اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدIDEAL: An Open-Source Identifier Name Appraisal Tool
69
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Developers must comprehend the code they will maintain, meaning that the code must be legible and reasonably self-descriptive. Unfortunately, there is still a lack of research and tooling that supports developers in understanding their naming practices; whether the names they choose make sense, whether they are consistent, and whether they convey the information required of them. In this paper, we present IDEAL, a tool that will provide feedback to developers about their identifier naming practices. Among its planned features, it will support linguistic anti-pattern detection, which is what will be discussed in this paper. IDEAL is designed to, and will, be extended to cover further anti-patterns, naming structures, and practices in the near future. IDEAL is open-source and publicly available, with a demo video available at: https://youtu.be/fVoOYGe50zg
قيم البحث
اقرأ أيضاً
Open source development, to a great extent, is a type of social movement in which shared ideologies play critical roles. For participants of open source development, ideology determines how they make sense of things, shapes their thoughts, actions, a
nd interactions, enables rich social dynamics in their projects and communities, and hereby realizes profound impacts at both individual and organizational levels. While software engineering researchers have been increasingly recognizing ideologys importance in open source development, the notion of ideology has shown significant ambiguity and vagueness, and resulted in theoretical and empirical confusion. In this article, we first examine the historical development of ideologys conceptualization, and its theories in multiple disciplines. Then, we review the extant software engineering literature related to ideology. We further argue the imperatives of developing an empirical theory of ideology in open source development, and propose a research agenda for developing such a theory. How such a theory could be applied is also discussed.
Quantum computing (QC) is an emerging computing paradigm with potential to revolutionize the field of computing. QC is a field that is quickly developing globally and has high barriers of entry. In this paper we explore both successful contributors t
o the field as well as wider QC community with the goal of understanding the backgrounds and training that helped them succeed. We gather data on 148 contributors to open-source quantum computing projects hosted on GitHub and survey 46 members of QC community. Our findings show that QC practitioners and enthusiasts have diverse backgrounds, with most of them having a PhD and trained in physics or computer science. We observe a lack of educational resources on quantum computing. Our goal for these findings is to start a conversation about how best to prepare the next generation of QC researchers and practitioners.
Research institutions are bound to contribute to greenhouse gas emission (GHG) reduction efforts for several reasons. First, part of the scientific communitys research deals with climate change issues. Second, scientists contribute to students educat
ion: they must be consistent and role models. Third the literature on the carbon footprint of researchers points to the high level of some individual footprints. In a quest for consistency and role models, scientists, teams of scientists or universities have started to quantify their carbon footprints and debate on reduction options. Indeed, measuring the carbon footprint of research activities requires tools designed to tackle its specific features. In this paper, we present an open-source web application, GES 1point5, developed by an interdisciplinary team of scientists from several research labs in France. GES 1point5 is specifically designed to estimate the carbon footprint of research activities in France. It operates at the scale of research labs, i.e. laboratoires, which are the social structures around which research is organized in France and the smallest decision making entities in the French research system. The application allows French research labs to compute their own carbon footprint along a standardized, open protocol. The data collected in a rapidly growing network of labs will be used as part of the Labos 1point5 project to estimate Frances research carbon footprint. At the time of submitting this manuscript, 89 research labs had engaged with GES 1point5 to estimate their greenhouse gas emissions. We expect that an international adoption of GES 1point5 (adapted to fit domestic specifics) could contribute to establishing a global understanding of the drivers of the research carbon footprint worldwide and the levers to decrease it.
Image based biomarker discovery typically requires an accurate segmentation of histologic structures (e.g., cell nuclei, tubules, epithelial regions) in digital pathology Whole Slide Images (WSI). Unfortunately, annotating each structure of interest
is laborious and often intractable even in moderately sized cohorts. Here, we present an open-source tool, Quick Annotator (QA), designed to improve annotation efficiency of histologic structures by orders of magnitude. While the user annotates regions of interest (ROI) via an intuitive web interface, a deep learning (DL) model is concurrently optimized using these annotations and applied to the ROI. The user iteratively reviews DL results to either (a) accept accurately annotated regions, or (b) correct erroneously segmented structures to improve subsequent model suggestions, before transitioning to other ROIs. We demonstrate the effectiveness of QA over comparable manual efforts via three use cases. These include annotating (a) 337,386 nuclei in 5 pancreatic WSIs, (b) 5,692 tubules in 10 colorectal WSIs, and (c) 14,187 regions of epithelium in 10 breast WSIs. Efficiency gains in terms of annotations per second of 102x, 9x, and 39x were respectively witnessed while retaining f-scores >.95, suggesting QA may be a valuable tool for efficiently fully annotating WSIs employed in downstream biomarker studies.
In this work we present NJOY+NCrystal, a tool to generate thermal neutron scattering libraries with support for coherent and incoherent elastic components for crystalline solid materials. This tool, which is a customized version of NJOY, was created
by modifying the nuclear data processing program NJOY to call the thermal scattering software library NCrystal, and includes a proposed change in the ENDF-6 format to store both the coherent and incoherent elastic components. Necessary changes to enable this format in NJOY, as well as to sample it in the OpenMC Monte Carlo code, are detailed here. Examples of materials that are coherent-dominant, incoherent-dominant, and mixed elastic scatterers are presented, as well as the creation of novel libraries for MgH$_2$ and MgD$_2$, that are under consideration as advanced neutron reflectors in the HighNESS project at the European Spallation Source. NJOY+NCrystal simplifies greatly the process to generate thermal scattering libraries (TSL) and this is exemplified with 213 new and updated TSL evaluations.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
