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OSS projects are being developed by globally distributed contributors, who often collaborate through the pull-based model today. While this model lowers the barrier to entry for OSS developers by synthesizing, automating and optimizing the contribution process, coordination among an increasing number of contributors remains as a challenge due to the asynchronous and self-organized nature of distributed development. In particular, duplicate contributions, where multiple different contributors unintentionally submit duplicate pull requests to achieve the same goal, are an elusive problem that may waste effort in automated testing, code review and software maintenance. While the issue of duplicate pull requests has been highlighted, to what extent duplicate pull requests affect the development in OSS communities has not been well investigated. In this paper, we conduct a mixed-approach study to bridge this gap. Based on a comprehensive dataset constructed from 26 popular GitHub projects, we obtain the following findings: (a) Duplicate pull requests result in redundant human and computing resources, exerting a significant impact on the contribution and evaluation process. (b) Contributors' inappropriate working patterns and the drawbacks of their collaborating environment might result in duplicate pull requests. (c) Compared to non-duplicate pull requests, duplicate pull requests have significantly different features, e.g., being submitted by inexperienced contributors, being fixing bugs, touching cold files, and solving tracked issues. (d) Integrators choosing between duplicate pull requests prefer to accept those with early submission time, accurate and high-quality implementation, broad coverage, test code, high maturity, deep discussion, and active response. Finally, actionable suggestions and implications are proposed for OSS practitioners.

Commercial participation continues to grow in open source software (OSS) projects and novel arrangements appear to emerge in company-dominated projects and ecosystems. What is the nature of these novel arrangements? Does volunteers’ participation remain critical for these ecosystems? Despite extensive research on commercial participation in OSS, the exact nature and extent of company contributions to OSS development, and the impact of this engagement may have on the volunteer community have not been clarified. To bridge the gap, we perform an exploratory study of OpenStack: a large OSS ecosystem with intense commercial participation. We quantify companies’ contributions via the developers that they provide and the commits made by those developers. We find that companies made far more contributions than volunteers and the distribution of the contributions made by different companies is also highly unbalanced. We observe eight unique contribution models based on companies’ commercial objectives and characterize each model according to three dimensions: contribution intensity, extent, and focus. Companies providing full cloud solutions tend to make both intensive (more than other companies) and extensive (involving a wider variety of projects) contributions. Usage-oriented companies make extensive but less intense contributions. Companies driven by particular business needs focus their contributions on the specific projects addressing these needs. Minor contributors include community players (e.g., the Linux Foundation) and research groups. A model relating the number of volunteers to the diversity of contribution shows a strong positive association between them.

The design and synthesis of metal–organic framework/graphene oxide (MOF/GO) composites are vital for their applications in energy, adsorption, sensing, supercapacitors, and catalysis. How to achiev...

It is preliminary estimated that the proved geological reserves of original low-oil-saturation reservoirs (OLOSRs) of 54 blocks in China are more than 820 million tons. As same as the high water-cut stage of a reservoir after water flooding, the initial water cut of OLOSRS is higher than 80%. The industrial application of EOR technology could efficiently enhance oil recovery rate from 8.4% to 20.1% of reservoirs after water flooding. The suitable measurements of EOR to the OLOSRs probably have a profound impact on achieving higher oilfield reserves and production. In this paper, the main characteristics and reserves potential of OLOSRs have been studied by investigations and laboratory experiments. The relationship between initial oil saturation and displacement efficiency is also analyzed. The feasibility of EOR technologies for different OLOSRs is discussed. The results show that the OLOSRs could be divided into two categories (primary and secondary) by origin of low oil saturation. The two categories contain abundant reserves in China’s major oilfields, but their occurrence states of crude oil are quite different. If the average oil saturation of the OLOSR is defined at 40%, there are 13.1%–54.2% crude oil that could be recovered by using the existing EOR technology, especially in the primary OLOSRs with low permeability and the secondary OLOSRs with high permeability. Keywords: Low oil saturation, Transition zones, Occurrence state, Oil displacement efficiency, Gas flooding, Surfactant flooding

This study aimed to increase the solubility of glycyrrhetinic acid (GA) in water and enhance its liver-targeting ability using self-assembling nanomicelles (NMs) based on stearic acid-modified fenu...

