Among the multitude of software development processes available, hardly any is used by the book. Regardless of company size or industry sector, a majority of project teams and companies use customized processes that combine different development methods -- so-called hybrid development methods. Even though such hybrid development methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. In this paper, we make a first step towards devising such guidelines. Grounded in 1,467 data points from a large-scale online survey among practitioners, we study the current state of practice in process use to answer the question: What are hybrid development methods made of? Our findings reveal that only eight methods and few practices build the core of modern software development. This small set allows for statistically constructing hybrid development methods. Using an 85% agreement level in the participants' selections, we provide two examples illustrating how hybrid development methods are characterized by the practices they are made of. Our evidence-based analysis approach lays the foundation for devising hybrid development methods.

0
下载
关闭预览

相关内容

Processing 是一门开源编程语言和与之配套的集成开发环境(IDE)的名称。Processing 在电子艺术和视觉设计社区被用来教授编程基础,并运用于大量的新媒体和互动艺术作品中。

Fast and light-weight methods for animating 3D characters are desirable in various applications such as computer games. We present a learning-based approach to enhance skinning-based animations of 3D characters with vivid secondary motion effects. We design a neural network that encodes each local patch of a character simulation mesh where the edges implicitly encode the internal forces between the neighboring vertices. The network emulates the ordinary differential equations of the character dynamics, predicting new vertex positions from the current accelerations, velocities and positions. Being a local method, our network is independent of the mesh topology and generalizes to arbitrarily shaped 3D character meshes at test time. We further represent per-vertex constraints and material properties such as stiffness, enabling us to easily adjust the dynamics in different parts of the mesh. We evaluate our method on various character meshes and complex motion sequences. Our method can be over 30 times more efficient than ground-truth physically based simulation, and outperforms alternative solutions that provide fast approximations.

0
0
下载
预览

Due to researchers'aim to study personalized recommendations for different business fields, the summary of recommendation methods in specific fields is of practical significance. News recommendation systems were the earliest research field regarding recommendation systems, and were also the earliest recommendation field to apply the collaborative filtering method. In addition, news is real-time and rich in content, which makes news recommendation methods more challenging than in other fields. Thus, this paper summarizes the research progress regarding news recommendation methods. From 2018 to 2020, developed news recommendation methods were mainly deep learning-based, attention-based, and knowledge graphs-based. As of 2020, there are many news recommendation methods that combine attention mechanisms and knowledge graphs. However, these methods were all developed based on basic methods (the collaborative filtering method, the content-based recommendation method, and a mixed recommendation method combining the two). In order to allow researchers to have a detailed understanding of the development process of news recommendation methods, the news recommendation methods surveyed in this paper, which cover nearly 10 years, are divided into three categories according to the abovementioned basic methods. Firstly, the paper introduces the basic ideas of each category of methods and then summarizes the recommendation methods that are combined with other methods based on each category of methods and according to the time sequence of research results. Finally, this paper also summarizes the challenges confronting news recommendation systems.

0
0
下载
预览

High throughput sequencing (HTS)-based technology enables identifying and quantifying non-culturable microbial organisms in all environments. Microbial sequences have enhanced our understanding of the human microbiome, the soil and plant environment, and the marine environment. All molecular microbial data pose statistical challenges due to contamination sequences from reagents, batch effects, unequal sampling, and undetected taxa. Technical biases and heteroscedasticity have the strongest effects, but different strains across subjects and environments also make direct differential abundance testing unwieldy. We provide an introduction to a few statistical tools that can overcome some of these difficulties and demonstrate those tools on an example. We show how standard statistical methods, such as simple hierarchical mixture and topic models, can facilitate inferences on latent microbial communities. We also review some nonparametric Bayesian approaches that combine visualization and uncertainty quantification. The intersection of molecular microbial biology and statistics is an exciting new venue. Finally, we list some of the important open problems that would benefit from more careful statistical method development.

0
0
下载
预览

Visual data storytelling is gaining importance as a means of presenting data-driven information or analysis results, especially to the general public. This has resulted in design principles being proposed for data-driven storytelling, and new authoring tools being created to aid such storytelling. However, data analysts typically lack sufficient background in design and storytelling to make effective use of these principles and authoring tools. To assist this process, we present ChartStory for crafting data stories from a collection of user-created charts, using a style akin to comic panels to imply the underlying sequence and logic of data-driven narratives. Our approach is to operationalize established design principles into an advanced pipeline which characterizes charts by their properties and similarity, and recommends ways to partition, layout, and caption story pieces to serve a narrative. ChartStory also augments this pipeline with intuitive user interactions for visual refinement of generated data comics. We extensively and holistically evaluate ChartStory via a trio of studies. We first assess how the tool supports data comic creation in comparison to a manual baseline tool. Data comics from this study are subsequently compared and evaluated to ChartStory's automated recommendations by a team of narrative visualization practitioners. This is followed by a pair of interview studies with data scientists using their own datasets and charts who provide an additional assessment of the system. We find that ChartStory provides cogent recommendations for narrative generation, resulting in data comics that compare favorably to manually-created ones.

