J. Sapphire Paul ¹, E. Brumancia ², S. Justin Samuel ²

Affiliations
1 Department of Information Technology, Sathyabama University, Chennai – 600119, Tamil Nadu, IN
2 Faculty of Computing, Sathyabama University, Chennai – 600119, Tamil Nadu, IN

Abstract

Background/Objectives: This paper represents a review of how to fix bugs and how to accurately assign a brand new bug to a particular developer. Software firms pay over 45 % of value in coping with computer code bugs. A complicated process of fixing bugs is bug triage that aims to accurately assign a developer to a brand new bug. To diminish the time value in human work, text classification techniques are used to conduct automatic bug sorting. Methods: Findings: In this paper, we possess a mind set to address the problem of knowledge reduction for bug sorting, i.e., the means to prune back the ratio and improve the standard of bug knowledge. We associate instance selection with feature selection to at the same time cut back knowledge ratio on the bug dimension and also the word dimension. To make away the parliamentary procedure of applying instance selection and feature election, we extract attributes from historical bug knowledge sets and build a prophetic model for a brand new bug knowledge set. We induce a tendency to thorough empirical observation and to investigate the execution of data reduction of the whole 600,000 bug reports of 2 giant open supply comes, namely Eclipse and Mozilla. Data reduction techniques such as classification and prediction method can be used to cut down the large bug dataset (by removing noisy and irrelevant data), and predict the correct answer for the bugs by applying keywords and properties. A classification method such as Bayesian Classification is used to classify the incoming bug dataset. Properties of Instance Selection and Feature Selection techniques are merged along with the attributes and keywords to solve the problem of bug prioritizing. Applications/Improvements: These outcomes demonstrate that the data reduction will effectively cut back the knowledge ratio and improve the accuracy of bug sorting. Our work provides associate approach to investment techniques on processing to create decreased and high-quality bug expertise in computer code development and sustenance. Regression analysis techniques can also be applied to a decision table to calculate the effort that is needed to resolve an incoming bug.

Keywords

Bayesian Classification, Data Reduction, Feature Selection, Instance Selection, Regression Analysis.

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T. Priya ¹, S. Justin Samuel ¹

Affiliations
1 Faculty of Computing, Sathyabama University, Chennai – 600119, Tamil Nadu, IN

Abstract

Background/Objectives: In recent years Mobile WSN (Wireless Sensor Networks) has obtained more attention because it has an ability to allow economical and effective solutions in different fields. When designing the Mobile WSN, energy saving becomes an issue. So a network clustering technique is used to divide the network into small group of clusters and every cluster group is controlled and examined by one node called CH (Cluster Head). An effectual procedure for clustering and LEACH (Low Energy Adaptive Cluster Head Election) can forcefully decrease the consumption of energy and improve the lifetime of network. The existing WSN contains more sensor nodes that vary from one to another based on performance, computing power, initial energy, etc. According to decrease congestion and undesirable power, during the transmission of data long distance, stable number of transmit nodes are placed and selected as router to keep the connectivity. Then the Transmitted node collects the aggregated data using cluster head and moves it to base station. In this system, we are going to work the tradeoff between data assembly latency and power saving in gathering of mobile data by exploring an aggregation of local data and the SenCar moving length. A framework of three layers is suggested in WSN for mobile data group, which comprises layer of cluster head, layer of mobile collector and layer of Sencar. Methods: This framework deals with Dual Data Uploading and Load Balanced Clustering which is abbreviated as LBC-DDU. In emergency situations the signal priority is not mentioned in LBC-DDU. To achieve this problem, we suggested the Multi sensor techniques of priority Based in WSN (Wireless Sensor Networks). To assess the efficiency of the suggested priority scheme a large simulation are regulated. The proposed algorithm of shuffling allows the base station to identify the head of cluster and transmit node on the basis of residual energy, node density, base station distance and nodes overlapping ratio, etc. Findings and Improvements: We correlate the SenCar mobility in multi-hop routing to complete the advantages of double data uploading, which eventually shows data collection in priority based with both short time uploading of data and short path of moving.

Keywords

Data Collection, Dual Data Uploading, Load Balanced Clustering, SenCar, Wireless Sensor Networks (WSNs).

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