Integrated Reasoning Engine for Code Clone Detection

Author:

Bynagari Naresh Babu

Abstract

This article seeks to foray into the nitty-gritty of integrated reasoning for code clone detection and how it is effectively carried out, given the amount of analytics usually associated with such activities. Detection of codes requires high-pitch familiarity with cloning systems and their workings. Hence, discovering similar code segments that are often regarded and seen as code imitations (clone) is not an easy responsibility. More especially, this very detection process might possess key purposes in the context of susceptibility findings, refactoring, and imitation detecting. Through the voyage of discovery this article intends to expose you to, you will realize that identical code segments, more often than not described as code clones, appear to be a serious duty, especially for large code bases <1; 2; 3; 4>. There are certain approaches and deep technicalities that this sort of detection is known for. Still, from the avalanche of resources that formed the bedrock of this article, one would discover the easiest formula to adopt in maneuvering such strenuous issues.

Publisher

ABC Journals

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Overcoming the Vanishing Gradient Problem during Learning Recurrent Neural Nets (RNN);Asian Journal of Applied Science and Engineering;2020-12-31

2. Enhancing Predictions in Ungauged Basins Using Machine Learning to Its Full Potential;Asian Journal of Applied Science and Engineering;2019-05-05

3. Do Internals of Neural Networks Make Sense in the Context of Hydrology?;Asian Journal of Applied Science and Engineering;2018-07-13

4. Multimodal Learning Analysis via Machine Learning and Deep Learning Methodologies;Asian Journal of Applied Science and Engineering;2018-07-12

5. On the ChEMBL Platform, a Large-scale Evaluation of Machine Learning Algorithms for Drug Target Prediction;Asian Journal of Applied Science and Engineering;2018-07-10

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