Schedulability of Rate Monotonic Algorithm using Improved Time Demand Analysis for Multiprocessor Environment

Real-Time Monotonic algorithm (RMA) is a widely used static priority scheduling algorithm. For application of RMA at various systems, it is essential to determine the system’s feasibility first. The various existing algorithms perform the analysis by reducing the scheduling points in a given task set. In this paper we propose a schedubility test algorithm, which reduces the number of tasks to be analyzed instead of reducing the scheduling points of a given task. This significantly reduces the number of iterations taken to compute feasibility. This algorithm can be used along with the existing algorithms to effectively reduce the high complexities encountered in processing large task sets. We also extend our algorithm to multiprocessor environment and compare number of iterations with different number of processors. This paper then compares the proposed algorithm with existing algorithm. The expected results show that the proposed algorithm performs better than the existing algorithms

Software Reliability Prediction using Fuzzy Min-Max Algorithm and Recurrent Neural Network Approach

Fuzzy Logic (FL) together with Recurrent Neural Network (RNN) is used to predict the software reliability. Fuzzy Min-Max algorithm is used to optimize the number of the kgaussian nodes in the hidden layer and delayed input neurons. The optimized recurrentneural network is used to dynamically reconfigure in real-time as actual software failure. In this work, an enhanced fuzzy min-max algorithm together with recurrent neural network based machine learning technique is explored and a comparative analysis is performed for the modeling of reliability prediction in software systems. The model has been applied on data sets collected across several standard software projects during system testing phase with fault removal. The performance of our proposed approach has been tested using distributed system application failure data set

Minimal TestCase Generation for Object-Oriented Software with State Charts

Today statecharts are a de facto standard in industry for modeling system behavior. Test data generation is one of the key issues in software testing. This paper proposes an reduction approach to test data generation for the state-based software testing. In this paper, first state transition graph is derived from state chart diagram. Then, all the required information are extracted from the state chart diagram. Then, test cases are generated. Lastly, a set of test cases are minimized by calculating the node coverage for each test case. It is also determined that which test cases are covered by other test cases. The advantage of our test generation technique is that it optimizes test coverage by minimizing time and cost. The present test data generation scheme generates test cases which satisfy transition path coverage criteria, path coverage criteria and action coverage criteria. A case study on Railway Ticket Vending Machine (RTVM) has been presented to illustrate our approach.Comment: 21 pages, 7 figures, 3-4 tables; International Journal of Software Engineering & Applications (IJSEA), Vol.3, No.4, July 2012. arXiv admin note: substantial text overlap with arXiv:1206.037