scholarly journals Testing machine learning based systems: a systematic mapping

2020 ◽  
Vol 25 (6) ◽  
pp. 5193-5254
Author(s):  
Vincenzo Riccio ◽  
Gunel Jahangirova ◽  
Andrea Stocco ◽  
Nargiz Humbatova ◽  
Michael Weiss ◽  
...  
Keyword(s):  
Machine Learning ◽  
Testing Machine ◽  
Empirical Evaluation ◽  
Autonomous Driving ◽  
Research Area ◽  
Future Research ◽  
Systematic Mapping Study ◽  
Systematic Mapping ◽  
Data Set ◽  
Ongoing Research

Abstract Context: A Machine Learning based System (MLS) is a software system including one or more components that learn how to perform a task from a given data set. The increasing adoption of MLSs in safety critical domains such as autonomous driving, healthcare, and finance has fostered much attention towards the quality assurance of such systems. Despite the advances in software testing, MLSs bring novel and unprecedented challenges, since their behaviour is defined jointly by the code that implements them and the data used for training them. Objective: To identify the existing solutions for functional testing of MLSs, and classify them from three different perspectives: (1) the context of the problem they address, (2) their features, and (3) their empirical evaluation. To report demographic information about the ongoing research. To identify open challenges for future research. Method: We conducted a systematic mapping study about testing techniques for MLSs driven by 33 research questions. We followed existing guidelines when defining our research protocol so as to increase the repeatability and reliability of our results. Results: We identified 70 relevant primary studies, mostly published in the last years. We identified 11 problems addressed in the literature. We investigated multiple aspects of the testing approaches, such as the used/proposed adequacy criteria, the algorithms for test input generation, and the test oracles. Conclusions: The most active research areas in MLS testing address automated scenario/input generation and test oracle creation. MLS testing is a rapidly growing and developing research area, with many open challenges, such as the generation of realistic inputs and the definition of reliable evaluation metrics and benchmarks.

scholarly journals Global and Latin American female participation in evidence-based software engineering: a systematic mapping study

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Katia Romero Felizardo ◽  
Amanda Möhring Ramos ◽  
Claudia de O. Melo ◽  
Érica Ferreira de Souza ◽  
Nandamudi L. Vijaykumar ◽  
...  
Keyword(s):  
Software Engineering ◽  
Latin American ◽  
Gender Issue ◽  
Research Area ◽  
Systematic Mapping Study ◽  
Evidence Based ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Participation Of Women ◽  
New Generation

Abstract Context While the digital economy requires a new generation of technology for scientists and practitioners, the software engineering (SE) field faces a gender crisis. SE research is a global enterprise that requires the participation of both genders for the advancement of science and evidence-based practice. However, women across the world tend to be significantly underrepresented in such research, receiving less funding and less participation, frequently, than men as authors in research publications. Data about this phenomenon is still sparse and incomplete; particularly in evidence-based software engineering (EBSE), there are no studies that analyze the participation of women in this research area. Objective The objective of this work is to present the results of a systematic mapping study (SM) conducted to collect and evaluate evidence on female researchers who have contributed to the area of EBSE. Method Our SM was performed by manually searching studies in the major conferences and journals of EBSE. We identified 981 studies and 183 were authored/co-authored by women and, therefore, included. Results Contributions from women in secondary studies have globally increased over the years, but it is still concentrated in European countries. Additionally, collaboration among research groups is still fragile, based on a few women as a bridge. Latin American researchers contribute a great deal to the field, despite they do not collaborate as much within their region. Conclusions The findings from this study are expected to be aggregated to the existing knowledge with respect to women’s contribution to the EBSE area. We expect that our results bring up a reflection on the gender issue and motivate actions and policies to attract female researchers to this area.

scholarly journals THE RESEARCH DOMAIN OF PRODUCT-SERVICE SYSTEMS AND VOICE OF THE CUSTOMER: A SYSTEMATIC MAPPING

2021 ◽  
Vol 1 ◽  
pp. 3101-3110
Author(s):  
Carl Nils Konrad Toller ◽  
Marco Bertoni
Keyword(s):  
Research Area ◽  
Service Systems ◽  
Future Research ◽  
Systematic Mapping ◽  
Progressive Development ◽  
Development Cycle ◽  
Product Service ◽  
Product Service Systems ◽  
Research Domain ◽  
Voice Of The Customer

AbstractProduct-Service Systems (PSS) have emerged as a key concept to meet the societal and market trends of increasing customer needs through the entire life-cycle. Unfortunately, several companies are struggling with getting revenues from service investments and translating 'real needs' to design improvements. The demand of the designer to go beyond the Voice of the Customer (VoC) is evident. This paper aims to map the interventions proposed by research in the area of PSS and VoC. Using a systematic mapping approach, the research domain was analyzed with regards to context and interventions. The results show a progressive development in the research area with a focus on the specification and realization of needs. A gap exists in connecting the engineers with 'real needs' and integrating the customer as a natural part of the entire development cycle of a PSS. By performing a systematic mapping, future research can be more focused and hopefully increasing its impact.

