scholarly journals Impact of Historical Software Metric Changes in Predicting Future Maintainability Trends in Open-Source Software Development

2020 ◽  
Vol 10 (13) ◽  
pp. 4624
Author(s):  
Mitja Gradišnik ◽  
Tina Beranič ◽  
Sašo Karakatič
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Maintenance ◽  
Software Metrics ◽  
Prediction Models ◽  
Measurement Data ◽  
Software Project ◽  
Software Projects ◽  
Software Metric ◽  
Software Maintainability

Software maintenance is one of the key stages in the software lifecycle and it includes a variety of activities that consume the significant portion of the costs of a software project. Previous research suggest that future software maintainability can be predicted, based on various source code aspects, but most of the research focuses on the prediction based on the present state of the code and ignores its history. While taking the history into account in software maintainability prediction seems intuitive, the research empirically testing this has not been done, and is the main goal of this paper. This paper empirically evaluates the contribution of historical measurements of the Chidamber & Kemerer (C&K) software metrics to software maintainability prediction models. The main contribution of the paper is the building of the prediction models with classification and regression trees and random forest learners in iterations by adding historical measurement data extracted from previous releases gradually. The maintainability prediction models were built based on software metric measurements obtained from real-world open-source software projects. The analysis of the results show that an additional amount of historical metric measurements contributes to the maintainability prediction. Additionally, the study evaluates the contribution of individual C&K software metrics on the performance of maintainability prediction models.

The Impact of Project Initiators on Open Source Software Project Success

2021 ◽  
pp. 221-238
Author(s):  
Ling Wang ◽  
Jinxiao Wang
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Project Success ◽  
Software Project ◽  
Software Projects ◽  
Collective Behaviors ◽  
Collective Wisdom ◽  
Open Source Software Project ◽  
Development Costs ◽  
Collaborative Innovation

This paper focuses on studying the role of open source software project initiator in affecting the OSS project success from the perspective of individual and collective behaviors. The authors collected the data from an emerging OSS hosting platform Gitee in China. This research indicates that the success mode for open source software projects in China relies a lot on the project initiators. Project initiators not only contribute codes to aid the project directly, but also use their social capital to facilitate the project success. But no full play has been given to social network's effect on mass production and collaborative innovation. The authors suggest collaborative innovation which could lead to coherence of global collective wisdom, reduced development costs, and expanded source of innovation should be the further direction for the OSS project in emerging platforms.

scholarly journals What Is Needed for the Sustainable Success of OSS Projects: Efficiency Analysis of Commit Production Process via Git

2018 ◽  
Vol 10 (9) ◽  
pp. 3001
Author(s):  
Jaeyoon Song ◽  
Changhee Kim
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Coordination Mechanism ◽  
Software Project ◽  
Tobit Regression ◽  
Software Projects ◽  
Open Source Software Project ◽  
Regulatory Processes ◽  
Massive Number ◽  
Tobit Regression Analysis

The purpose of this study is to investigate the relative efficiency of open source software projects, and to analyze what is needed for their sustainable success. The success of open source software is known to be attributable to a massive number of contributors engaging in the development process. However, an efficient open source software project is not guaranteed simply by active participation by many; a coordination mechanism is needed to seamlessly manage the multi-party collaboration. On this basis, this study aimed to examine the internal regulatory processes based on Git and GitHub, which serve as such a mechanism, and redefine the efficiency of open source software projects to fully reflect them. For this purpose, a two-stage data envelopment analysis was used to measure the project efficiency reflecting the internal processes. Moreover, this study considered the Kruskal–Wallis test and Tobit regression analysis to examine the effects of the participation by many on an open source software project based on the newly defined efficiency. Results show that a simple increase in contributors can be poisonous in terms of the efficiency of open source software projects.

scholarly journals Identification-Method Research for Open-Source Software Ecosystems

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 182 ◽  
Author(s):  
Zhifang Liao ◽  
Ningwei Wang ◽  
Shengzong Liu ◽  
Yan Zhang ◽  
Hui Liu ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Clustering Algorithm ◽  
Research Area ◽  
Software Project ◽  
Software Projects ◽  
Virtual Collaboration ◽  
Software Ecosystem ◽  
Software Ecosystems ◽  
Core Project

