scholarly journals On Predicting Rediscoveries of Software Defects

2021 ◽  
Author(s):  
Mefta Sadat
Keyword(s):  
Customer Satisfaction ◽  
Open Source ◽  
Predictive Model ◽  
Software Quality ◽  
Open Source Software ◽  
Large Scale ◽  
Software Defects ◽  
Enterprise Software ◽  
Software Projects ◽  
Multiple Clients

The same defect may be rediscovered by multiple clients, causing unplanned outages and leading to reduced customer satisfaction. One solution is forcing clients to install a fix for every defect. However, this approach is economically infeasible, because it requires extra resources and increases downtime. Moreover, it may lead to regression of functionality, as new fixes may break the existing functionality. Our goal is to find a way to proactively predict defects that a client may rediscover in the future. We build a predictive model by leveraging recommender algorithms. We evaluate our approach with extracted rediscovery data from four groups of large-scale open source software projects (namely, Eclipse, Gentoo, KDE, and Libre) and one enterprise software. The datasets contain information about ⇡ 1.33 million unique defect reports over a period of 18 years (1999-2017). Our proposed approach may help in understanding the defect rediscovery phenomenon, leading to improvement of software quality and customer satisfaction.

scholarly journals On Predicting Rediscoveries of Software Defects

2021 ◽  
Author(s):  
Mefta Sadat
Keyword(s):  
Customer Satisfaction ◽  
Open Source ◽  
Predictive Model ◽  
Software Quality ◽  
Open Source Software ◽  
Large Scale ◽  
Software Defects ◽  
Enterprise Software ◽  
Software Projects ◽  
Multiple Clients

The same defect may be rediscovered by multiple clients, causing unplanned outages and leading to reduced customer satisfaction. One solution is forcing clients to install a fix for every defect. However, this approach is economically infeasible, because it requires extra resources and increases downtime. Moreover, it may lead to regression of functionality, as new fixes may break the existing functionality. Our goal is to find a way to proactively predict defects that a client may rediscover in the future. We build a predictive model by leveraging recommender algorithms. We evaluate our approach with extracted rediscovery data from four groups of large-scale open source software projects (namely, Eclipse, Gentoo, KDE, and Libre) and one enterprise software. The datasets contain information about ⇡ 1.33 million unique defect reports over a period of 18 years (1999-2017). Our proposed approach may help in understanding the defect rediscovery phenomenon, leading to improvement of software quality and customer satisfaction.

scholarly journals A large-scale empirical exploration on refactoring activities in open source software projects

2019 ◽  
Vol 180 ◽  
pp. 1-15 ◽  
Author(s):  
Carmine Vassallo ◽  
Giovanni Grano ◽  
Fabio Palomba ◽  
Harald C. Gall ◽  
Alberto Bacchelli
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Software Projects

A Study of Measurement for Development Efficiency in Large Scale Open Source Software Projects

2018 ◽  
Vol 138 (8) ◽  
pp. 1011-1019 ◽  
Author(s):  
Ayako Masuda ◽  
Chikako Morimoto ◽  
Tohru Matsuodani ◽  
Kazuhiko Tsuda
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Software Projects

scholarly journals A study of quality prediction for large-scale open source software projects

2021 ◽  
Vol 10 (1) ◽  
pp. 34
Author(s):  
Shinji Akatsu ◽  
Ayako Masuda ◽  
Tsuyoshi Shida ◽  
Kazuhiko Tsuda
Keyword(s):  
Decision Making ◽  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Quality Analysis ◽  
Quality Prediction ◽  
Software Projects ◽  
Resolution Rate ◽  
The Status

Open source software (OSS) has seen remarkable progress in recent years. Moreover, OSS usage in corporate information systems has been increasing steadily; consequently, the overall impact of OSS on the society is increasing as well. While product quality of enterprise software is assured by the provider, the deliverables of an OSS are developed by the OSS developer community; therefore, their quality is not guaranteed. Thus, the objective of this study is to build an artificial-intelligence-based quality prediction model that corporate businesses could use for decision-making to determine whether a desired OSS should be adopted. We define the quality of an OSS as “the resolution rate of issues processed by OSS developers as well as the promptness and continuity of doing so.” We selected 44 large-scale OSS projects from GitHub for our quality analysis. First, we investigated the monthly changes in the status of issue creation and resolution for each project. It was found that there are three different patterns in the increase of issue creation, and three patterns in the relationship between the increase in issue creation and that of resolution. It was confirmed that there are multiple cases of each pattern that affect the final resolution rate. Next, we investigated the correlation between the final resolution rate and that for a relevant number of months after issue creation. We deduced that the correlation coefficient even between the resolution rate in the first month and the final rate exceeded 0.5. Based on these analysis results, we conclude that the issue resolution rate in the first month once an issue is created is applicable as knowledge for knowledge-based AI systems that can be used to assist in decision-making regarding OSS adoption in business projects.

scholarly journals PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Mohammadreza Yaghoobi ◽  
Krzysztof S. Stopka ◽  
Aaditya Lakshmanan ◽  
Veera Sundararaghavan ◽  
John E. Allison ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Metals And Alloys ◽  
Analysis Tool ◽  
Computational Framework ◽  
Crystal Plasticity Finite Element ◽  
Polycrystalline Metals ◽  
Simulation Based ◽  
Open Source Framework

AbstractThe PRISMS-Fatigue open-source framework for simulation-based analysis of microstructural influences on fatigue resistance for polycrystalline metals and alloys is presented here. The framework uses the crystal plasticity finite element method as its microstructure analysis tool and provides a highly efficient, scalable, flexible, and easy-to-use ICME community platform. The PRISMS-Fatigue framework is linked to different open-source software to instantiate microstructures, compute the material response, and assess fatigue indicator parameters. The performance of PRISMS-Fatigue is benchmarked against a similar framework implemented using ABAQUS. Results indicate that the multilevel parallelism scheme of PRISMS-Fatigue is more efficient and scalable than ABAQUS for large-scale fatigue simulations. The performance and flexibility of this framework is demonstrated with various examples that assess the driving force for fatigue crack formation of microstructures with different crystallographic textures, grain morphologies, and grain numbers, and under different multiaxial strain states, strain magnitudes, and boundary conditions.

What Makes Open Source Software Projects Impactful: A Data-Driven Approach

2020 ◽  
Author(s):  
Huaiwei Yang ◽  
Shuang Liu ◽  
Lin Gui ◽  
Yongxin Zhao ◽  
Jun Sun ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Data Driven ◽  
Software Projects ◽  
Data Driven Approach
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