Theories of Meaning in Schema Matching

2009 ◽  
pp. 282-308
Author(s):  
Joerg Evermann
Keyword(s):  
Theoretical Basis ◽  
Research Area ◽  
Schema Matching ◽  
Future Research ◽  
Database Integration ◽  
Ongoing Research ◽  
Matching Methods ◽  
The Past ◽  
Theories Of Meaning

Schema matching is the identification of database elements with similar meaning as preparation for subsequent database integration. Over the past 20 years, different schema-matching methods have been proposed and have been shown to be successful to various degrees. However, schema matching is an ongoing research area and the problem is not yet considered to be solved. This article reviews existing schema-matching methods from the perspective of theories of meanings drawn from philosophy and psychology. It positions existing methods, raises questions for future research based on these theories, and shows how these theories can form a firm theoretical basis as well as guide future schema-matching research.

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

A Systematic Mapping on the Learning Analytics Field and Its Analysis in the Massive Open Online Courses Context

2015 ◽  
Vol 13 (3) ◽  
pp. 1-24 ◽  
Author(s):  
Barbara Moissa ◽  
Isabela Gasparini ◽  
Avanilde Kemczinski
Keyword(s):  
Learning Analytics ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Future Research ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Data Types ◽  
Massive Open Online ◽  
Systematic Mapping ◽  
Dynamical Processes

Learning Analytics (LA) is a field that aims to optimize learning through the study of dynamical processes occurring in the students' context. It covers the measurement, collection, analysis and reporting of data about students and their contexts. This study aims at surveying existing research on LA to identify approaches, topics, and needs for future research. A systematic mapping study is launched to find as much literature as possible. The 127 papers found (resulting in 116 works) are classified with respect to goals, data types, techniques, stakeholders and interventions. Despite the increasing interest in field, there are no studies relating it to the Massive Open Online Courses (MOOCs) context. The goal of this paper is twofold, first we present the systematic mapping on LA and after we analyze its findings in the MOOCs context. As results we provide an overview of LA and identify perspectives and challenges in the MOOCs context.

scholarly journals Vehicle Price Prediction using SVM Techniques

2020 ◽  
Vol 9 (8) ◽  
pp. 398-401
Keyword(s):  
Machine Learning ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Set ◽  
Network Support ◽  
Java Application ◽  
Learning Techniques ◽  
The Individual

The prediction of price for a vehicle has been more popular in research area, and it needs predominant effort and information about the experts of this particular field. The number of different attributes is measured and also it has been considerable to predict the result in more reliable and accurate. To find the price of used vehicles a well defined model has been developed with the help of three machine learning techniques such as Artificial Neural Network, Support Vector Machine and Random Forest. These techniques were used not on the individual items but for the whole group of data items. This data group has been taken from some web portal and that same has been used for the prediction. The data must be collected using web scraper that was written in PHP programming language. Distinct machine learning algorithms of varying performances had been compared to get the best result of the given data set. The final prediction model was integrated into Java application

scholarly journals Visualization Methods of 3D Plant Models: A Systematic Mapping Study

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Shilin Di ◽  
Lingzhi Cao ◽  
Yongqi Ge
Keyword(s):  
Growth Process ◽  
Three Dimensional ◽  
Future Research ◽  
Systematic Mapping Study ◽  
Plant Structure ◽  
Phenotypic Characteristics ◽  
Systematic Mapping ◽  
Research Directions ◽  
Research Types ◽  
Model Visualization