During software maintenance, developers may need to migrate an already in-use library to another library with similar functionalities. However, it is difficult to make the optimal migration decision with limited information, knowledge, or expertise. In this paper, we present MigrationAdvisor, an evidence-based tool to recommend library migration targets through intelligent analysis upon a large number of GitHub repositories and Java libraries. The migration advisories are provided through a search engine style web service where developers can seek migration suggestions for a specific library. We conduct systematic evaluations on the correctness of results, and evaluate the usefulness of the tool by collecting usage feedback from industry developers. Video: https://youtu.be/4I75W22TqwQ.

As a renewable source, solar energy plays an important role in meeting energy demand for human beings and in relieving global warming. In this paper, the concentrated solar heat is utilized to drive the high-endothermic methane reforming with carbon looping. In so doing, the process increases the utilization of CO2 and reduces the carbon emissions as well as saves the extra fuel consumption for combustion, leading to high efficiency of energy utilization. By optimizing this proposed system, the energy efficiency can reach approximate 67.13% with simultaneously reducing CO2 emissions by 34.98% compared with SMR process. Exergy analysis is used to assess the location of irreversibility within process. The maximal part of exergy destroyer was localized in reformer with a contribution of 68%. In addition, the effects of hourly variation of direct normal irradiation on thermodynamic performance and methane conversion on the four typical days (spring equinox, summer solstice, autumn equinox, winter solstice) were analyzed in this work. The current work might be insightful for solar-hydrogen production field.

The wide adoption of third-party libraries in software projects is beneficial but also risky. An already-adopted third-party library may be abandoned by its maintainers, may have license incompatibilities, or may no longer align with current project requirements. Under such circumstances, developers need to migrate the library to another library with similar functionalities, but the migration decisions are often opinion-based and sub-optimal with limited information at hand. Therefore, several filtering-based approaches have been proposed to mine library migrations from existing software data to leverage "the wisdom of crowd," but they suffer from either low precision or low recall with different thresholds, which limits their usefulness in supporting migration decisions.In this paper, we present a novel approach that utilizes multiple metrics to rank and therefore recommend library migrations. Given a library to migrate, our approach first generates candidate target libraries from a large corpus of software repositories, and then ranks them by combining the following four metrics to capture different dimensions of evidence from development histories: Rule Support, Message Support, Distance Support, and API Support. We evaluate the performance of our approach with 773 migration rules (190 source libraries) that we borrow from previous work and recover from 21,358 Java GitHub projects. The experiments show that our metrics are effective to help identify real migration targets, and our approach significantly outperforms existing works, with MRR of 0.8566, top-1 precision of 0.7947, top-10 NDCG of 0.7702, and top-20 recall of 0.8939. To demonstrate the generality of our approach, we manually verify the recommendation results of 480 popular libraries not included in prior work, and we confirm 661 new migration rules from 231 of the 480 libraries with comparable performance. The source code, data, and supplementary materials are provided at: https://github.com/hehao98/MigrationHelper.

Keeping a good influx of newcomers is critical for open source software projects' survival, while newcomers face many barriers to contributing to a project for the first time. To support newcomers onboarding, GitHub encourages projects to apply labels such as good first issue (GFI) to tag issues suitable for newcomers. However, many newcomers still fail to contribute even after many attempts, which not only reduces the enthusiasm of newcomers to contribute but makes the efforts of project members in vain. To better support the onboarding of newcomers, this paper reports a preliminary study on this mechanism from its application status, effect, problems, and best practices. By analyzing 9,368 GFIs from 816 popular GitHub projects and conducting email surveys with newcomers and project members, we obtain the following results. We find that more and more projects are applying this mechanism in the past decade, especially the popular projects. Compared to common issues, GFIs usually need more days to be solved. While some newcomers really join the projects through GFIs, almost half of GFIs are not solved by newcomers. We also discover a series of problems covering mechanism (e.g., inappropriate GFIs), project (e.g., insufficient GFIs) and newcomer (e.g., uneven skills) that makes this mechanism ineffective. We discover the practices that may address the problems, including identifying GFIs that have informative description and available support, and require limited scope and skill, etc. Newcomer onboarding is an important but challenging question in open source projects and our work enables a better understanding of GFI mechanism and its problems, as well as highlights ways in improving them.

