Software Engineering and Data Science

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 (23 May 2022) | Viewed by 28199

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Department of Theoretical and Applied Sciences (DiSTA), University of Insubria, 21100 Varese, Italy
Interests: software quality; big data; data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue titled “Software Engineering and Data Science” of the Future Internet Journal is devoted to recent trends and advancements made in the field of engineering data-intensive software solutions. In the last few years, data-driven software solutions have obtained a lot of attention in research and development at academic, industry, business, and government levels, in order to exploit the hidden knowledge and big data that can be offered to cities and citizens in the future. However, data-driven software solutions are different from “traditional” software development projects, as the focus of the main development core is on managing data (e.g., data store and data quality) and designing behavioral models with the aid of artificial intelligence and machine learning techniques. To this end, new life-cycles, algorithms, methods, processes, and tools are required. Original and innovative ideas that stress all phases in the life-cycle of data-driven software solutions are invited to this Special Issue in order to effectively contribute to addressing these challenges in developing, testing, and maintaining such systems.

Dr. Davide Tosi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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

  • software life-cycle
  • data science
  • big data
  • data analysis
  • artificial intelligence
  • data driven development
  • machine learning
  • agile development
  • DevOps

Related Special Issue

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research

2 pages, 170 KiB  
Editorial
Editorial for the Special Issue on “Software Engineering and Data Science”
Future Internet 2022, 14(11), 306; https://doi.org/10.3390/fi14110306 - 26 Oct 2022
Cited by 1 | Viewed by 810
Abstract
In the last few years, data-driven software solutions have attracted a lot of attention in research and development at academic, industry, business, and government levels to exploit the hidden knowledge and big data that can be offered to cities and citizens in the [...] Read more.
In the last few years, data-driven software solutions have attracted a lot of attention in research and development at academic, industry, business, and government levels to exploit the hidden knowledge and big data that can be offered to cities and citizens in the future [...] Full article
(This article belongs to the Special Issue Software Engineering and Data Science)

Research

Jump to: Editorial

19 pages, 907 KiB  
Article
Fast Library Recommendation in Software Dependency Graphs with Symmetric Partially Absorbing Random Walks
Future Internet 2022, 14(5), 124; https://doi.org/10.3390/fi14050124 - 20 Apr 2022
Cited by 2 | Viewed by 1852
Abstract
To help developers discover libraries suited to their software projects, automated approaches often start from already employed libraries and recommend more based on co-occurrence patterns in other projects. The most accurate project–library recommendation systems employ Graph Neural Networks (GNNs) that learn latent node [...] Read more.
To help developers discover libraries suited to their software projects, automated approaches often start from already employed libraries and recommend more based on co-occurrence patterns in other projects. The most accurate project–library recommendation systems employ Graph Neural Networks (GNNs) that learn latent node representations for link prediction. However, GNNs need to be retrained when dependency graphs are updated, for example, to recommend libraries for new projects, and are thus unwieldy for scalable deployment. To avoid retraining, we propose that recommendations can instead be performed with graph filters; by analyzing dependency graph dynamics emulating human-driven library discovery, we identify low-pass filtering with memory as a promising direction and introduce a novel filter, called symmetric partially absorbing random walks, which infers rather than trains the parameters of filters with node-specific memory to guarantee low-pass filtering. Experiments on a dependency graph between Android projects and third-party libraries show that our approach makes recommendations with a quality and diversification loosely comparable to those state-of-the-art GNNs without computationally intensive retraining for new predictions. Full article
(This article belongs to the Special Issue Software Engineering and Data Science)
Show Figures

Figure 1

14 pages, 413 KiB  
Article
Exploring the Benefits of Combining DevOps and Agile
Future Internet 2022, 14(2), 63; https://doi.org/10.3390/fi14020063 - 19 Feb 2022
Cited by 17 | Viewed by 9707
Abstract
The combined adoption of Agile and DevOps enables organizations to cope with the increasing complexity of managing customer requirements and requests. It fosters the emergence of a more collaborative and Agile framework to replace the waterfall models applied to software development flow and [...] Read more.
The combined adoption of Agile and DevOps enables organizations to cope with the increasing complexity of managing customer requirements and requests. It fosters the emergence of a more collaborative and Agile framework to replace the waterfall models applied to software development flow and the separation of development teams from operations. This study aims to explore the benefits of the combined adoption of both models. A qualitative methodology is adopted by including twelve case studies from international software engineering companies. Thematic analysis is employed in identifying the benefits of the combined adoption of both paradigms. The findings reveal the existence of twelve benefits, highlighting the automation of processes, improved communication between teams, and reduction in time to market through process integration and shorter software delivery cycles. Although they address different goals and challenges, the Agile and DevOps paradigms when properly combined and aligned can offer relevant benefits to organizations. The novelty of this study lies in the systematization of the benefits of the combined adoption of Agile and DevOps considering multiple perspectives of the software engineering business environment. Full article
(This article belongs to the Special Issue Software Engineering and Data Science)
Show Figures

