Fault Localization Models in Debugging

Debugging is considered as a rigorous but important feature of software engineering process. Since more than a decade, the software engineering research community is exploring different techniques for removal of faults from programs but it is quite difficult to overcome all the faults of software programs. Thus, it is still remains as a real challenge for software debugging and maintenance community. In this paper, we briefly introduced software anomalies and faults classification and then explained different fault localization models using theory of diagnosis. Furthermore, we compared and contrasted between value based and dependencies based models in accordance with different real misbehaviours and presented some insight information for the debugging process. Moreover, we discussed the results of both models and manifested the shortcomings as well as advantages of these models in terms of debugging and maintenance.Comment: 58-6

Evaluation of IoT-Based Computational Intelligence Tools for DNA Sequence Analysis in Bioinformatics

In contemporary age, Computational Intelligence (CI) performs an essential role in the interpretation of big biological data considering that it could provide all of the molecular biology and DNA sequencing computations. For this purpose, many researchers have attempted to implement different tools in this field and have competed aggressively. Hence, determining the best of them among the enormous number of available tools is not an easy task, selecting the one which accomplishes big data in the concise time and with no error can significantly improve the scientist's contribution in the bioinformatics field. This study uses different analysis and methods such as Fuzzy, Dempster-Shafer, Murphy and Entropy Shannon to provide the most significant and reliable evaluation of IoT-based computational intelligence tools for DNA sequence analysis. The outcomes of this study can be advantageous to the bioinformatics community, researchers and experts in big biological data

Treatment of Reactive Routing Protocols Using Second Chance Based on Malicious behavior of Nodes in MANETS

Mobile nodes of various routing protocols in Mobile Ad hoc Networks follow different strategies in transmission and receiving of data. Security, packet delivery and routing overhead are important concerns for any protocol during designing them. The presence and absence of malicious nodes in the network affect a lot on the performance of the protocol. This research focused on the study of the threats, attacks and reasons for malicious behavior of nodes in the network for reactive routing protocols in MANETS. DSR and AODV are the two reactive routing protocols that considered the study to propose a second chance strategy to given to the nodes considering the reason for malicious behavior to improve the packet delivery ratio and reduce the routing overhead in the network. A simulative study has conducted using Ad hoc Simulator (ASIM) considering the DSR and AODV routing protocols in the presence of malicious nodes and in the absence of malicious nodes that showed, that the packet delivery ratio is low and routing overhead is high in the absence of malicious nodes. The second chance strategy proposed considers the reasons for malicious behavior and helps the node to be reintegrate in the network to improve the packet delivery ratio and reduce the routing overhead