scholarly journals DeBGUer: A Tool for Bug Prediction and Diagnosis

2019 ◽  
Vol 33 ◽  
pp. 9446-9451
Author(s):  
Amir Elmishali ◽  
Roni Stern ◽  
Meir Kalech
Keyword(s):  
Artificial Intelligence ◽  
Fault Prediction ◽  
Web Interface ◽  
Bug Detection ◽  
Web Based ◽  
Test Planning ◽  
Software Bugs ◽  
Planning Algorithm ◽  
Partial Implementation ◽  
Software Bug

In this paper, we present the DeBGUer tool, a web-based tool for prediction and isolation of software bugs. DeBGUer is a partial implementation of the Learn, Diagnose, and Plan (LDP) paradigm, which is a recently introduced paradigm for integrating Artificial Intelligence (AI) in the software bug detection and correction process. In LDP, a diagnosis (DX) algorithm is used to suggest possible explanations – diagnoses – for an observed bug. If needed, a test planning algorithm is subsequently used to suggest further testing. Both diagnosis and test planning algorithms consider a fault prediction model, which associates each software component (e.g., class or method) with the likelihood that it contains a bug. DeBGUer implements the first two components of LDP, bug prediction (Learn) and bug diagnosis (Diagnose). It provides an easy-to-use web interface, and has been successfully tested on 12 projects.

Hybrid Multi-Objective Grey Wolf Search Optimizer and Machine Learning Approach for Software Bug Prediction

2021 ◽  
pp. 314-337
Author(s):  
Mrutyunjaya Panda ◽  
Ahmad Taher Azar
Keyword(s):  
Error Detection ◽  
Grey Wolf ◽  
Feature Selection Technique ◽  
Bug Detection ◽  
Software Bugs ◽  
Search Optimization ◽  
Multi Class Classification ◽  
Software Bug Prediction ◽  
Robust Prediction ◽  
Software Bug

Software bugs (or malfunctions) pose a serious threat to software developers with many known and unknown bugs that may be vulnerable to computer systems, demanding new methods, analysis, and techniques for efficient bug detection and repair of new unseen programs at a later stage. This chapter uses evolutionary grey wolf (GW) search optimization as a feature selection technique to improve classifier efficiency. It is also envisaged that software error detection would consider the nature of the error when repairing it for remedial action instead of simply finding it either faulty or non-defective. To address this problem, the authors use bug severity multi-class classification to build an efficient and robust prediction model using multilayer perceptron (MLP), logistic regression (LR), and random forest (RF) for bug severity classification. Both tests are performed on two software error datasets, namely Ant 1.7 and Tomcat.

Artificial intelligence-based automatic visual inspection system for built heritage

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lukman E. Mansuri ◽  
D.A. Patel
Keyword(s):  
Artificial Intelligence ◽  
Visual Inspection ◽  
Image Data ◽  
Inspection System ◽  
Data Set ◽  
Web Based ◽  
Content Type ◽  
Built Heritage ◽  
Automatic Visual Inspection ◽  
Inspection Systems

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

scholarly journals Physicians’ requirements and expectations of future Artificial Intelligence applications for healthcare – a web-based survey in German university hospitals (Preprint)

2020 ◽  
Author(s):  
Oliver Maassen ◽  
Sebastian Fritsch ◽  
Julia Gantner ◽  
Saskia Deffge ◽  
Julian Kunze ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Patient Care ◽  
Biomedical Research ◽  
Positive Attitude ◽  
Patient Data ◽  
University Hospitals ◽  
Web Based ◽  
Wide Range ◽  
Medical Disciplines

BACKGROUND The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of healthcare. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research has not been investigated widely in German university hospitals. OBJECTIVE Evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research e.g. for the development of machine learning (ML) algorithms in university hospitals in Germany. METHODS A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given on Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS 121 (39.9%) female and 173 (57.1%) male physicians (N=303) from a wide range of medical disciplines and work experience levels completed the online survey. The majority of respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). 82.5% of respondents (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism, there come several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (e.g., imaging procedures in radiology and pathology) or data is collected continuously (e.g. cardiology and intensive care medicine), physicians’ expectations to substantially improve future patient care are high. However, for the practical usage of AI in healthcare regulatory and organizational challenges still have to be mastered.

scholarly journals KiMoSys 2.0: an upgraded database for submitting, storing and accessing experimental data for kinetic modeling

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Hugo Mochão ◽  
Pedro Barahona ◽  
Rafael S Costa
Keyword(s):  
Experimental Data ◽  
Metabolic Networks ◽  
Model Simulation ◽  
Web Interface ◽  
Concentration Data ◽  
Web Based ◽  
Share Data ◽  
Visualization Tools ◽  
Filter Mechanism ◽  
Machine Readable

Abstract The KiMoSys (https://kimosys.org), launched in 2014, is a public repository of published experimental data, which contains concentration data of metabolites, protein abundances and flux data. It offers a web-based interface and upload facility to share data, making it accessible in structured formats, while also integrating associated kinetic models related to the data. In addition, it also supplies tools to simplify the construction process of ODE (Ordinary Differential Equations)-based models of metabolic networks. In this release, we present an update of KiMoSys with new data and several new features, including (i) an improved web interface, (ii) a new multi-filter mechanism, (iii) introduction of data visualization tools, (iv) the addition of downloadable data in machine-readable formats, (v) an improved data submission tool, (vi) the integration of a kinetic model simulation environment and (vii) the introduction of a unique persistent identifier system. We believe that this new version will improve its role as a valuable resource for the systems biology community. Database URL:  www.kimosys.org

scholarly journals THE CENSUS OF SMS GATEWAY BASED WEB BASED KOTO LUAR PAUH DISTRICTS PADANG CITY

