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Future Internet
Special Issues
Machine Learning for Software Engineering
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Machine Learning for Software Engineering

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 8993

Special Issue Editors

Prof. Dr. Manuel Mazzara

E-Mail Website
Guest Editor
Institute of Software Development and Engineering, Innopolis University, 420500 Innopolis, Russia
Interests: software engineering; formal methods; service-oriented architectures; microservices; software dependability
Prof. Dr. Adriano Bessa Albuquerque

E-Mail Website
Guest Editor
Pos-Graduate Program in Applied Informatics (PPGIA), University of Fortaleza, Fortaleza 60811-905, CE, Brazil
Interests: software engineering; software process; software product quality; IT Management
Dr. Luiz Jonata Pires de Araujo

E-Mail Website
Assistant Guest Editor
Machine Learning & Knowledge Representation (MlKr) Lab, Innopolis University, 420500 Innopolis, Russia
Interests: artificial intelligence; machine learning; optimization; evolutionary algorithms

Special Issue Information

Dear Colleagues,

The use of data-driven approaches for decision making and process management has become ubiquitous in several fields ranging from manufacturing to software development. Moreover, there has been a growing body of literature focusing on techniques for collecting data and designing key performance indicators that can provide helpful insights into the application at hand. This Special Issue focuses on presenting state-of-the-art research on the effectiveness of statistical models, features and indicators to increase efficiency in software engineering. Authors are encouraged to submit high-quality work addressing recurrent and challenging issues to predict, identify and mitigate errors and risks.

Prof. Dr. Manuel Mazzara
Prof. Dr. Adriano Bessa Albuquerque
Dr. Luiz Jonata Pires de Araujo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data science
  • software engineering
  • machine learning
  • artificial intelligence
  • statistical models
  • risk management

Published Papers (3 papers)