In recent years, open-source software (OSS) development has grown, with many developers around the world working on different OSS projects. A variety of open-source software ecosystems have emerged, for instance, GitHub, StackOverflow, and SourceForge. One of the most typical social-programming and code-hosting sites, GitHub, has amassed numerous open-source-software projects and developers in the same virtual collaboration platform. Since GitHub itself is a large open-source community, it hosts a collection of software projects that are developed together and coevolve. The great challenge here is how to identify the relationship between these projects, i.e., project relevance. Software-ecosystem identification is the basis of other studies in the ecosystem. Therefore, how to extract useful information in GitHub and identify software ecosystems is particularly important, and it is also a research area in symmetry. In this paper, a Topic-based Project Knowledge Metrics Framework (TPKMF) is proposed. By collecting the multisource dataset of an open-source ecosystem, project-relevance analysis of the open-source software is carried out on the basis of software-ecosystem identification. Then, we used our Spectral Clustering algorithm based on Core Project (CP-SC) to identify software-ecosystem projects and further identify software ecosystems. We verified that most software ecosystems usually contain a core software project, and most other projects are associated with it. Furthermore, we analyzed the characteristics of the ecosystem, and we also found that interactive information has greater impact on project relevance. Finally, we summarize the Topic-based Project Knowledge Metrics Framework.

Prediction Models and Techniques for Open Source Software Projects

2014 ◽  
Vol 5 (2) ◽  
pp. 1-39 ◽  
Author(s):  
M.M. Mahbubul Syeed ◽  
Imed Hammouda ◽  
Tarja Systä
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Prediction Models ◽  
Literature Survey ◽  
Future Research ◽  
Software Projects ◽  
Reliable Prediction ◽  
Research Directions ◽  
Fundamental Knowledge ◽  
Future Research Directions

Open Source Software (OSS) is currently a widely adopted approach to developing and distributing software. For effective adoption of OSS, fundamental knowledge of project development is needed. This often calls for reliable prediction models to simulate project evolution and to envision project future. These models provide help in supporting preventive maintenance and building quality software. This paper reports on a systematic literature survey aimed at the identification and structuring of research that offer prediction models and techniques in analyzing OSS projects. In this review, we systematically selected and reviewed 52 peer reviewed articles that were published between January, 2000 and March, 2013. The study outcome provides insight in what constitutes the main contributions of the field, identifies gaps and opportunities, and distills several important future research directions.

The Impact of Project Initiators on Open Source Software Project Success

2020 ◽  
Vol 12 (1) ◽  
pp. 28-43
Author(s):  
Ling Wang ◽  
Jinxiao Wang
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Project Success ◽  
Software Project ◽  
Software Projects ◽  
Collective Behaviors ◽  
Collective Wisdom ◽  
Open Source Software Project ◽  
Collaborative Innovation ◽  
The Impact

This paper focuses on studying the role of open source software project initiator in affecting the OSS project success from the perspective of individual and collective behaviors. The authors collected the data from an emerging OSS hosting platform Gitee in China. This research indicates that the success mode for open source software projects in China relies a lot on the project initiators. Project initiators not only contribute codes to aid the project directly, but also use their social capital to facilitate the project success. But no full play has been given to social network's effect on mass production and collaborative innovation. The authors suggest collaborative innovation which could lead to coherence of global collective wisdom, reduced development costs, and expanded source of innovation should be the further direction for the OSS project in emerging platforms.

scholarly journals A Tool-Based Perspective on Software Code Maintainability Metrics: A Systematic Literature Review

2020 ◽  
Vol 2020 ◽  
pp. 1-26
Author(s):  
Luca Ardito ◽  
Riccardo Coppola ◽  
Luca Barbato ◽  
Diego Verga
Keyword(s):  
Literature Review ◽  
Programming Languages ◽  
Systematic Literature Review ◽  
Digital Libraries ◽  
Software Metrics ◽  
Software Project ◽  
Software Projects ◽  
Software Maintainability ◽  
Software Code ◽  
Selection Of