2016 ◽  
Vol 1 (2) ◽  
pp. 169
Author(s):  
Danyl Mallisza
Keyword(s):  
Short Message Service ◽  
Government Performance ◽  
Population Census ◽  
Short Message ◽  
Web Interface ◽  
Web Based ◽  
The World ◽  
Message Service ◽  
Multi Level ◽  
Computerized System

short message service (SMS) plays an important role in the field of IT in the future, SMS GATEWAY can be used as a mobile device for collecting data in the population census in achieving the availability and effectiveness. This research is about the development of software based on short message service (SMS) system to send messages via SMS gateway. The main objective of the proposed system is to provide a multi-level local authentication services to the SMS gateway. This service can be implemented in multi-departmental organizations where SMS service is used for system notifications. The proposed system has a web interface and encryption methods to provide services. Census information system that uses a computerized system is an effort to help smooth in the world of government so that the data obtained more precise and accurate. It can be expected to facilitate the penyensusan population in Koto village of Padang Pauh Subdistrict Affairs effectively, efficiently and accurately to improve government performance.

Visualizing the Bug Distribution Information Available in Software Bug Repositories

2014 ◽  
pp. 48-66
Author(s):  
N. K. Nagwani ◽  
S. Verma
Keyword(s):  
Open Source ◽  
Three Dimensional ◽  
Distribution Patterns ◽  
Two Dimensional ◽  
Software Bugs ◽  
Process Visualization ◽  
Software Information ◽  
Visualization Techniques ◽  
Software Bug ◽  
Bug Repositories

Software repositories contain a wealth of information that can be analyzed for knowledge extraction. Software bug repositories are one such repository that stores the information about the defects identified during the development of software. Information available in software bug repositories like number of bugs priority-wise, component-wise, status-wise, developers-wise, module-wise, summary-terms-wise, can be visualized with the help of two- or three-dimensional graphs. These visualizations help in understanding the bug distribution patterns, software matrices related to the software bugs, and developer information in the bug-fixing process. Visualization techniques are exploited with the help of open source technologies in this chapter to visualize the bug distribution information available in the software bug repositories. Two-dimensional and three-dimensional graphs are generated using java-based open source APIs, namely Jzy3d (Java Easy 3d) and JFreeChart. Android software bug repository is selected for the experimental demonstrations of graphs. The textual bug attribute information is also visualized using frequencies of frequent terms present in it.

Ideas on the Future of Intelligent Web-Based E-Learning

2018 ◽  
pp. 2274-2287
Author(s):  
Utku Kose
Keyword(s):  
Artificial Intelligence ◽  
Teaching And Learning ◽  
Current Status ◽  
Learning Approaches ◽  
Learning Tools ◽  
Web Based ◽  
The Future ◽  
Teachers And Students ◽  
Important Approach ◽  
E Learning

With the outstanding improvements in technology, the number of e-learning applications has increased greatly. This increment is associated with awareness levels of educational institutions on the related improvements and the power of communication and computer technologies to ensure effective and efficient teaching and learning experiences for teachers and students. Consequently, there is a technological flow that changes the standards of e-learning processes and provides better ways to obtain desired educational objectives. When we consider today's widely used technological factors, Web-based e-learning approaches have a special role in directing the educational standards. Improvements among m-learning applications and the popularity of the Artificial Intelligence usage for educational works have given great momentum to this orientation. In this sense, this chapter provides some ideas on the future of intelligent Web-based e-learning applications by thinking on the current status of the literature. As it is known, current trends in developing Artificial Intelligence-supported e-learning tools continue to shape the future of e-learning. Therefore, it is an important approach to focus on the future. The author thinks that the chapter will be a brief but effective enough reference for similar works, which focus on the future of Artificial Intelligence-supported distance education and e-learning.

Educational Responsibility in the Deepfake Era

2021 ◽  
pp. 1-23
Author(s):  
Rebecca J. Blankenship
Keyword(s):  
Artificial Intelligence ◽  
Instructional Design ◽  
Student Learning ◽  
Learning Outcomes ◽  
Student Learning Outcomes ◽  
Web Based ◽  
Student Work ◽  
Integrated Classrooms ◽  
The Right ◽  
Design Challenges

Choosing the right technologies to match student learning outcomes in today's technology-integrated classrooms presents educators with multiple instructional design challenges including selecting appropriate technologies to match desired student learning outcomes. As students continue to have broad access to information from a variety of web-based platforms, teachers are increasingly tasked with ensuring the information used to complete key assignments is authentic and from a verifiable resource. As such, the era of deep fakes in images, audio, videos, and digital texts is more prevalent than ever as numerous programs using artificial intelligence (AI) can significantly alter original content to fundamentally change the intent of original content. A discussion of educational and pedagogic responsibility in the era of deep fakes will serve as the primer for reform of the TPACK construct with recommendations for remediating student work in which deep fake resources were utilized.

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