Predicting Software Reliability Using Ant Colony Optimization Technique with Travelling Salesman Problem for Software Process – A Literature Survey

Computer software has become an essential and important foundation in several versatile domains including medicine, engineering, etc. Consequently, with such widespread application of software, there is a need of ensuring software reliability and quality. In order to measure such software reliability and quality, one must wait until the software is implemented, tested and put for usage for a certain time period. Several software metrics have been proposed in the literature to avoid this lengthy and costly process, and they proved to be a good means of estimating software reliability. For this purpose, software reliability prediction models are built. Software reliability is one of the important software quality features. Software reliability is defined as the probability with which the software will operate without any failure for a specific period of time in a specified environment. Software reliability, when estimated in early phases of software development life cycle, saves lot of money and time as it prevents spending huge amount of money on fixing of defects in the software after it has been deployed to the client. Software reliability prediction is very challenging in starting phases of life cycle model. Software reliability estimation has thus become an important research area as every organization aims to produce reliable software, with good quality and error or defect free software. There are many software reliability growth models that are used to assess or predict the reliability of the software. These models help in developing robust and fault tolerant systems. In the past few years many software reliability models have been proposed for assessing reliability of software but developing accurate reliability prediction models is difficult due to the recurrent or frequent changes in data in the domain of software engineering. As a result, the software reliability prediction models built on one dataset show a significant decrease in their accuracy when they are used with new data. The main aim of this paper is to introduce a new approach that optimizes the accuracy of software reliability predictive models when used with raw data. Ant Colony Optimization Technique (ACOT) is proposed to predict software reliability based on data collected from literature. An ant colony system by combining with Travelling Sales Problem (TSP) algorithm has been used, which has been changed by implementing different algorithms and extra functionality, in an attempt to achieve better software reliability results with new data for software process. The intellectual behavior of the ant colony framework by means of a colony of cooperating artificial ants are resulting in very promising results. Keywords: Software Reliability, Reliability predictive Models, Bio-inspired Computing, Ant Colony Optimization technique, Ant Colon

Implementation of TCP Congestion Control mechanism for Wireless Networks using TCP Reserved Field and Signal to Noise Ratio (SNR)

TCP is the most popular and widely used network transmission protocol. All most 90% of the Internet connections make use of TCP for communication. TCP is reliable for wired networks and it considers all packet timeouts in wired networks as due to network congestion and not because of bit errors. However, TCP suffers from performance degradation over error-prone wireless links, as it has no technique to distinguish error deficits from congestion deficits, with networking becoming more divergent, with wired and wireless topologies. It considers all packet deficits are due to congestion and subsequently reduces the packet burst transmission, at the same time decreasing the network throughput. In this paper a new TCP congestion control mechanism is proposed that is suitable and applicable for wireless and also for wired networks and is capable of distinguishing congestion deficits from error deficits. The proposed technique uses the reserved field of the TCP header to indicate whether the connection established is over a wired or a wireless link. Further, the proposed technique influences the usage of Signal to Noise Ratio (SNR) to discover the reliability of the link and determine whether to decrease the packet burst or retransmit the timed-out packet. Investigations performed, revealed that the proposed mechanism confirmed to function amend in circumstances where timeouts were due to error and not due to congestion. Further, the future work can be enhanced upon the proposed mechanism so that it can leverage Cyclic Redundancy Check(CRC) and Header Error Check(HEC) errors so that it can be properly determined the reason for initializing transmission timeouts in wireless networks.

Facts and Treatment of Bullous Pemphigoid: A Review

Bullous pemphigoid (a type of pemphigoid) is an autoimmune pruritic skin disease that typically occurs in people aged over 60, that may involve the formation of blisters (bullae) in the space between the epidermal and dermal skin layers. It is classified as a type II hypersensitivity reaction, which involves formation of anti-hemidesmosome antibodies, causing a loss of keratinocytes to basement membrane adhesion. Pemphigus and bullous pemphigoid are autoantibody-mediated blistering skin diseases. In pemphigus, keratinocytes in epidermis and mucous membranes lose cell-cell adhesion, and in pemphigoid, the basal keratinocytes lose adhesion to the basement membrane. Pemphigus lesions are mediated directly by the autoantibodies, whereas the autoantibodies in pemphigoid fix complement and mediate inflammation. In both diseases, the autoantigens have been cloned and characterized; pemphigus antigens are desmogleins (cell adhesion molecules in desmosomes), and pemphigoid antigens are found in hemidesmosomes (which mediate adhesion to the basement membrane). This knowledge has enabled diagnostic testing for these diseases by enzyme-linked immunosorbent assays and dissection of various pathophysiological mechanisms, including direct inhibition of cell adhesion, antibody-induced internalization of antigen, and cell signaling. Understanding these mechanisms of disease has led to rational targeted therapeutic strategies