scholarly journals Stability Assessment Method Considering Fault Fixing Time in Open Source Project

2020 ◽  
Vol 5 (4) ◽  
pp. 591-601
Author(s):  
Hironobu Sone ◽  
Yoshinobu Tamura ◽  
Shigeru Yamada
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Transition Probability ◽  
Assessment Method ◽  
Project Managers ◽  
Open Source Project ◽  
Proposed Model ◽  
Software Reliability Growth ◽  
The Stability ◽  
Project Data

Recently, open source software (OSS) are adopted various situations because of quick delivery, cost reduction and standardization of systems. Many OSS are developed under the peculiar development style known as bazaar method. According to this method, faults are detected and fixed by users and developers around the world, and the fixed result will be reflected in the next release. Also, the fix time of faults tends to be shorter as the development of OSS progresses. However, several large-scale open source projects have a problem that faults fixing takes a lot of time because faults corrector cannot handle many faults reports quickly. Furthermore, imperfect fault fixing sometimes occurs because the fault fixing is performed by various people and environments. Therefore, OSS users and project managers need to know the stability degree of open source projects by grasping the fault fixing time. In this paper, for assessment stability of large-scale open source project, we derive the imperfect fault fixing probability and the transition probability distribution. For derivation, we use the software reliability growth model based on the Wiener process considering that the fault fixing time in open source projects changes depending on various factors such as the fault reporting time and the assignees for fixing faults. In addition, we applied the proposed model to actual open source project data and examined the validity of the model.

scholarly journals Prediction of Fault Fix Time Transition in Large-Scale Open Source Project Data

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 109
Author(s):  
Hironobu Sone ◽  
Yoshinobu Tamura ◽  
Shigeru Yamada
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Cost Reduction ◽  
Large Scale ◽  
Project Managers ◽  
Time Data ◽  
Software Reliability Growth Model ◽  
Software Reliability Growth ◽  
The Stability ◽  
Project Data

Open source software (OSS) programs are adopted as embedded systems regarding their server usage, due to their quick delivery, cost reduction, and standardization of systems. Many OSS programs are developed using the peculiar style known as the bazaar method, in which faults are detected and fixed by developers around the world, and the result is then reflected in the next release. Furthermore, the fix time of faults tends to be shorter as the development of the OSS progresses. However, several large-scale open source projects encounter the problem that fault fixing takes much time because the fault corrector cannot handle many fault reports. Therefore, OSS users and project managers need to know the stability degree of open source projects by determining the fault fix time. In this paper, we predict the transition of the fix time in large-scale open source projects. To make the prediction, we use the software reliability growth model based on the Wiener process considering that the fault fix time in open source projects changes depending on various factors such as the fault reporting time and the assignees to fix the faults. In addition, we discuss the assumption that fault fix time data depend on the prediction of the transition in fault fixing time.

scholarly journals A Method of Fault Fix Priority Identification for Open Source Project

2019 ◽  
Vol 8 (4) ◽  
pp. 2396-2400
Keyword(s):  
Random Forest ◽  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Tracking System ◽  
Detection Accuracy ◽  
Open Source Project ◽  
Report Data ◽  
Bug Tracking ◽  
Project Data

Open source software are adopted as embedded systems, server usage because of quick delivery, cost reduction and standardization of systems. Many open source software are developed under the peculiar development style known as bazaar method. According to this method, faults are detected and fixed by developers around the world, and the fixed result will be reflected in the next release. Also, the fix time of faults tends to be shorter as the development of open source software progresses. However, several large-scale open source projects have a problem that faults fixing takes a lot of time because the faults corrector cannot handle many faults reports quickly. In this paper, we aim to identify the fix priority of newly registered faults in the bug tracking system by using random forest, and we make an index to detect the faults that require high fix priority and long fault fixing time when faults are reported in specific version of open source project. The index is derived and identified by using open source project data obtained from bug tracking system. In addition, we try to improve the detection accuracy of the proposed index by learning not only the specific version but also the fault report data of the past version by using random forest considering the characteristic similarities of faults fix among different versions. As a result, the detection accuracy has highly improved comparing with using only specific version data and using logistic regression

scholarly journals Deep-Reinforcement Learning-Based Co-Evolution in a Predator–Prey System

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 773 ◽  
Author(s):  
Xueting Wang ◽  
Jun Cheng ◽  
Lei Wang
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Behavior Pattern ◽  
Learning Ability ◽  
Predator Prey ◽  
Complex Processes ◽  
Proposed Model ◽  
The Stability ◽  
Reasonable Behavior ◽  
Evolutionary Networks

Understanding or estimating the co-evolution processes is critical in ecology, but very challenging. Traditional methods are difficult to deal with the complex processes of evolution and to predict their consequences on nature. In this paper, we use the deep-reinforcement learning algorithms to endow the organism with learning ability, and simulate their evolution process by using the Monte Carlo simulation algorithm in a large-scale ecosystem. The combination of the two algorithms allows organisms to use experiences to determine their behavior through interaction with that environment, and to pass on experience to their offspring. Our research showed that the predators’ reinforcement learning ability contributed to the stability of the ecosystem and helped predators obtain a more reasonable behavior pattern of coexistence with its prey. The reinforcement learning effect of prey on its own population was not as good as that of predators and increased the risk of extinction of predators. The inconsistent learning periods and speed of prey and predators aggravated that risk. The co-evolution of the two species had resulted in fewer numbers of their populations due to their potentially antagonistic evolutionary networks. If the learnable predators and prey invade an ecosystem at the same time, prey had an advantage. Thus, the proposed model illustrates the influence of learning mechanism on a predator–prey ecosystem and demonstrates the feasibility of predicting the behavior evolution in a predator–prey ecosystem using AI approaches.