Graphical abstract

29 pages, 1813 KiB  
Article
Multi-Attribute Decision Making for Energy-Efficient Public Transport Network Selection in Smart Cities
Future Internet 2022, 14(2), 42; https://doi.org/10.3390/fi14020042 - 26 Jan 2022
Cited by 8 | Viewed by 2539
Abstract
Smart cities use many smart devices to facilitate the well-being of society by different means. However, these smart devices create great challenges, such as energy consumption and carbon emissions. The proposed research lies in communication technologies to deal with big data-driven applications. Aiming [...] Read more.
Smart cities use many smart devices to facilitate the well-being of society by different means. However, these smart devices create great challenges, such as energy consumption and carbon emissions. The proposed research lies in communication technologies to deal with big data-driven applications. Aiming at multiple sources of big data in a smart city, we propose a public transport-assisted data-dissemination system to utilize public transport as another communication medium, along with other networks, with the help of software-defined technology. Our main objective is to minimize energy consumption with the maximum delivery of data. A multi-attribute decision-making strategy is adopted for the selction of the best network among wired, wireless, and public transport networks, based upon users’ requirements and different services. Once public transport is selected as the best network, the Capacitated Vehicle Routing Problem (CVRP) will be implemented to offload data onto buses as per the maximum capacity of buses. For validation, the case of Auckland Transport is used to offload data onto buses for energy-efficient delay-tolerant data transmission. Experimental results show that buses can be utilized efficiently to deliver data as per their demands and consume 33% less energy in comparison to other networks. Full article
(This article belongs to the Special Issue Software Engineering and Data Science)
Show Figures

Figure 1

16 pages, 5482 KiB  
Article
A Bayesian Analysis of the Inversion of the SARS-COV-2 Case Rate in the Countries of the 2020 European Football Championship
Future Internet 2021, 13(8), 212; https://doi.org/10.3390/fi13080212 - 17 Aug 2021
Cited by 3 | Viewed by 1966
Abstract
While Europe was beginning to deal with the resurgence of COVID-19 due to the Delta variant, the European football championship took place from 11 June to 11 July 2021. We studied the inversion in the decreased/increased rate of new SARS-COV-2 infections in the [...] Read more.
While Europe was beginning to deal with the resurgence of COVID-19 due to the Delta variant, the European football championship took place from 11 June to 11 July 2021. We studied the inversion in the decreased/increased rate of new SARS-COV-2 infections in the countries of the tournament, investigating the hypothesis of an association. Using a Bayesian piecewise regression with a Poisson generalized linear model, we looked for a changepoint in the timeseries of the new SARS-COV-2 cases of each country, expecting it to appear not later than two to three weeks after the date of their first match. The two slopes, before and after the changepoint, were used to discuss the reversal from a decreasing to an increasing rate of the infections. For 17 out of 22 countries (77%) the changepoint came on average 14.97 days after their first match (95% CI 12.29–17.47). For all those 17 countries, the changepoint coincides with an inversion from a decreasing to an increasing rate of the infections. Before the changepoint, the new cases were decreasing, halving on average every 18.07 days (95% CI 11.81–29.42). After the changepoint, the cases begin to increase, doubling every 29.10 days (95% CI 14.12–9.78). This inversion in the SARS-COV-2 case rate, which happened during the tournament, provides evidence in favor of a relationship. Full article
(This article belongs to the Special Issue Software Engineering and Data Science)
Show Figures

Figure 1

28 pages, 452 KiB  
Article
Ontology-Based Feature Selection: A Survey
Future Internet 2021, 13(6), 158; https://doi.org/10.3390/fi13060158 - 18 Jun 2021
Cited by 10 | Viewed by 3244
Abstract
The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information. At its core, the concept of ontology provides the means to semantically describe and structure information, and expose it to [...] Read more.
The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information. At its core, the concept of ontology provides the means to semantically describe and structure information, and expose it to software and human agents in a machine and human-readable form. For software agents to be realized, it is crucial to develop powerful artificial intelligence and machine-learning techniques, able to extract knowledge from information sources, and represent it in the underlying ontology. This survey aims to provide insight into key aspects of ontology-based knowledge extraction from various sources such as text, databases, and human expertise, realized in the realm of feature selection. First, common classification and feature selection algorithms are presented. Then, selected approaches, which utilize ontologies to represent features and perform feature selection and classification, are described. The selective and representative approaches span diverse application domains, such as document classification, opinion mining, manufacturing, recommendation systems, urban management, information security systems, and demonstrate the feasibility and applicability of such methods. This survey, in addition to the criteria-based presentation of related works, contributes a number of open issues and challenges related to this still active research topic. Full article
(This article belongs to the Special Issue Software Engineering and Data Science)
Show Figures

Figure 1

12 pages, 3467 KiB  
Article
How Schools Affected the COVID-19 Pandemic in Italy: Data Analysis for Lombardy Region, Campania Region, and Emilia Region
Future Internet 2021, 13(5), 109; https://doi.org/10.3390/fi13050109 - 27 Apr 2021
Cited by 8 | Viewed by 5786
Abstract
Background: Coronavirus Disease 2019 (COVID-19) is the main discussed topic worldwide in 2020 and at the beginning of the Italian epidemic, scientists tried to understand the virus diffusion and the epidemic curve of positive cases with controversial findings and numbers. Objectives: In this [...] Read more.
Background: Coronavirus Disease 2019 (COVID-19) is the main discussed topic worldwide in 2020 and at the beginning of the Italian epidemic, scientists tried to understand the virus diffusion and the epidemic curve of positive cases with controversial findings and numbers. Objectives: In this paper, a data analytics study on the diffusion of COVID-19 in Lombardy Region and Campania Region is developed in order to identify the driver that sparked the second wave in Italy. Methods: Starting from all the available official data collected about the diffusion of COVID-19, we analyzed Google mobility data, school data and infection data for two big regions in Italy: Lombardy Region and Campania Region, which adopted two different approaches in opening and closing schools. To reinforce our findings, we also extended the analysis to the Emilia Romagna Region. Results: The paper shows how different policies adopted in school opening/closing may have had an impact on the COVID-19 spread, while other factors related to citizen mobility did not affect the second Italian wave. Conclusions: The paper shows that a clear correlation exists between the school contagion and the subsequent temporal overall contagion in a geographical area. Moreover, it is clear that highly populated provinces have the greatest spread of the virus. Full article
(This article belongs to the Special Issue Software Engineering and Data Science)
Show Figures

Figure 1

Back to TopTop