Topic-based integrator matching for pull request

Pull Request (PR) is the main method for code contributions from the external contributors in GitHub. PR review is an essential part of open source software developments to maintain the quality of software. Matching a new PR for an appropriate integrator will make the PR reviewing more effective. However, PR and integrator matching are now organized manually in GitHub. To make this process more efficient, we propose a Topic-based Integrator Matching Algorithm (TIMA) to predict highly relevant collaborators(the core developers) as the integrator to incoming PRs . TIMA takes full advantage of the textual semantics of PRs. To define the relationships between topics and collaborators, TIMA builds a relation matrix about topic and collaborators. According to the relevance between topics and collaborators, TIMA matches the suitable collaborators as the PR integrator

Action-based recommendation in pull-request development

Pull requests (PRs) selection is a challenging task faced by integrators in pull-based development (PbD), with hundreds of PRs submitted on a daily basis to large open-source projects. Managing these PRs manually consumes integrators' time and resources and may lead to delays in the acceptance, response, or rejection of PRs that can propose bug fixes or feature enhancements. On the one hand, well-known platforms for performing PbD, like GitHub, do not provide built-in recommendation mechanisms for facilitating the management of PRs. On the other hand, prior research on PRs recommendation has focused on the likelihood of either a PR being accepted or receive a response by the integrator. In this paper, we consider both those likelihoods, this to help integrators in the PRs selection process by suggesting to them the appropriate actions to undertake on each specific PR. To this aim, we propose an approach, called CARTESIAN (aCceptance And Response classificaTion-based requESt IdentificAtioN) modeling the PRs recommendation according to PR actions. In particular, CARTESIAN is able to recommend three types of PR actions: accept, respond, and reject. We evaluated CARTESIAN on the PRs of 19 popular GitHub projects. The results of our study demonstrate that our approach can identify PR actions with an average precision and recall of about 86%. Moreover, our findings also highlight that CARTESIAN outperforms the results of two baseline approaches in the task of PRs selection

Study of variational symplectic algorithms for the numerical integration of guiding center equations of motion

Questa tesi presenta una discussione dei più moderni algoritmi simplettici variazionali per l'integrazione delle equazioni del moto del centro di guida in particelle cariche in campi magnetici arbitrari statici, utili nello studio di plasmi debolmente collisionali. Differenti varianti degli algoritmi sono presentate, insieme a studi numerici ed analitici che ne evidenziano la stabilità numerica, o la relativa mancanza di essa

The Review - Winter 1997/1998

A Standout OT Representative - 1 PRO/CON on Managed Care and Physical Therapy - 5 How to: Managing Dementia at Home - 6 Biotechnology Opens With New Degree - 7 Taking it to the Streets: CHP Community Service - 10 Alumni Events - 12 Alumni Notes - 14 Donor List - 1

Modeling User-Affected Software Properties for Open Source Software Supply Chains

Background: Open Source Software development community relies heavily on users of the software and contributors outside of the core developers to produce top-quality software and provide long-term support. However, the relationship between a software and its contributors in terms of exactly how they are related through dependencies and how the users of a software affect many of its properties are not very well understood. Aim: My research covers a number of aspects related to answering the overarching question of modeling the software properties affected by users and the supply chain structure of software ecosystems, viz. 1) Understanding how software usage affect its perceived quality; 2) Estimating the effects of indirect usage (e.g. dependent packages) on software popularity; 3) Investigating the patch submission and issue creation patterns of external contributors; 4) Examining how the patch acceptance probability is related to the contributors\u27 characteristics. 5) A related topic, the identification of bots that commit code, aimed at improving the accuracy of these and other similar studies was also investigated. Methodology: Most of the Research Questions are addressed by studying the NPM ecosystem, with data from various sources like the World of Code, GHTorrent, and the GiHub API. Different supervised and unsupervised machine learning models, including Regression, Random Forest, Bayesian Networks, and clustering, were used to answer appropriate questions. Results: 1) Software usage affects its perceived quality even after accounting for code complexity measures. 2) The number of dependents and dependencies of a software were observed to be able to predict the change in its popularity with good accuracy. 3) Users interact (contribute issues or patches) primarily with their direct dependencies, and rarely with transitive dependencies. 4) A user\u27s earlier interaction with the repository to which they are contributing a patch, and their familiarity with related topics were important predictors impacting the chance of a pull request getting accepted. 5) Developed BIMAN, a systematic methodology for identifying bots. Conclusion: Different aspects of how users and their characteristics affect different software properties were analyzed, which should lead to a better understanding of the complex interaction between software developers and users/ contributors

Digital cockpits and decision support systems : design of technics and tools to extract and process data from heterogeneous databases

Tableau d'honneur de la Faculté des études supérieures et postdoctorales, 2006-200

Decision Support System for Pull Requests Review Using Path-based Network Portrait Divergence and Visualization

Title from PDF of title page, viewed September 8, 2022Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (pages 74-79)Thesis (M.S.)--Department of Computer Science Electrical Engineering. University of Missouri--Kansas City, 2022Pull requests are widely used in open-source and industrial environments to contribute and assess contributions. Unlike the typical code review process, pull requests provide a more lightweight approach for committing, reviewing, and managing code changes. Pull request code reviews also serve multiple objectives, including detecting problems in code, giving a venue to discuss code contributions, and supporting the easy integration of external contributions by project maintainers. The code changes and tests are written for a specific work item are contained in a pull request. Previous studies have reported that pull request review is crucial for software development and that reviewers do not spend more time on test files than on code files. At the same time, code reviewers are concerned that the tests that accompany the code modifications are adequate and cover all possible paths. The purpose of this research is to determine whether the test changes that go along with the code changes match the structural changes made in the Pull Request. The structural changes are determined using recent network comparison breakthroughs in prior work with GraphEvo. We also determine whether or not the visual representation and software metrics can support the software review process. We conducted a case study of 14 Java open-source projects, analyzing thousands of lines of code quality issues in 627 pull requests. We calculated the class level metrics, including network portrait divergence for each Pull request with and without change. In addition, for each pull request, we counted the number of existing test cases that failed due to the modification. Furthermore, correlations were investigated between class-level metrics, including network portrait divergence and tests that failed in pull requests.Introduction -- Related work -- Methodology -- Results and evaluations -- Conclusio

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

Interpersonal Conflicts During Code Review

Code review consists of manual inspection, discussion, and judgment of source code by developers other than the code's author. Due to discussions around competing ideas and group decision-making processes, interpersonal conflicts during code reviews are expected. This study systematically investigates how developers perceive code review conflicts and addresses interpersonal conflicts during code reviews as a theoretical construct. Through the thematic analysis of interviews conducted with 22 developers, we confirm that conflicts during code reviews are commonplace, anticipated and seen as normal by developers. Even though conflicts do happen and carry a negative impact for the review, conflicts-if resolved constructively-can also create value and bring improvement. Moreover, the analysis provided insights on how strongly conflicts during code review and its context (i.e., code, developer, team, organization) are intertwined. Finally, there are aspects specific to code review conflicts that call for the research and application of customized conflict resolution and management techniques, some of which are discussed in this paper. Data and material: https://doi.org/10.5281/zenodo.584879