Machine learning techniques for code smell detection: A systematic literature review and meta-analysis

2019 ◽  
Vol 108 ◽  
pp. 115-138 ◽  
Author(s):  
Muhammad Ilyas Azeem ◽  
Fabio Palomba ◽  
Lin Shi ◽  
Qing Wang
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Code Smell

scholarly journals Machine Learning Techniques Focusing on the Energy Performance of Buildings: A Dimensions and Methods Analysis

Buildings ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 28
Author(s):  
Maria Anastasiadou ◽  
Vítor Santos ◽  
Miguel Sales Dias
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Meta Analysis ◽  
Building Energy ◽  
Energy Performance ◽  
Machine Learning Techniques ◽  
Energy Performance Of Buildings ◽  
Learning Techniques ◽  
Specific Factors

The problem of energy consumption and the importance of improving existing buildings’ energy performance are notorious. This work aims to contribute to this improvement by identifying the latest and most appropriate machine learning or statistical techniques, which analyze this problem by looking at large quantities of building energy performance certification data and other data sources. PRISMA, a well-established systematic literature review and meta-analysis method, was used to detect specific factors that influence the energy performance of buildings, resulting in an analysis of 35 papers published between 2016 and April 2021, creating a baseline for further inquiry. Through this systematic literature review and bibliometric analysis, machine learning and statistical approaches primarily based on building energy certification data were identified and analyzed in two groups: (1) automatic evaluation of buildings’ energy performance and, (2) prediction of energy-efficient retrofit measures. The main contribution of our study is a conceptual and theoretical framework applicable in the analysis of the energy performance of buildings with intelligent computational methods. With our framework, the reader can understand which approaches are most used and more appropriate for analyzing the energy performance of different types of buildings, discussing the dimensions that are better used in such approaches.

scholarly journals Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis

PLoS ONE ◽  
2019 ◽  
Vol 14 (7) ◽  
pp. e0220242 ◽  
Author(s):  
Ana Luiza Dallora ◽  
Peter Anderberg ◽  
Ola Kvist ◽  
Emilia Mendes ◽  
Sandra Diaz Ruiz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Meta Analysis ◽  
Bone Age ◽  
Machine Learning Techniques ◽  
Age Assessment ◽  
Learning Techniques ◽  
Bone Age Assessment

Code Clone Detection Using Machine Learning Techniques

2020 ◽  
Vol 11 (2) ◽  
pp. 49-75
Author(s):  
Amandeep Kaur ◽  
Sandeep Sharma ◽  
Munish Saini
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Machine Learning Techniques ◽  
Clone Detection ◽  
Learning Approaches ◽  
Code Clone ◽  
Online Databases ◽  
Learning Techniques ◽  
Tools And Techniques

Code clone refers to code snippets that are copied and pasted with or without modifications. In recent years, traditional approaches for clone detection combine with other domains for better detection of a clone. This paper discusses the systematic literature review of machine learning techniques used in code clone detection. This study provides insights into various tools and techniques developed for clone detection by implementing machine learning approaches and how effectively those tools and techniques to identify clones. The authors perform a systematic literature review on studies selected from popular computer science-related digital online databases from January 2004 to January 2020. The software system and datasets used for analyzing tools and techniques are mentioned. A neural network machine learning technique is primarily used for the identification of the clone. Clone detection based on a program dependency graph must be explored in the future because it carries semantic information of code fragments.

scholarly journals A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 518 ◽  
Author(s):  
Hafsa Khalid ◽  
Muzammil Hussain ◽  
Mohammed A. Al Ghamdi ◽  
Tayyaba Khalid ◽  
Khadija Khalid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Ct Scan ◽  
Systematic Literature Review ◽  
Machine Learning Techniques ◽  
Ct Scans ◽  
X Rays ◽  
X Ray ◽  
Learning Techniques

The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

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