Three-dimensional plant model visualization (3D-PMV) is based on plant biology and the plant structure construction model for virtual simulation three-dimensional display, reflecting plant structure characteristics and demonstrating the growth process. In this work, we used the method of systematic mapping study (SMS) to screen the relevant literature in order to explore and analyze the methods and goals of virtual plant visualization research and to provide assistance in future literature reviews. To this end, we conducted extensive searches to identify articles related to plant model visualization technology. We searched for papers from July 2010 to November 2020 from four mainstream databases, namely, ACM, IEEE Xplore, EI, and Web of Science, and found more than 2,900 papers on 3D-PMV. Finally, we selected 60 qualified papers. We mainly followed the SMS method to classify papers by answering seven questions, mainly extracting data for analysis on research types, research goals, model construction methods, visual model categories, number of publications, tools, and methods. The results show that solution proposals account for the largest proportion of research types, accounting for 75% of the total number of papers. This shows that the focus is on the improvement of original methods or the proposal of new methods in this field; research goals of research plant phenotypic characteristics account for the total 35%; 28% of the research objectives focus on showing the reflection of plant growth process, indicating that the research objective trends in this research field regard plant phenotypic characteristics and visualization of the growth process; static type model visualization also accounts for 83% of the total, indicating that the related research on visualization in this field is mostly static typing. Therefore, our work not only analyzes the challenges faced by 3D-PVM and the research directions that need more attention in the future but also provides some suggestions for researchers to find suitable research directions in this field. As the future direction of development, researchers need to pay more attention to the study of dynamic visualization methods of plant growth. We believe that for future research, it is indispensable to provide 3D-PMV with research methods, research goals, visualization model types, development, and other tools. This article analyzes the results of the extracted data based on the SMS method and makes an important contribution to the researcher's extensive understanding of the current status of 3D-PMV and the potential future research opportunities in the field.

scholarly journals Cardiac Telocytes 16 Years on—What Have We Learned So Far, and How Close Are We to Routine Application of the Knowledge in Cardiovascular Regenerative Medicine?

2021 ◽  
Vol 22 (20) ◽  
pp. 10942
Author(s):  
Martin Klein ◽  
Mária Csöbönyeiová ◽  
Stanislav Žiaran ◽  
Ľuboš Danišovič ◽  
Ivan Varga
Keyword(s):  
Myocardial Infarction ◽  
Stem Cell ◽  
Cell Therapy ◽  
Human Subjects ◽  
Research Area ◽  
Future Research ◽  
Ongoing Research ◽  
Routine Application ◽  
Cardiovascular Regeneration ◽  
A Cell

The regeneration of a diseased heart is one of the principal challenges of modern cardiovascular medicine. There has been ongoing research on stem-cell-based therapeutic approaches. A cell population called telocytes (TCs) described only 16 years ago largely contributed to the research area of cardiovascular regeneration. TCs are cells with small bodies and extremely long cytoplasmic projections called telopodes, described in all layers of the heart wall. Their functions include cell-to-cell signaling, stem-cell nursing, mechanical support, and immunoregulation, to name but a few. The functional derangement or quantitative loss of TCs has been implicated in the pathogenesis of myocardial infarction, heart failure, arrhythmias, and many other conditions. The exact pathomechanisms are still unknown, but the loss of regulative, integrative, and nursing functions of TCs may provide important clues. Therefore, a viable avenue in the future modern management of these conditions is TC-based cell therapy. TCs have been previously transplanted into a mouse model of myocardial infarction with promising results. Tandem transplantation with stem cells may provide additional benefit; however, many underresearched areas need to be addressed in future research before routine application of TC-based cell therapy in human subjects. These include the standardization of protocols for isolation, cultivation, and transplantation, quantitative optimization of TC transplants, cost-effectivity analysis, and many others.

scholarly journals BIG HEALTH DATA: A SYSTEMATIC MAPPING STUDY

2019 ◽  
Vol XLII-4/W12 ◽  
pp. 113-119
Author(s):  
S. Lbrini ◽  
A. Fadil ◽  
H. Rhinane ◽  
H. J. Oulidi
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Real Time ◽  
Digital Libraries ◽  
Research Area ◽  
Health Data ◽  
Systematic Mapping Study ◽  
Data Systems ◽  
Mapping Study ◽  
Systematic Mapping

<p><strong>Abstract.</strong> The Big Data, a result of the digital revolution, offers several opportunities in the field of health. Indeed, appliances and applications permanently connected to humans and the global digitalization of medical documents produce a vast health data: "Big Health Data". This data is the subject of several projects in the world given the opportunities offered to optimize this area. This paper focuses on quantifying the production of scientific articles about Big Health Data research and the most investigated Big Health Data topics. It also presents a mapping of countries producing articles about this subject. In remote sensing using real time categories, we aimed to quantify articles dealing with “big data architectures”, technologies and data sources used. A systematic mapping study was conducted with a set of seven research questions by investigating articles from two digital libraries: Scopus and Springer. The study concern articles published in 2017 and the first half of 2018. The results are illustrated by diagrams answering each question from which a set of recommendations are concluded in this area of research. The study shows that this Data is used the most in studies of oncology. Statistics show that while remote sensing and monitoring is a hot topic, real-time use is not as interesting. It was found that there’s a lack in studies interested in big data technologies used in real time remote sensing in the field of health. In conclusion, we recommend more focus on research area treating architecture in remote sensing real time Big Health Data systems combined with geolocation.</p>

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