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Research

23 pages, 2280 KiB  
Article
Forecasting the Risk Factor of Frontier Markets: A Novel Stacking Ensemble of Neural Network Approach
by Mst. Shapna Akter, Hossain Shahriar, Reaz Chowdhury and M. R. C. Mahdy
Future Internet 2022, 14(9), 252; https://doi.org/10.3390/fi14090252 - 25 Aug 2022
Cited by 10 | Viewed by 2073
Abstract
Forecasting the risk factor of the financial frontier markets has always been a very challenging task. Unlike an emerging market, a frontier market has a missing parameter named “volatility”, which indicates the market’s risk and as a result of the absence of this [...] Read more.
Forecasting the risk factor of the financial frontier markets has always been a very challenging task. Unlike an emerging market, a frontier market has a missing parameter named “volatility”, which indicates the market’s risk and as a result of the absence of this missing parameter and the lack of proper prediction, it has almost become difficult for direct customers to invest money in frontier markets. However, the noises, seasonality, random spikes and trends of the time-series datasets make it even more complicated to predict stock prices with high accuracy. In this work, we have developed a novel stacking ensemble of the neural network model that performs best on multiple data patterns. We have compared our model’s performance with the performance results obtained by using some traditional machine learning ensemble models such as Random Forest, AdaBoost, Gradient Boosting Machine and Stacking Ensemble, along with some traditional deep learning models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term (BiLSTM). We have calculated the missing parameter named “volatility” using stock price (Close price) for 20 different companies of the frontier market and then made predictions using the aforementioned machine learning ensemble models, deep learning models and our proposed stacking ensemble of the neural network model. The statistical evaluation metrics RMSE and MAE have been used to evaluate the performance of the models. It has been found that our proposed stacking ensemble neural network model outperforms all other traditional machine learning and deep learning models which have been used for comparison in this paper. The lowest RMSE and MAE values we have received using our proposed model are 0.3626 and 0.3682 percent, respectively, and the highest RMSE and MAE values are 2.5696 and 2.444 percent, respectively. The traditional ensemble learning models give the highest RMSE and MAE error rate of 20.4852 and 20.4260 percent, while the deep learning models give 15.2332 and 15.1668 percent, respectively, which clearly states that our proposed model provides a very low error value compared with the traditional models. Full article
(This article belongs to the Special Issue Machine Learning for Software Engineering)
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21 pages, 2475 KiB  
Article
High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method
by Shangkun Deng, Yingke Zhu, **aoru Huang, Shuangyang Duan and Zhe Fu
Future Internet 2022, 14(6), 180; https://doi.org/10.3390/fi14060180 - 9 Jun 2022
Cited by 6 | Viewed by 2898
Abstract
Futures price-movement-direction forecasting has always been a significant and challenging subject in the financial market. In this paper, we propose a combination approach that integrates the XGBoost (eXtreme Gradient Boosting), SMOTE (Synthetic Minority Oversampling Technique), and NSGA-II (Non-dominated Sorting Genetic Algorithm-II) methods. We [...] Read more.
Futures price-movement-direction forecasting has always been a significant and challenging subject in the financial market. In this paper, we propose a combination approach that integrates the XGBoost (eXtreme Gradient Boosting), SMOTE (Synthetic Minority Oversampling Technique), and NSGA-II (Non-dominated Sorting Genetic Algorithm-II) methods. We applied the proposed approach on the direction prediction and simulation trading of rebar futures, which are traded on the Shanghai Futures Exchange. Firstly, the minority classes of the high-frequency rebar futures price change magnitudes are oversampled using the SMOTE algorithm to overcome the imbalance problem of the class data. Then, XGBoost is adopted to construct a multiclassification model for the price-movement-direction prediction. Next, the proposed approach employs NSGA-II to optimize the parameters of the pre-designed trading rule for trading simulation. Finally, the price-movement direction is predicted, and we conducted the high-frequency trading based on the optimized XGBoost model and the trading rule, with the classification and trading performances empirically evaluated by four metrics over four testing periods. Meanwhile, the LIME (Local Interpretable Model-agnostic Explanations) is applied as a model explanation approach to quantify the prediction contributions of features to the forecasting samples. From the experimental results, we found that the proposed approach performed best in terms of direction prediction accuracy, profitability, and return–risk ratio. The proposed approach could be beneficial for decision-making of the rebar traders and related companies engaged in rebar futures trading. Full article
(This article belongs to the Special Issue Machine Learning for Software Engineering)
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22 pages, 4133 KiB  
Article
Bot-Based Emergency Software Applications for Natural Disaster Situations
by Gabriel Ovando-Leon, Luis Veas-Castillo, Veronica Gil-Costa and Mauricio Marin
Future Internet 2022, 14(3), 81; https://doi.org/10.3390/fi14030081 - 9 Mar 2022
Cited by 3 | Viewed by 3193
Abstract
Upon a serious emergency situation such as a natural disaster, people quickly try to call their friends and family with the software they use every day. On the other hand, people also tend to participate as a volunteer for rescue purposes. It is [...] Read more.
Upon a serious emergency situation such as a natural disaster, people quickly try to call their friends and family with the software they use every day. On the other hand, people also tend to participate as a volunteer for rescue purposes. It is unlikely and impractical for these people to download and learn to use an application specially designed for aid processes. In this work, we investigate the feasibility of including bots, which provide a mechanism to get inside the software that people use daily, to develop emergency software applications designed to be used by victims and volunteers during stressful situations. In such situations, it is necessary to achieve efficiency, scalability, fault tolerance, elasticity, and mobility between data centers. We evaluate three bot-based applications. The first one, named Jayma, sends information about affected people during the natural disaster to a network of contacts. The second bot-based application, Ayni, manages and assigns tasks to volunteers. The third bot-based application named Rimay registers volunteers and manages campaigns and emergency tasks. The applications are built using common practice for distributed software architecture design. Most of the components forming the architecture are from existing public domain software, and some components are even consumed as an external service as in the case of Telegram. Moreover, the applications are executed on commodity hardware usually available from universities. We evaluate the applications to detect critical tasks, bottlenecks, and the most critical resource. Results show that Ayni and Rimay tend to saturate the CPU faster than other resources. Meanwhile, the RAM memory tends to reach the highest utilization level in the Jayma application. Full article
(This article belongs to the Special Issue Machine Learning for Software Engineering)
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