scholarly journals The Economics of Community Open Source Software Projects: An Empirical Analysis of Maintenance Effort

2010 ◽  
Vol 2010 ◽  
pp. 1-17 ◽  
Author(s):  
Eugenio Capra ◽  
Chiara Francalanci ◽  
Francesco Merlo
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Effort Estimation ◽  
Estimation Model ◽  
Software Projects ◽  
Increasing Trend ◽  
Entropy Effect ◽  
Degradation Effect ◽  
Over Time

Previous contributions in the empirical software engineering literature have consistently observed a quality degradation effect of proprietary code as a consequence of maintenance. This degradation effect, referred to as entropy effect, has been recognized to be responsible for significant increases in maintenance effort. In the Open Source context, the quality of code is a fundamental design principle. As a consequence, the maintenance effort of Open Source applications may not show a similar increasing trend over time. The goal of this paper is to empirically verify the entropy effect for a sample of 4,289 community Open Source application versions. Analyses are based on the comparison with an estimate of effort obtained with a traditional effort estimation model. Findings indicate that community Open Source applications show a slower growth of maintenance effort over time, and, therefore, are less subject to the entropy effect.

The Effects of Communication Patterns on the Success of Open Source Software Projects

2016 ◽  
Vol 24 (4) ◽  
pp. 22-44 ◽  
Author(s):  
Jing Wu ◽  
Khim-Yong Goh ◽  
He Li ◽  
Chuan Luo ◽  
Haichao Zheng
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Fixed Effects ◽  
Communication Patterns ◽  
Estimation Model ◽  
Software Projects ◽  
Team Members ◽  
Analysis Methodology ◽  
Model Following ◽  
Time Period

Drawing on the theoretical lens of communication patterns in organizational theory, this research analyzed the longitudinal success of open source software (OSS) projects by employing social network analysis method, based on extensive analyses of empirical data. This study is expected to provide an understanding on how communication patterns established in different roles and different levels. The authors not only measured OSS success from both developers and users' perspectives, but also extended the existing research by including the potential relationships among these success measures in the estimation model. Following the panel data econometric analysis methodology, they evaluated their research hypotheses using the Three-Stage Least Squares model, accounting for both time-period and project fixed effects. The authors' results indicated that according to the objectives of projects, a proper and planned control for the communication among team members is crucial for the success of OSS projects.

scholarly journals MINING EFFORT DATA FROM THE OSS REPOSITORY OF DEVELOPER’S BUG FIX ACTIVITY

2016 ◽  
Vol 3 (1) ◽  
pp. 107-128
Author(s):  
Syed Nadeem Ahsan ◽  
Muhammad Tanvir Afzal ◽  
Safdar Zaman ◽  
Christian Gütel ◽  
Franz Wotawa
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
State Of The Art ◽  
Tracking System ◽  
Previous History ◽  
Effort Estimation ◽  
Estimation Model ◽  
Activity Data ◽  
Actual Time ◽  
History Of

During the evolution of any software, efforts are made to fix bugs or to add new features in software. In software engineering, previous history of effort data is required to build an effort estimation model, which estimates the cost and complexity of any software. Therefore, the role of effort data is indispensable to build state-of-the-art effort estimation models. Most of the Open Source Software does not maintain any effort related information. Consequently there is no state-of-the-art effort estimation model for Open Source Software, whereas most of the existing effort models are for commercial software. In this paper we present an approach to build an effort estimation model for Open Source Software. For this purpose we suggest to mine effort data from the history of the developer’s bug fix activities. Our approach determines the actual time spend to fix a bug, and considers it as an estimated effort. Initially, we use the developer’s bug-fix-activity data to construct the developer’s activity log-book. The log-book is used to store the actual time elapsed to fix a bug. Subsequently, the log-book information is used to mine the bug fix effort data. Furthermore, the developer’s bug fix activity data is used to define three different measures for the developer’s contribution or expertise level. Finally, we used the bug-fix-activity data to visualize the developer’s collaborations and the involved source files. In order to perform an experiment we selected the Mozilla open source project and downloaded 93,607 bug reports from the Mozilla project bug tracking system i.e., Bugzilla. We also downloaded the available CVS-log data from the Mozilla project repository. In this study we reveal that in case of Mozilla only 4.9% developers have been involved in fixing 71.5% of the reported bugs.

scholarly journals Duration Estimation Models for Open Source Software Projects

2021 ◽  
Vol 13 (1) ◽  
pp. 1-17
Author(s):  
Donatien Koulla Moulla ◽  
◽  
Alain Abran ◽  
Kolyang
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Total Duration ◽  
Full Time ◽  
Effort Estimation ◽  
Estimation Model ◽  
Data Repositories ◽  
Independent Variable ◽  
Duration Estimation ◽  
Estimation Models

For software organizations that rely on Open Source Software (OSS) to develop customer solutions and products, it is essential to accurately estimate how long it will take to deliver the expected functionalities. While OSS is supported by government policies around the world, most of the research on software project estimation has focused on conventional projects with commercial licenses. OSS effort estimation is challenging since OSS participants do not record effort data in OSS repositories. However, OSS data repositories contain dates of the participants’ contributions and these can be used for duration estimation. This study analyses historical data on WordPress and Swift projects to estimate OSS project duration using either commits or lines of code (LOC) as the independent variable. This study proposes first an improved classification of contributors based on the number of active days for each contributor in the development period of a release. For the WordPress and Swift OSS projects environments the results indicate that duration estimation models using the number of commits as the independent variable perform better than those using LOC. The estimation model for full-time contributors gives an estimate of the total duration, while the models with part-time and occasional contributors lead to better estimates of projects duration with both for the commits data and the lines of data.

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 The significance of bug report elements

2020 ◽  
Vol 25 (6) ◽  
pp. 5255-5294
Author(s):  
Mozhan Soltani ◽  
Felienne Hermans ◽  
Thomas Bäck
Keyword(s):  
Statistical Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Test Cases ◽  
Software Projects ◽  
Software Debugging ◽  
Bug Reports ◽  
Bug Report

Abstract Open source software projects often use issue repositories, where project contributors submit bug reports. Using these repositories, more bugs in software projects may be identified and fixed. However, the content and therefore quality of bug reports vary. In this study, we aim to understand the significance of different elements in bug reports. We interviewed 35 developers to gain insights into their perceptions on the importance of various contents in bug reports. To assess our findings, we surveyed 305 developers. The results show developers find it highly important that bug reports include crash description, reproducing steps or test cases, and stack traces. Software version, fix suggestions, code snippets, and attached contents have lower importance for software debugging. Furthermore, to evaluate the quality of currently available bug reports, we mined issue repositories of 250 most popular projects on Github. Statistical analysis on the mined issues shows that crash reproducing steps, stack traces, fix suggestions, and user contents, have statistically significant impact on bug resolution times, for ∼70%, ∼76%, ∼55%, and ∼33% of the projects. However, on avarage, over 70% of bug reports lack these elements.

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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