In recent years, Random Network Coding (RNC) has emerged as a promising solution for efficient Peer-to-Peer (P2P) video multicasting over the Internet. This probably refers to this fact that RNC noticeably increases the error resiliency and throughput of the network. However, high transmission overhead arising from sending large coefficients vector as header has been the most important challenge of the RNC. Moreover, due to employing the Gauss-Jordan elimination method, considerable computational complexity can be imposed on peers in decoding the encoded blocks and checking linear dependency among the coefficients vectors. In order to address these challenges, this study introduces MATIN which is a random network coding based framework for efficient P2P video streaming. The MATIN includes a novel coefficients matrix generation method so that there is no linear dependency in the generated coefficients matrix. Using the proposed framework, each peer encapsulates one instead of n coefficients entries into the generated encoded packet which results in very low transmission overhead. It is also possible to obtain the inverted coefficients matrix using a bit number of simple arithmetic operations. In this regard, peers sustain very low computational complexities. As a result, the MATIN permits random network coding to be more efficient in P2P video streaming systems. The results obtained from simulation using OMNET++ show that it substantially outperforms the RNC which uses the Gauss-Jordan elimination method by providing better video quality on peers in terms of the four important performance metrics including video distortion, dependency distortion, End-to-End delay and Initial Startup delay.
Regression testing is crucial in ensuring that modifications made did not introduce any adverse effect on the software being modified. However, regression testing suffers from execution cost and time consumption problems. Test case prioritization (TCP) is one of the techniques used to overcome these issues by re-ordering test cases based on their priorities. Model-based TCP (MB-TCP) is an approach in TCP where the software models are manipulated to perform prioritization. The issue with MB-TCP is that most of the existing approaches do not provide satisfactory faults detection capability. Besides, their granularity of test selection criteria is not very good and this can affect prioritization effectiveness. This study proposes an MB-TCP approach that can improve the faults detection performance of regression testing. It combines the implementation of two existing approaches from the literature while incorporating an additional ordering criterion to boost prioritization efficacy. A detailed empirical study is conducted with the aim to evaluate and compare the performance of the proposed approach with the selected existing approaches from the literature using the average of the percentage of faults detected (APFD) metric. Three web applications were used as the objects of study to obtain the required test suites that contained the tests to be prioritized. From the result obtained, the proposed approach yields the highest APFD values over other existing approaches which are 91%, 86% and 91% respectively for the three web applications. These higher APFD values signify that the proposed approach is very effective in revealing faults early during testing. They also show that the proposed approach can improve the faults detection performance of regression testing.