Supporting software development using bug triage a focus on corrective maintenance

Abstract

Nowadays software development has become as an important investment and asset for organizations Software project management evaluation includes cost quality evaluation and risk investigation which are affected by the accuracy of the software product Software project accuracy and software maintainability are the most important goals of software engineering project management Clubbing data mining technique with software engineering does not only enhance the accurateness but also improves the reliability of the software A software development application error is a fault or bug in a computer program It produces wrong or unexpected outcomes The traditional software development faults are manually triaged through a specialist developer ie a human being triaged Manual fault triage takes longer time and may produce less accuracy due to huge amount of fault data set traversed for fault detection an Efficient Integration of Instance and Aspect Preferment Algorithm EIIAPA has been proposed to decrease the scale of fault report data concurrently and to enhance the accurateness of data The proposed technique helps to validate and verify software application in efficient manner Reduction of data on fault triage objective is to construct a high-superiority set of fault data in the small-scale system through eliminating the fault report. By applying an algorithm, fault data set and attributes are extracted from every fault data set and train a predictive model based on the historical dataset Based on experimental evaluations proposed methodology reduces 0.06 seconds ET Execution Time and improves 0.5 P Precision 0.75 R Recall 0.39 F-M F-Measure and 5.07 percentage accuracy compared than existing methodologies. newline