scholarly journals FIBR-OSS: fault injection model for bug reports in open-source software

2020 ◽  
Vol 20 (1) ◽  
pp. 465
Author(s):  
Sundos Abdulameer Alazawi ◽  
Mohammed Najim Al-Salam
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Fault Injection ◽  
Failure Prediction ◽  
Machine Learning Techniques ◽  
Significant Advance ◽  
Bug Reports ◽  
Learning Techniques ◽  
Injection Model

<span>For assessment of system dependability, fault injection techniques are used to expedite the presence of an error or failure in the system, which helps evaluate fault tolerance and system failure prediction. Defects classification and prediction is the principal significant advance in the trustworthiness evaluation of complex software systems such as open-source software since it can quickly be affected by the reliability of those systems, improves performance, and lessening the product cost.   In this context, a new prototype of the fault injection model is presented, FIBR-OSS (Fault Injection for Bug Reports in Open-Source Software). FIBR-OSS can support developers to evaluate the system performance during phase's development for its dependability attributes such as reliability and system dependability means such as fault prediction or forecasting. FIBR-OSS is used for fault speed-up to test the system's failure prediction performance. Some machine learning techniques are implemented on bug reports produced existing by the bug tracking system as datasets for failure prediction techniques, some of those machine learning techniques are used in our approach.</span>

scholarly journals Applying Machine Learning Techniques to Predict the Maintainability of Open Source Software

2019 ◽  
Vol 8 (5S3) ◽  
pp. 192-195
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Software Applications ◽  
Learning Techniques ◽  
Software Maintainability ◽  
Quality Aspect

Software maintainability is a vital quality aspect as per ISO standards. This has been a concern since decades and even today, it is of top priority. At present, majority of the software applications, particularly open source software are being developed using Object-Oriented methodologies. Researchers in the earlier past have used statistical techniques on metric data extracted from software to evaluate maintainability. Recently, machine learning models and algorithms are also being used in a majority of research works to predict maintainability. In this research, we performed an empirical case study on an open source software jfreechart by applying machine learning algorithms. The objective was to study the relationships between certain metrics and maintainability.

A Unified Framework for Bug Report Assignment

2019 ◽  
Vol 29 (04) ◽  
pp. 607-628 ◽  
Author(s):  
Yuan Zhao ◽  
Tieke He ◽  
Zhenyu Chen
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Software Testing ◽  
Mobile Internet ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Unified Framework ◽  
Bug Reports ◽  
Learning Techniques ◽  
Bug Report

It is typically a manual, time-consuming, and tedious task of assigning bug reports to individual developers. Although some machine learning techniques are adopted to alleviate this dilemma, they are mainly focused on the open source projects, which use traditional repositories such as Bugzilla to manage their bug reports. With the boom of the mobile Internet, some new requirements and methods of software testing are emerging, especially the crowdsourced testing. Unlike the traditional channels, whose bug reports are often heavyweight, which means their bug reports are standardized with detailed attribute localization, bug reports tend to be lightweight in the context of crowdsourced testing. To exploit the differences of the bug reports assignment in the new settings, a unified bug reports assignment framework is proposed in this paper. This framework is capable of handling both the traditional heavyweight bug reports and the lightweight ones by (i) first preprocessing the bug reports and feature selections, (ii) then tuning the parameters that indicate the ratios of choosing different methods to vectorize bug reports, (iii) and finally applying classification algorithms to assign bug reports. Extensive experiments are conducted on three datasets to evaluate the proposed framework. The results indicate the applicability of the proposed framework, and also reveal the differences of bug report assignment between traditional repositories and crowdsourced ones.