Context. As a novel coronavirus swept the world in early 2020, thousands of software developers began working from home. Many did so on short notice, under difficult and stressful conditions. Objective. This study investigates the effects of the pandemic on developers' wellbeing and productivity. Method. A questionnaire survey was created mainly from existing, validated scales and translated into 12 languages. The data was analyzed using non-parametric inferential statistics and structural equation modeling. Results. The questionnaire received 2225 usable responses from 53 countries. Factor analysis supported the validity of the scales and the structural model achieved a good fit (CFI = 0.961, RMSEA = 0.051, SRMR = 0.067). Confirmatory results include: (1) the pandemic has had a negative effect on developers' wellbeing and productivity; (2) productivity and wellbeing are closely related; (3) disaster preparedness, fear related to the pandemic and home office ergonomics all affect wellbeing or productivity. Exploratory analysis suggests that: (1) women, parents and people with disabilities may be disproportionately affected; (2) different people need different kinds of support. Conclusions. To improve employee productivity, software companies should focus on maximizing employee wellbeing and improving the ergonomics of employees' home offices. Women, parents and disabled persons may require extra support., Comment: 34 pages, 7 tables, 5 figures, to appear in Empirical Software Engineering

Modern programming question & answer (QA however, this behavior may affect the contribution quality as well. In this paper, we report on a large-scale, mixed-methods empirical study of the gamification-influenced FA phenomenon in SO. We first quantitatively investigate the popularity of the phenomenon and user behaviors regarding FAs. Then, we study the quality of FAs by using regression modeling and qualitatively analyzing 300 instances of FAs. Our main findings reveal that more than 70% and 90% of FAs are not edited by the answerers and other users, respectively, and that later incoming answers have lower chances of being voted on and accepted. Notably, we find that the answer length, code snippets length, and readability of FAs are significantly lower than those of non-fast answers. Although FAs have higher crowd assessment scores, they have no relationship with acceptance from the perspective of asker assessment, and a considerable portion of FAs solve the problem by interacting with the asker in the comments. These results help us better understand the effects of reward-based gamification on crowdsourced software engineering communitites and provide implications for designers of gamified systems.

Large-scale open source communities, such as the Linux kernel, have gone through decades of development, substantially growing in scale and complexity. In the traditional workflow, maintainers serve as "gatekeepers" for the subsystems that they maintain. As the number of patches and authors significantly increases, maintainers come under considerable pressure, which may hinder the operation and even the sustainability of the community. A few subsystems have begun to use new workflows to address these issues. However, it is unclear to what extent these new workflows are successful, or how to apply them. Therefore, we conduct an empirical study on the multiple-committer model (MCM) that has provoked extensive discussion in the Linux kernel community. We explore the effect of the model on the i915 subsystem with respect to four dimensions: pressure, latency, complexity, and quality assurance. We find that after this model was adopted, the burden of the i915 maintainers was significantly reduced. Also, the model scales well to allow more committers. After analyzing the online documents and interviewing the maintainers of i915, we propose that overloaded subsystems which have trustworthy candidate committers are suitable for adopting the model. We further suggest that the success of the model is closely related to a series of measures for risk mitigation---sufficient precommit testing, strict review process, and the use of tools to simplify work and reduce errors. We employ a network analysis approach to locate candidate committers for the target subsystems and validate this approach and contextual success factors through email interviews with their maintainers. To the best of our knowledge, this is the first study focusing on how to scale open source communities. We expect that our study will help the rapidly growing Linux kernel and other similar communities to adapt to changes and remain sustainable.

