An Efficient Software Error Prediction and Recommend System
B. Venkateswarlu1, K. Murali Manohar2, K. Gopichand3, V. Sandeep Reddy4
1B. Venkateswarlu, Asst. Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, vaddeswaram, Guntur Dt, Andhra Pradesh, India.
2K. Murali Manohar, Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, vaddeswaram, Guntur Dt, Andhra Pradesh, India.
3K. Gopichand, Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, vaddeswaram, Guntur Dt, Andhra Pradesh, India.
4V. Sandeep Reddy, Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, vaddeswaram, Guntur Dt, Andhra Pradesh, India.
Manuscript received on November 13, 2019. | Revised Manuscript received on 22 November, 2019. | Manuscript published on December 10, 2019. | PP: 5300-5303 | Volume-9 Issue-2, December 2019. | Retrieval Number: B9004129219/2019©BEIESP | DOI: 10.35940/ijitee.B9004.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This present paper proposes the while its beginning, and past years software testing has been involved. Modern technology in software is using Artificial Intelligent and machine learning for advancing the technology. According to software engineering various techniques are analysed depending on the required predictions. Here in order to give the importance for the development of software defect prediction technique helps foe testers to focus on modules that defect prone. Depending on the development aspect the literature survey states various techniques based on features that are mostly captured for the prediction of defects. So in this paper we give a novel machine learning technique which is the foremost objective for finding prospective areas defects by considering various parameters like system testing metrics and unique parameters called ‘Component Dependency Score’. By applying element determination method we can reduce the words present in defect information and also there will be an expansion in precision so that both systems can build the additional qualities like precision and reducing defect reports or words. Depending on this new technique we can reduce the defect information sets for getting 71.8 percent exactness for reducing the request. The present issue reducing information to defect and increase the information set of defect in two viewpoints such as all the while diminish the sizes of defect extent and the word extent and to enhance the precision of defect triage. So we propose a mix way for dealing with of attention of issuing for reducing information. This is viewed as an example for purpose of choice highlighting in defecting store house. So we construct a parallel categoriser for expecting the request in determination of applying example and highlighting choices. Here the request for applying occurrence in highlighting the choices is not yet related to the research space.
Keywords: Defect, Recommendation
Scope of the Article: Systems and Software Engineering