorithm is improved.The shortest path method is used to initialize the swarm,and the probability selection approach is used to update the particle positions. Furthermore,density-based modularization quality( DMQ) function is designed to evaluate the clustering quality. Five typical open source projects are selected as benchmark programs to verify the efficiency of the DPSO algorithm. Hill climbing( HC) algorithm,genetic algorithm( GA),PSO and DPSO algorithm are compared in the modularization quality( MQ) and DMQ. The experimental results show that the DPSO is more stable and more convergent than the other three traditional algorithms. The DMQ standard is more reasonable than MQ standard in guiding software module clustering.

DPTSV:A Dynamic Priority Task Scheduling Strategy for TSS Deadlock Based on Value Evaluation

This paper analyzes the threat of TCG Software Stack(TSS)/TCM Service Module(TSM) deadlock in multi-user environment such as cloud and discusses its causes and mechanism.In addition,this paper puts forward a dynamic priority task scheduling strategy based on value evaluation to handle this threat.The strategy is based on the implementation features of trusted hardware and establishes a multi-level ready queue.In this strategy,an algorithm for real-time value computing is also designed,and it can adjust the production curves of the real time value by setting parameters in different environment,thus enhancing its adaptability,which is followed by scheduling and algorithm description.This paper also implements the algorithm and carries out its performance optimization.Due to the experiment result from Intel NUC,it is shown that TSS based on advanced DPTSV is able to solve the problem of deadlock with no negative influence on performance and security in multi-user environment.