Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 31, Pages: 1-8
J. Sapphire Paul1*, E. Brumancia2 and S. Justin Samuel2
1 Department of Information Technology, Sathyabama University, [email protected]
2 Faculty of Computing, [email protected]
*Author for correspondence
Department of Information Technology,
Email: [email protected]
Background/Objectives: This survey represents a various techniques of fixing bugs and methods 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 bug fixing is termed as bug triage which aims to accurately propose a software developer to a brand new incoming bug. To diminish the time that is involved in human work, classification via text techniques are used to perform bug sorting automatically. Findings: This methodology, leads to a mind set to get a solution to the problem of knowledge reduction for bug sorting, i.e., the means to prune back the ratio and increase the standard of bug knowledge. Instance and feature selection techniques are associated to the bugs at the same time to cut back knowledge ratio on the dimension of the bug and also the dimension of the word. To make away this parliamentary procedure of incorporating instance and feature selection, the attributes are mined and keywords from older bug knowledge datasets and construct a prophetic model for a brand new bug knowledge dataset. Tendency for thorough empirical observation is induced and to investigate the execution of data reduction of the whole 60000 bug reports of 2 giant openly available supply comes, such as 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 increase the quality of bug sorting. This proposal gives associate method to investment techniques while improving to create decreased and highly qualified bug expertise in computer code generation 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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