Software maintainability is a crucial property of software projects. It can be defined as the ease with which a software system or component can be modified to be corrected, improved, or adapted to its environment. The software engineering literature proposes many models and metrics to predict the maintainability of a software project statically. However, there is no common accordance with the most dependable metrics or metric suites to evaluate such nonfunctional property. The goals of the present manuscript are as follows: (i) providing an overview of the most popular maintainability metrics according to the related literature; (ii) finding what tools are available to evaluate software maintainability; and (iii) linking the most popular metrics with the available tools and the most common programming languages. To this end, we performed a systematic literature review, following Kitchenham’s SLR guidelines, on the most relevant scientific digital libraries. The SLR outcome provided us with 174 software metrics, among which we identified a set of 15 most commonly mentioned ones, and 19 metric computation tools available to practitioners. We found optimal sets of at most five tools to cover all the most commonly mentioned metrics. The results also highlight missing tool coverage for some metrics on commonly used programming languages and minimal coverage of metrics for newer or less popular programming languages. We consider these results valuable for researchers and practitioners who want to find the best selection of tools to evaluate the maintainability of their projects or to bridge the discussed coverage gaps for newer programming languages.

scholarly journals Data Mining Techniques for Software Quality Prediction in Open Source Software

2019 ◽  
Vol 214 ◽  
pp. 05007
Author(s):  
Marco Canaparo ◽  
Elisabetta Ronchieri
Keyword(s):  
Data Mining ◽  
Software Engineering ◽  
Open Source ◽  
Software Quality ◽  
Open Source Software ◽  
Software Metrics ◽  
Prediction Models ◽  
Quality Prediction ◽  
Data Mining Techniques ◽  
Software Quality Prediction

Software quality monitoring and analysis are among the most productive topics in software engineering research. Their results may be effectively employed by engineers during software development life cycle. Open source software constitutes a valid test case for the assessment of software characteristics. The data mining approach has been proposed in literature to extract software characteristics from software engineering data. This paper aims at comparing diverse data mining techniques (e.g., derived from machine learning) for developing effective software quality prediction models. To achieve this goal, we tackled various issues, such as the collection of software metrics from open source repositories, the assessment of prediction models to detect software issues and the adoption of statistical methods to evaluate data mining techniques. The results of this study aspire to identify the data mining techniques that perform better amongst all the ones used in this paper for software quality prediction models.

scholarly journals Anatomy of BioJS, an open source community for the life sciences

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Guy Yachdav ◽  
Tatyana Goldberg ◽  
Sebastian Wilzbach ◽  
David Dao ◽  
Iris Shih ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Life Sciences ◽  
Biological Data ◽  
Software Project ◽  
Software Projects ◽  
Open Source Software Project ◽  
Different Types ◽  
Visualization Tools

BioJS is an open source software project that develops visualization tools for different types of biological data. Here we report on the factors that influenced the growth of the BioJS user and developer community, and outline our strategy for building on this growth. The lessons we have learned on BioJS may also be relevant to other open source software projects.

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

scholarly journals Internal Quality Evolution of Open-Source Software Systems

2021 ◽  
Vol 11 (12) ◽  
pp. 5690
Author(s):  
Mamdouh Alenezi
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Evolution ◽  
Quality Metrics ◽  
Internal Quality ◽  
Software Systems ◽  
Software Metric ◽  
Significant Difference ◽  
The Relationship

The evolution of software is necessary for the success of software systems. Studying the evolution of software and understanding it is a vocal topic of study in software engineering. One of the primary concepts of software evolution is that the internal quality of a software system declines when it evolves. In this paper, the method of evolution of the internal quality of object-oriented open-source software systems has been examined by applying a software metric approach. More specifically, we analyze how software systems evolve over versions regarding size and the relationship between size and different internal quality metrics. The results and observations of this research include: (i) there is a significant difference between different systems concerning the LOC variable (ii) there is a significant correlation between all pairwise comparisons of internal quality metrics, and (iii) the effect of complexity and inheritance on the LOC was positive and significant, while the effect of Coupling and Cohesion was not significant.

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