Gas-to-liquid fuels technology has been deemed as an important implement to address the domestic oil shortage with the conversion of syngas into liquid fuel through Fischer-Tropsch synthesis. In this study, we propose a more effective approach (a novel carbon looping cyclic reforming) to aid such necessity based on quantifying the benefits of such process by comparison with conventional steam methane reforming. Our approach is underpinned by using a reformer with a water-gas shift reactor that enables the acquisition of an optimal H2/CO ratio. As such, adjustment of H2/CO ratio in syngas can be easily attained, beneficial to downstream Fischer-Tropsch synthesis, ultimately minimizing exergy destruction. We find that, with conventional steam methane reforming as reference, the exergy efficiency of this novel process could be increased by 4.34 percentage points, with simultaneously reducing exergy destruction in syngas production unit by 5.17 kJ/mol CH4. Further, the exergy destruction in Fischer-Tropsch synthesis is cut down by 13.98 kJ/mol by adoption of one-through pass of unreacted gas.

Issue tracking data have been used extensively to aid in predicting or recommending software development practices. Issue attributes typically change over time, but users may use data from a separate time of data collection rather than the time of their application scenarios. We, therefore, investigate data leakage, which results from ignoring the chronological order in which the data were produced. Information leaked from the "future" makes prediction models misleadingly optimistic. We examine existing literature to confirm the existence of data leakage and reproduce three typical studies (detecting duplicate issues, localizing issues, and predicting issue-fix time) adjusted for appropriate data to quantify the impact of the data leakage. We confirm that 11 out of 58 studies have leakage problem, while 44 are suspected. We observe biased results caused by data leakage while the extent is not striking. Attributes of summary, component, and assignee have the largest impact on the results. Our findings suggest that data users are often unaware of the context of the data being produced. We recommend researchers and practitioners who attempt to utilize issue tracking data to address software development problems to have a full understanding of their application scenarios, the origin and change of the data, and the influential issue attributes to manage data leakage risks.

Code retrieval and summarization are two tasks often employed by software developers to reuse code that spreads over online repositories. In this paper, we present a neural framework that allows bidirectional mapping between source code and natural language to improve these two tasks. Our framework, BVAE, is designed to have two Variational AutoEncoders (VAEs) to model bimodal data: C-VAE for source code and L-VAE for natural language. Both VAEs are trained jointly to reconstruct their input as much as possible with regularization that captures the closeness between the latent variables of code and description. BVAE could learn semantic vector representations for both code and description and generate completely new descriptions for arbitrary code snippets. We design two instance models of BVAE for retrieval and summarization tasks respectively and evaluate their performance on a benchmark which involves two programming languages: C# and SQL. Experiments demonstrate BVAE’s potential on the two tasks.

In many open source software projects (e.g., the Linux kernel), people contribute by sending code patches to the community. The community evaluates these contributions and decides whether to integrate the changes. To improve the efficiency of code contributions, substantial effort has been devoted to analyzing how patches are submitted and processed. Patch data are critical for this type of analysis, while retrieving and cleaning the data is a non-trivial job. To facilitate these studies, we share a multi-level dataset of a Linux kernel patchwork covering a nine-year history of patches and related discussion recorded by the Linux kernel mailing list (LKML). The data and scripts are provided at: https://zenodo.org/record/1165576

Because of the increasing acceptance and possibly expanding market of free/libre open source software (FLOSS), the spectrum and scale of companies that participate in FLOSS development have substantially expanded in recent years. Companies get involved in FLOSS projects to acquire user innovations [3, 12], to reduce costs [8, 11], to make money on complementary services [13], etc. Such intense involvement may change the nature of FLOSS development and pose critical challenges for the sustainability of the projects. For example, it has been found that a company's full control and intense involvement is associated with a decrease of volunteer inflow [13]. Sometimes a project may fail after one company pulls resources from the project [13]. This raises concerns about the domination of one company in a project. In large projects like OpenStack, there are often hundreds of companies involved in contributing code. Despite substantial researches on commercial participation, whether or not one company dominates a project and the impact of such domination has never been explicitly explored. We investigate four main projects of OpenStack, a large ecosystem that has had a tremendous impact on computing and society, to answer the following research questions: Does one company dominate the project's development (RQ1)? If the answer to RQ1 is yes, does the domination affect the community (RQ2)?

