Spectrum-based Fault Localization Techniques Application on Multiple-Fault Programs: A Review

Software fault localization is one of the most tedious and costly activities in program debugging in the endeavor to identify faults locations in a software program. In this paper, the studies that used spectrum-based fault localization (SBFL) techniques that makes use of different multiple fault localization debugging methods such as one-bug-at-a-time (OBA) debugging, parallel debugging, and simultaneous debugging in localizing multiple faults are classified and critically analyzed in order to extensively discuss the current research trends, issues, and challenges in this field of study. The outcome strongly shows that there is a high utilization of OBA debugging method, poor fault isolation accuracy, and dominant use of artificial faults that limit the existing techniques applicability in the software industry

Performance analysis of cloud-based cve communication architecture in comparison with the traditional client server, p2p and hybrid models

Gital et al. (2014) proposed a cloud based communication architecture for improving efficiency of collaborative virtual environment (CVE) systems in terms of Scalability and Consistency requirements. This paper evaluates the performance of the proposed CVE architecture. The metrics use for the evaluation is response time. We compare the cloud-based architecture to the traditional client server and peer-2–peer (P2P) architecture. The comparison was implemented in the CVE systems. The comparative simulation analysis of the results suggested that the CVE architecture based on cloud computing can significantly improve the performance of the CVE system

A review of the applications of bio-inspired flower pollination algorithm

The Flower Pollination Algorithm (FPA) is a novel bio-inspired optimization algorithm that mimics the real life processes of the flower pollination. In this paper, we review the applications of the Single Flower Pollination Algorithm (SFPA), Multi-objective Flower Pollination Algorithm an extension of the SFPA and the Hybrid of FPA with other bio-inspired algorithms. The review has shown that there is still a room for the extension of the FPA to Binary FPA. The review presented in this paper can inspire researchers in the bio-inspired algorithms research community to further improve the effectiveness of the PFA as well as to apply the algorithm in other domains for solving real life, complex and nonlinear optimization problems in engineering and industry. Further research and open questions were highlighted in the pape

Text normalization algorithm for facebook chats in Hausa language

The rapid increase in using non-standard words (NSWs) in communication through the social media is causing difficulties in understanding contents of the text messages. In addition, it affects the performance of several natural language processing (NLP) task such as machine translation, information retrievals, summarization and etc. In this study, we present an automatic text normalization system on Facebook chatting based on Hausa language. The proposed algorithm manually developed dictionary that employ normalization of each non-standard word with its equivalent standard word. This is accomplished through modification of the technique employed by [1] to fit Hausa NSWs' formation. It was found that our proposed algorithm was able to normalized Hausa NSWs with an accuracy of 100%. The results of this research can facilitate comprehensive communication via Facebook using Hausa language

Utilizing modular neural network for prediction of possible emergencies locations within point of interest of Hajj pilgrimage

This paper utilize modular neural network for prediction of possible emergencies locations during hajj pilgrimage. Available location, localization and positioning determination systems become increasingly important for use in day-to-day activities. These systems dwells on various scientific tools which ensure that the systems will provide accurate response to the needed service at the right time. Unfortunately, some tools were faced with drawbacks, either their use was not appropriate or they do not give reliable results, or the results obtained in certain scenario might not be apply to other scenarios. For this reasons, we utilize modular neural network tool to examine the analysis of determining possible emergencies locations within point of Interest of Hajj Pilgrimage in Meccah Saudi Arabia. The prediction results are generated by the use of longitude, latitude and distances as the dataset. Modular neural network takes longitude and latitude as inputs and predict distances within pilgrim’s possible point of interest. The learning systems were trained on the collected data. Experimental investigation demonstrated that modular network produce higher prediction accuracy compaired to other tools. This finding would contribute to the design of add-on applications which will deem to provide location based services for possible emergencies locations