Collaborative Software Development Platforms Research Articles

MetPy: A Meteorological Python Library for Data Analysis and Visualization

Abstract MetPy is an open-source, Python-based package for meteorology, providing domain-specific functionality built extensively on top of the robust scientific Python software stack, which includes libraries like NumPy, SciPy, Matplotlib, and xarray. The goal of the project is to bring the weather analysis capabilities of GEMPAK (and similar software tools) into a modern computing paradigm. MetPy strives to employ best practices in its development, including software tests, continuous integration, and automated publishing of web-based documentation. As such, MetPy represents a sustainable, long-term project that fills a need for the meteorological community. MetPy’s development is substantially driven by its user community, both through feedback on a variety of open, public forums like Stack Overflow, and through code contributions facilitated by the GitHub collaborative software development platform. MetPy has recently seen the release of version 1.0, with robust functionality for analyzing and visualizing meteorological datasets. While previous versions of MetPy have already seen extensive use, the 1.0 release represents a significant milestone in terms of completeness and a commitment to long-term support for the programming interfaces. This article provides an overview of MetPy’s suite of capabilities, including its use of labeled arrays and physical unit information as its core data model, unit-aware calculations, cross sections, skew T and GEMPAK-like plotting, station model plots, and support for parsing a variety of meteorological data formats. The general road map for future planned development for MetPy is also discussed.

Improving the pull requests review process using learning-to-rank algorithms

Collaborative software development platforms (such as GitHub and GitLab) have become increasingly popular as they have attracted thousands of external contributors to contribute to open source projects. The external contributors may submit their contributions via pull requests, which must be reviewed before being integrated into the central repository. During the review process, reviewers provide feedback to contributors, conduct tests and request further modifications before finally accepting or rejecting the contributions. The role of reviewers is key to maintain the effective review process of the project. However, the number of decisions that reviewers can make is far superseded by the increasing number of pull requests submissions. To help reviewers to perform more decisions on pull requests within their limited working time, we propose a learning-to-rank (LtR) approach to recommend pull requests that can be quickly reviewed by reviewers. Different from a binary model for predicting the decisions of pull requests, our ranking approach complements the existing list of pull requests based on their likelihood of being quickly merged or rejected. We use 18 metrics to build LtR models and we use six different LtR algorithms, such as ListNet, RankNet, MART and random forest. We conduct empirical studies on 74 Java projects to compare the performances of the six LtR algorithms. We compare the best performing algorithm against two baselines obtained from previous research regarding pull requests prioritization: the first-in-and-first-out (FIFO) baseline and the small-size-first baseline. We then conduct a survey with GitHub reviewers to understand the perception of code reviewers regarding the usefulness of our approach. We observe that: (1) The random forest LtR algorithm outperforms other five well adapted LtR algorithms to rank quickly merged pull requests. (2) The random forest LtR algorithm performs better than both the FIFO and the small-size-first baselines, which means our LtR approach can help reviewers make more decisions and improve their productivity. (3) The contributor’s social connections and contributor’s experience are the most influential metrics to rank pull requests that can be quickly merged. (4) The GitHub reviewers that participated in our survey acknowledge that our approach complements existing prioritization baselines to help them to prioritize and to review more pull requests.

Collaborative and teamwork software development in an undergraduate software engineering course

Two key elements of modern software development are collaboration and teamwork. Current methodologies (e.g., agile) and platforms are based on these key elements. This paper describes our experience in stimulating collaboration and teamwork activities of students in the context of a software engineering course at the third year of an undergraduate program in computer science at the University of Milano-Bicocca in Italy. The students were asked to develop a software project in teams of 3 to 5 students for the final exam of the course. The students used GitHub as a collaborative software development platform. In addition, they analyzed the quality of the developed software through SonarQube. The students were also asked to perform project management tasks (e.g., the Gantt) using the Microsoft Project tool. At the end of the course, we gathered the student feedback through a questionnaire on their collaboration and teamwork experience (through GitHub and Microsoft Project tools) and on the use of a software analysis assessment tool, i.e., SonarQube. From their feedback, the students were enthusiastic about working in teams for their project development and about learning how to use tools which are exploited not only in the academic world but also in industry.

Collaborative Software Development Platforms for Crowdsourcing

Outsourcing to the crowd, or crowdsourcing, has launched extremely successful businesses, such as Linux. But platforms for efficient collaboration and crowdsourcing support are still emerging. This article provides an overview of current technologies for crowdsourcing.