In order to reduce cost, get to market faster and utilize global talents, large companies often organize their software development globally (distributed over Internet), a paradigm advocated by Internetware. Considering the complexity of distributed software development, it is important to understand the variation of various tasks, so the projects could work more efficiently on plan formulation, personnel organization, or task allocation. The main goal of this paper is to understand the variations of software development tasks. We conduct an interview with 47 interviewees and a survey with 148 people from 15 projects with different size and domain.Through the analysis of interviews and surveys, we find that a software task could be characterized through three aspects: value, difficulty and centrality. Among them, task value is influenced by the role of stakeholders and project context, and is related to the task difficulty and task centrality. Task difficulty is reflected by technology, domain difference, working relationships, customer related issues, and it is also related to the developers' personalities. Task centrality can be described by customer impact, system-wide impact, team impact and future impact. We believe our results can help project managers to optimize task allocation, or adjust project plan, and thus achieve efficient development.

Open source software ecosystems evolve ways to balance the workload among groups of participants ranging from core groups to peripheral groups. As ecosystems grow, it is not clear whether the mechanisms that previously made them work will continue to be relevant or whether new mechanisms will need to evolve. The impact of failure for critical ecosystems such as Linux is enormous, yet the understanding of why they function and are effective is limited. We, therefore, aim to understand how the Linux kernel sustains its growth, how to characterize the workload of maintainers, and whether or not the existing mechanisms are scalable. We quantify maintainers' work through the files that are maintained, and the change activity and the numbers of contributors in those files. We find systematic differences among modules; these differences are stable over time, which suggests that certain architectural features, commercial interests, or module-specific practices lead to distinct sustainable equilibria. We find that most of the modules have not grown appreciably over the last decade; most growth has been absorbed by a few modules. We also find that the effort per maintainer does not increase, even though the community has hypothesized that required effort might increase. However, the distribution of work among maintainers is highly unbalanced, suggesting that a few maintainers may experience increasing workload. We find that the practice of assigning multiple maintainers to a file yields only a power of 1/2 increase in productivity. We expect that our proposed framework to quantify maintainer practices will help clarify the factors that allow rapidly growing ecosystems to be sustainable.

Code contributions in Free/Libre and Open Source Software projects are controlled to maintain high-quality of software. Alternatives to patch-based code contribution tools such as mailing lists and issue trackers have been developed with the pull request systems being the most visible and widely available on GitHub. Is the code contribution process more effective with pull request systems? To answer that, we quantify the effectiveness via the rates contributions are accepted and ignored, via the time until the first response and final resolution and via the numbers of contributions. To control for the latent variables, our study includes a project that migrated from an issue tracker to the GitHub pull request system and a comparison between projects using mailing lists and pull request systems. Our results show pull request systems to be associated with reduced review times and larger numbers of contributions. However, not all the comparisons indicate substantially better accept or ignore rates in pull request systems. These variations may be most simply explained by the differences in contribution practices the projects employ and may be less affected by the type of tool. Our results clarify the importance of understanding the role of tools in effective management of the broad network of potential contributors and may lead to strategies and practices making the code contribution more satisfying and efficient from both contributors' and maintainers' perspectives.

Many studies analyze issue tracking repositories to understand and support software development. To facilitate the analyses, we share a Mozilla issue tracking dataset covering a 15-year history. The dataset includes three extracts and multiple levels for each extract. The three extracts were retrieved through two channels, a front-end (web user interface (UI)), and a back-end (official database dump) of Mozilla Bugzilla at three different times. The variations (dynamics) among extracts provide space for researchers to reproduce and validate their studies, while revealing potential opportunities for studies that otherwise could not be conducted. We provide different data levels for each extract ranging from raw data to standardized data as well as to the calculated data level for targeting specific research questions. Data retrieving and processing scripts related to each data level are offered too. By employing the multi-level structure, analysts can more efficiently start an inquiry from the standardized level and easily trace the data chain when necessary (e.g., to verify if a phenomenon reflected by the data is an actual event). We applied this dataset to several published studies and intend to expand the multi-level and multi-extract feature to other software engineering datasets.