Software Enhancement Effort Prediction Using Machine-Learning Techniques: A Systematic Mapping Study

2021 ◽  
Vol 2 (6) ◽  
Author(s):  
Zaineb Sakhrawi ◽  
Asma Sellami ◽  
Nadia Bouassida
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Learning Techniques ◽  
Effort Prediction

scholarly journals Gamified Mobile Applications for Improving Driving Behavior: A Systematic Mapping Study

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Abderrahim El hafidy ◽  
Taoufik Rachad ◽  
Ali Idri ◽  
Ahmed Zellou
Keyword(s):  
Machine Learning ◽  
Mobile Applications ◽  
Mobile Apps ◽  
Driving Behavior ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Learning Techniques

Many research works and official reports approve that irresponsible driving behavior on the road is the main cause of accidents. Consequently, responsible driving behavior can significantly reduce accidents’ number and severity. Therefore, in the research area as well as in the industrial area, mobile technologies are widely exploited in assisting drivers in reducing accident rates and preventing accidents. For instance, several mobile apps are provided to assist drivers in improving their driving behavior. Recently and thanks to mobile cloud computing, smartphones can benefit from the computing power of servers in the cloud for executing machine learning algorithms. Therefore, many mobile applications of driving assistance and control are based on machine learning techniques to adjust their functioning automatically to driver history, context, and profile. Additionally, gamification is a key element in the design of these mobile applications that allow drivers to develop their engagement and motivation to improve their driving behavior. To have an overview concerning existing mobile apps that improve driving behavior, we have chosen to conduct a systematic mapping study about driving behavior mobile apps that exist in the most common mobile apps repositories or that were published as research works in digital libraries. In particular, we should explore their functionalities, the kinds of collected data, the used gamification elements, and the used machine learning techniques and algorithms. We have successfully identified 220 mobile apps that help to improve driving behavior. In this work, we will extract all the data that seem to be useful for the classification and analysis of the functionalities offered by these applications.

scholarly journals Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study

2021 ◽  
Vol 101 ◽  
pp. 107050
Author(s):  
Michał Choraś ◽  
Konstantinos Demestichas ◽  
Agata Giełczyk ◽  
Álvaro Herrero ◽  
Paweł Ksieniewicz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Systematic Mapping Study ◽  
Fake News ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Learning Techniques ◽  
News Online

Machine Learning Techniques for Code Smells Detection: A Systematic Mapping Study

2019 ◽  
Vol 29 (02) ◽  
pp. 285-316 ◽  
Author(s):  
Frederico Luiz Caram ◽  
Bruno Rafael De Oliveira Rodrigues ◽  
Amadeu Silveira Campanelli ◽  
Fernando Silva Parreiras
Keyword(s):  
Machine Learning ◽  
Empirical Studies ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Systematic Mapping Study ◽  
Code Smells ◽  
Mapping Study ◽  
Learning Techniques ◽  
Wide Range ◽  
High Level

Code smells or bad smells are an accepted approach to identify design flaws in the source code. Although it has been explored by researchers, the interpretation of programmers is rather subjective. One way to deal with this subjectivity is to use machine learning techniques. This paper provides the reader with an overview of machine learning techniques and code smells found in the literature, aiming at determining which methods and practices are used when applying machine learning for code smells identification and which machine learning techniques have been used for code smells identification. A mapping study was used to identify the techniques used for each smell. We found that the Bloaters was the main kind of smell studied, addressed by 35% of the papers. The most commonly used technique was Genetic Algorithms (GA), used by 22.22% of the papers. Regarding the smells addressed by each technique, there was a high level of redundancy, in a way that the smells are covered by a wide range of algorithms. Nevertheless, Feature Envy stood out, being targeted by 63% of the techniques. When it comes to performance, the best average was provided by Decision Tree, followed by Random Forest, Semi-supervised and Support Vector Machine Classifier techniques. 5 out of the 25 analyzed smells were not handled by any machine learning techniques. Most of them focus on several code smells and in general there is no outperforming technique, except for a few specific smells. We also found a lack of comparable results due to the heterogeneity of the data sources and of the provided results. We recommend the pursuit of further empirical studies to assess the performance of these techniques in a standardized dataset to improve the comparison reliability and replicability.

scholarly journals Recent Progress in Automated Code Generation from GUI Images Using Machine Learning Techniques

2020 ◽  
Vol 26 (9) ◽  
pp. 1095-1127
Author(s):  
Daniel Baulé ◽  
Christiane Gresse von Wangenheim ◽  
Aldo Wangenheim ◽  
Jean Carlo Rossa Hauck
Keyword(s):  
Machine Learning ◽  
Code Generation ◽  
Interface Design ◽  
Machine Learning Techniques ◽  
Systematic Mapping Study ◽  
Learning Approaches ◽  
Systematic Mapping ◽  
Learning Techniques ◽  
Performance Results ◽  
Automated Code Generation

The manual transformation of a user interface design into code is a costly and time-consuming process. A solution can be the automation of the generation of code based on sketches or GUI design images. Recently, Machine Learning approaches have shown promising results in detecting GUI elements for such automation. Thus, to provide an overview of existing approaches, we performed a systematic mapping study. As a result, we identified and compared 20 approaches, that demonstrate good performance results being considered useful. These results can be used by researchers and practitioners in order to improve the efficiency of the GUI design process as well as continue to evolve and improve approaches for its support.

Machine Learning Applied to Software Testing: A Systematic Mapping Study

2019 ◽  
Vol 68 (3) ◽  
pp. 1189-1212 ◽  
Author(s):  
Vinicius H. S. Durelli ◽  
Rafael S. Durelli ◽  
Simone S. Borges ◽  
Andre T. Endo ◽  
Marcelo M. Eler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Testing ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping

scholarly journals Election Prediction on Twitter: A Systematic Mapping Study

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-27
Author(s):  
Asif Khan ◽  
Huaping Zhang ◽  
Nada Boudjellal ◽  
Arshad Ahmad ◽  
Jianyun Shang ◽  
...  
Keyword(s):  
Demographic Data ◽  
Learning Needs ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Comprehensive Overview ◽  
Academic Organizations ◽  
Learning Techniques ◽  
Election Prediction ◽  
Social Media Platforms

Context. Social media platforms such as Facebook and Twitter carry a big load of people’s opinions about politics and leaders, which makes them a good source of information for researchers to exploit different tasks that include election predictions. Objective. Identify, categorize, and present a comprehensive overview of the approaches, techniques, and tools used in election predictions on Twitter. Method. Conducted a systematic mapping study (SMS) on election predictions on Twitter and provided empirical evidence for the work published between January 2010 and January 2021. Results. This research identified 787 studies related to election predictions on Twitter. 98 primary studies were selected after defining and implementing several inclusion/exclusion criteria. The results show that most of the studies implemented sentiment analysis (SA) followed by volume-based and social network analysis (SNA) approaches. The majority of the studies employed supervised learning techniques, subsequently, lexicon-based approach SA, volume-based, and unsupervised learning. Besides this, 18 types of dictionaries were identified. Elections of 28 countries were analyzed, mainly USA (28%) and Indian (25%) elections. Furthermore, the results revealed that 50% of the primary studies used English tweets. The demographic data showed that academic organizations and conference venues are the most active. Conclusion. The evolution of the work published in the past 11 years shows that most of the studies employed SA. The implementation of SNA techniques is lower as compared to SA. Appropriate political labelled datasets are not available, especially in languages other than English. Deep learning needs to be employed in this domain to get better predictions.

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