Khalid M.O. Nahar

Software Design Principles to Enhance SDN Architecture

SDN as a network architecture emerged on top of existing technologies and knowledge. Through defining the controller as a software program, SDN made a strong connection between networking and software engineering. Traditionally, network programs were vendor specific and embedded in hardware switches and routers. SDN focuses on isolation between control and forwarding or data planes. However, in the complete SDN network, there are many other areas (i.e. CPU, memory, hardware, bandwidth and software). In this paper, we propose extending SDN architecture and propose isolation layers with the goal of improving the overall network design. Such flexible architecture can support future evolution and changes without the need to significantly change original components or modules.

Combined Source Code Approach for Test Case Prioritization

Regression testing is an activity in the software testing process to ensure the software is validated and verified after modification occurred on software. It is costly process procedure which has been expected to reach half cost of the software maintenance cost. Many techniques and approaches have been used in regression testing process to enhance the efficiency and effectiveness of regression testing process. Such as test case reduction, test case selection, test case prioritization and retest all. Test case prioritization has been used in regression testing to increase the effectiveness through selecting the most vital test case that has the ability in finding and uncovering errors in the system under test. This paper has introduced a new algorithm for prioritizing test cases in test suite that is based on method and line of code coverage. Test cases which cover the most methods and line of code are more effective and efficient in finding errors.

Arabic Phonemes Transcription Using Learning Vector Quantization: "Towards the Development of Fast Quranic Text Transcription"

In this paper, we investigated the use of Learning Vector Quantization (LVQ) for phoneme transcription in Arabic speech recognition systems. We used Arabic speech corpus of TV news clips. Then, we employed feature vectors, which embed the frame neighboring correlation information between adjacent phonemes to replace the traditional trip hones models. Next, we generated the phonemes codebooks using the K-means splitting algorithm. After that, we trained the generated codebooks using the LVQ algorithm. When using the trained LVQ codebooks in utterance phoneme transcription of an open vocabulary test corpus, the phoneme recognition rate was 72% without the use of any added phoneme big rams or HMM models. The results of this research if improved could be used to serve the holy Quran text transcription without any phonemes big rams (phonemes language model). This would increase the speed of the Quranic speech to text transcription and creates the infrastructure of suitable high speed automatic identification system of Quranic sounds recognition and translation.