scholarly journals Health Status Assessment for Wind Turbine with Recurrent Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Zexian Sun ◽  
Hexu Sun
Keyword(s):  
Health Status ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Large Scale ◽  
Short Term Memory ◽  
Assessment Method ◽  
Sensory Data ◽  
Proposed Model ◽  
Status Assessment ◽  
Real World Datasets

In order to improve the safety, efficiency, and reliability in large scale wind turbines, a great deal of statistical and machine-learning models for wind turbine health monitoring system (WTHMS) are proposed based on SCADA variables. The data-driven WTHMS have been performed widely with the attentions on predicting the failures of the wind turbine or primary components. However, the health status of wind turbine often degrades gradually rather than suddenly. Thus, the SCADA variables change continuously to the occurrence of certain faults. Inspired by the ability of recurrent neural network (RNN) in redefining the raw sensory data, we introduce a hybrid methodology that combines the analysis of variance for each sequential SCADA variable with RNN to assess the health status of wind turbine. First, each original sequence is split by different variance ranges into several categories to improve the generalized ability of the RNN. Then, the long short-term memory (LSTM) is procured on the normal running sequence to learn the gradually changing situations. Finally, a weighted assessment method incorporating the health of primary components is applied to judge the health level of the wind turbine. Experiments on real-world datasets from two wind turbines demonstrate the effectiveness and generalization of the proposed model.

Evaluating the Effects of Architectural Documentation: A Case Study of a Large Scale Open Source Project

2016 ◽  
Vol 42 (3) ◽  
pp. 220-260 ◽  
Author(s):  
Rick Kazman ◽  
Dennis Goldenson ◽  
Ira Monarch ◽  
William Nichols ◽  
Giuseppe Valetto
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Open Source Project ◽  
Architectural Documentation

scholarly journals Spatial Transformation of the Pig Sector in Hungary and Slovenia: A Comparative Analysis

2021 ◽  
Vol 13 (21) ◽  
pp. 11851
Author(s):  
Arnold Csonka ◽  
Štefan Bojnec ◽  
Imre Fertő
Keyword(s):  
Comparative Analysis ◽  
Large Scale ◽  
Transition Probability ◽  
Herd Size ◽  
Rapid Decline ◽  
Spatial Transformation ◽  
Inequality Measures ◽  
Probability Matrices ◽  
Administrative Units ◽  
The Stability

This paper presents a comparative analysis of the spatial transformation in the Hungarian and Slovenian pig sectors at the level of local administrative units (LAU). Concentration and inequality measures were applied in the empirical analyses, along with Markov transition probability matrices, to examine the stability and/or mobility over time and the presence of clustering effects. Both countries experienced a rapid decline in pig population. This profound structural change has led to a smaller number of more concentrated pig farms and increased territorial concentration. The degree of farm and territorial concentration and inequality in Hungary has been much higher than in Slovenia, and the concentration gap between the countries has increased. Between 2000 and 2010, the degree of concentration was much higher in Hungary than in Slovenia; average herd size per holding increased by 68 percent in Hungary, and only seven percent in Slovenia. In Hungary, clustering effects were particularly significant, with the pig sector moving towards large-scale concentration. The former effect was also confirmed in the Slovenian pig sector, but significantly weakened during the period under investigation. The exploitation and policy management of spatial externalities justifies these agricultural, economic, and agri-environmental practices.

Entropy-Based Two-Dimensional Software Reliability Growth Modeling for Open-Source Software Incorporating Change-Point

2020 ◽  
Vol 27 (05) ◽  
pp. 2040009
Author(s):  
P. K. Kapur ◽  
Saurabh Panwar ◽  
Vivek Kumar ◽  
Ompal Singh
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Reliability ◽  
Source Code ◽  
Entropy Measure ◽  
Two Dimensional ◽  
Field Environment ◽  
Proposed Model ◽  
Software Reliability Growth ◽  
Code Complexity

This study provides an analytical model to predict the fixing pattern of issues in the open-source software (OSS) packages to assist developers in software development and maintenance. Moreover, the continuous evolution of software due to bugs removal, new features addition or existing features modification results in the source code complexity. The proposed model quantifies the complexity in the source code using the Shannon entropy measure. In addition, the issues fixing growth behavior is viewed as a function of continuation time of the software in the field environment and amount of uncertainty or complexity present in the source code. Therefore, a two-dimensional function called Cobb–Douglas production function is applied to model the intensity function of the issues fixing rate. Furthermore, the rate of fixing the different issue types is considered variable that may alter after certain time points. Thus, this study incorporates the concept of multiple change-points to predict and assess the fixing behavior of issues in the software system. The performance of the proposed model is validated by fitting the proposed model to the actual issues data of three open-source projects. Findings of the data analysis exhibit excellent prediction and estimation capability of the model.

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