Emergency Department Visit Forecasting and Dynamic Nursing Staff Allocation Using Machine Learning Techniques With Readily Available Open-Source Software

2015 ◽  
Vol 33 (8) ◽  
pp. 368-377 ◽  
Author(s):  
ALEXANDER ZLOTNIK ◽  
ASCENSIÓN GALLARDO-ANTOLÍN ◽  
MIGUEL CUCHÍ ALFARO ◽  
MARÍA CARMEN PÉREZ PÉREZ ◽  
JUAN MANUEL MONTERO MARTÍNEZ
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Open Source ◽  
Emergency Department Visit ◽  
Nursing Staff ◽  
Open Source Software ◽  
Machine Learning Techniques ◽  
Staff Allocation ◽  
Learning Techniques

scholarly journals The Waikato Open Source Frameworks (WEKA and MOA) for Machine Learning Techniques

2021 ◽  
Vol 1804 (1) ◽  
pp. 012133
Author(s):  
Mahmood Shakir Hammoodi ◽  
Hasanain Ali Al Essa ◽  
Wial Abbas Hanon
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Machine Learning Techniques ◽  
Learning Techniques

An Empirical Comparison of Machine Learning Techniques in Predicting the Bug Severity of Open and Closed Source Projects

2012 ◽  
Vol 4 (2) ◽  
pp. 32-59 ◽  
Author(s):  
K. K. Chaturvedi ◽  
V.B. Singh
Keyword(s):  
Machine Learning ◽  
Tracking System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Severity Level ◽  
Bug Reports ◽  
Learning Techniques ◽  
Bug Report ◽  
Closed Source ◽  
F Measure

Bug severity is the degree of impact that a defect has on the development or operation of a component or system, and can be classified into different levels based on their impact on the system. Identification of severity level can be useful for bug triager in allocating the bug to the concerned bug fixer. Various researchers have attempted text mining techniques in predicting the severity of bugs, detection of duplicate bug reports and assignment of bugs to suitable fixer for its fix. In this paper, an attempt has been made to compare the performance of different machine learning techniques namely Support vector machine (SVM), probability based Naïve Bayes (NB), Decision Tree based J48 (A Java implementation of C4.5), rule based Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and Random Forests (RF) learners in predicting the severity level (1 to 5) of a reported bug by analyzing the summary or short description of the bug reports. The bug report data has been taken from NASA’s PITS (Projects and Issue Tracking System) datasets as closed source and components of Eclipse, Mozilla & GNOME datasets as open source projects. The analysis has been carried out in RapidMiner and STATISTICA data mining tools. The authors measured the performance of different machine learning techniques by considering (i) the value of accuracy and F-Measure for all severity level and (ii) number of best cases at different threshold level of accuracy and F-Measure.

An Analysis of Software Bug Reports Using Machine Learning Techniques

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Ha Manh Tran ◽  
Son Thanh Le ◽  
Sinh Van Nguyen ◽  
Phong Thanh Ho
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Bug Reports ◽  
Learning Techniques ◽  
Software Bug

scholarly journals Developing an Open-Source Lightweight Game Engine with DNN Support

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1421
Author(s):  
Haechan Park ◽  
Nakhoon Baek
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Open Source ◽  
Programming Languages ◽  
Cost Effective ◽  
Machine Learning Techniques ◽  
Learning Technology ◽  
Game Engine ◽  
Learning Techniques ◽  
Technical Issues

With the growth of artificial intelligence and deep learning technology, we have many active research works to apply the related techniques in various fields. To test and apply the latest machine learning techniques in gaming, it will be very useful to have a light-weight game engine for quick prototyping. Our game engine is implemented in a cost-effective way, in comparison to well-known commercial proprietary game engines, by utilizing open source products. Due to its simple internal architecture, our game engine is especially beneficial for modifying and reviewing the new functions through quick and repetitive tests. In addition, the game engine has a DNN (deep neural network) module, with which the proposed game engine can apply deep learning techniques to the game features, through applying deep learning algorithms in real-time. Our DNN module uses a simple C++ function interface, rather than additional programming languages and/or scripts. This simplicity enables us to apply machine learning techniques more efficiently and casually to the game applications. We also found some technical issues during our development with open sources. These issues mostly occurred while integrating various open source products into a single game engine. We present details of these technical issues and our solutions.

scholarly journals NICeSim: An open-source simulator based on machine learning techniques to support medical research on prenatal and perinatal care decision making

2014 ◽  
Vol 62 (3) ◽  
pp. 193-201 ◽  
Author(s):  
Fabio Ribeiro Cerqueira ◽  
Tiago Geraldo Ferreira ◽  
Alcione de Paiva Oliveira ◽  
Douglas Adriano Augusto ◽  
Eduardo Krempser ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Medical Research ◽  
Open Source ◽  
Perinatal Care ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Care Decision
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