0
0
下载
预览

A recent study showed that more than 70\% of researchers fail to reproduce their peers's experiments and more than half fail to reproduce their own experiments. Obviously, from a perspective of scientific quality this is a more than unsatisfying numbers. One approach to mitigate this flaw lies in the transparent provision of relevant research data to increase the base of available material to evaluate and possibly reconduct experiments. However, such data needs to be presented and accessed in a findable and purposefully usable way. In this work, we report the development of a programming interface to enhance findability and accessibility of research data (available in DSpace systems) and hence reproducibility of scientific experiments with data. This interface allows researchers to (i) find research data in multiples languages trough automatic translation of metadata; (ii) display a preview of data without download it beforehand; (iii) provide a detailed statistics of the data with interactive graphs for quality assessment; (iv) automatic download of data directly from Python-based experiments. Usability tests revealed that this interface improves the effectiveness, efficiency and satisfaction during the reuse of research data.

0
0
下载
预览

In recent years, a vivid interest in hybrid development methods has been observed as practitioners combine various approaches to software creation to improve productivity, product quality, and adaptability of the process to react to change. Scientific papers on the subject proliferate, however evaluation of the effectiveness of hybrid methods in academic contexts has yet to follow. The work presented investigates if introducing a hybrid approach for student projects brings added value as compared to iterative and sequential development. A controlled experiment was carried out among Bachelor students of a French engineering school to assess the impacts of a given development method on the success of student computing undertakings. Its three dimensions were examined via a set of metrics: product quality, team productivity as well as human factors (teamwork quality & learning outcomes). Several patterns were observed, which can provide a starting point for educators and researchers wishing to tailor or design a software development process for academic needs.

0
0
下载
预览

Most scientific publications follow the familiar recipe of (i) obtain data, (ii) fit a model, and (iii) comment on the scientific relevance of the effects of particular covariates in that model. This approach, however, ignores the fact that there may exist a multitude of similarly-accurate models in which the implied effects of individual covariates may be vastly different. This problem of finding an entire collection of plausible models has also received relatively little attention in the statistics community, with nearly all of the proposed methodologies being narrowly tailored to a particular model class and/or requiring an exhaustive search over all possible models, making them largely infeasible in the current big data era. This work develops the idea of forward stability and proposes a novel, computationally-efficient approach to finding collections of accurate models we refer to as model path selection (MPS). MPS builds up a plausible model collection via a forward selection approach and is entirely agnostic to the model class and loss function employed. The resulting model collection can be displayed in a simple and intuitive graphical fashion, easily allowing practitioners to visualize whether some covariates can be swapped for others with minimal loss.

0
0
下载
预览

This paper derives the analytic form of the $h$-step ahead prediction density of a GARCH(1,1) process under Gaussian innovations, with a possibly asymmetric news impact curve. The contributions of the paper consists both in the derivation of the analytic form of the density, and in its application to a number of econometric problems. A first application of the explicit formulae is to characterize the degree of non-Gaussianity of the prediction distribution; for some values encountered in applications, deviations of the prediction distribution from the Gaussian are found to be small, and sometimes not. the Gaussian density as an approximation of the true prediction density. A second application of the formulae is to compute exact tail probabilities and functionals, such as the Value at Risk and the Expected Shortfall, that measure risk when the underlying asset return is generated by a Gaussian GARCH(1,1). This improves on existing methods based on Monte Carlo simulations and (non-parametric) estimation techniques, because the present exact formulae are free of Monte Carlo estimation uncertainty. A third application is the definition of uncertainty regions for functionals of the prediction distribution that reflect in-sample estimation uncertainty. These applications are illustrated on selected empirical examples.

0
0
下载
预览

The task of Question Answering has gained prominence in the past few decades for testing the ability of machines to understand natural language. Large datasets for Machine Reading have led to the development of neural models that cater to deeper language understanding compared to information retrieval tasks. Different components in these neural architectures are intended to tackle different challenges. As a first step towards achieving generalization across multiple domains, we attempt to understand and compare the peculiarities of existing end-to-end neural models on the Stanford Question Answering Dataset (SQuAD) by performing quantitative as well as qualitative analysis of the results attained by each of them. We observed that prediction errors reflect certain model-specific biases, which we further discuss in this paper.

0
6
下载
预览
小贴士
相关论文
Mianlun Zheng,Yi Zhou,Duygu Ceylan,Jernej Barbič
0+阅读 · 3月8日
Pratheepa Jeganathan,Susan P. Holmes
0+阅读 · 3月6日
Jian Zhao,Shenyu Xu,Senthil Chandrasegaran,Chris Bryan,Fan Du,Aditi Mishra,Xin Qian,Yiran Li,Kwan-Liu Ma
0+阅读 · 3月6日
Andrew Tristan,Vinicius Woloszyn Nir,Ben Kaden
0+阅读 · 3月5日
Rafał Włodarski,Jean-Rémy Falleri,Corinne Parvéry
0+阅读 · 3月5日
Nicholas Kissel,Lucas Mentch
0+阅读 · 3月5日
Paul Ralph,Nauman bin Ali,Sebastian Baltes,Domenico Bianculli,Jessica Diaz,Yvonne Dittrich,Neil Ernst,Michael Felderer,Robert Feldt,Antonio Filieri,Breno Bernard Nicolau de França,Carlo Alberto Furia,Greg Gay,Nicolas Gold,Daniel Graziotin,Pinjia He,Rashina Hoda,Natalia Juristo,Barbara Kitchenham,Valentina Lenarduzzi,Jorge Martínez,Jorge Melegati,Daniel Mendez,Tim Menzies,Jefferson Molleri,Dietmar Pfahl,Romain Robbes,Daniel Russo,Nyyti Saarimäki,Federica Sarro,Davide Taibi,Janet Siegmund,Diomidis Spinellis,Miroslaw Staron,Klaas Stol,Margaret-Anne Storey,Davide Taibi,Damian Tamburri,Marco Torchiano,Christoph Treude,Burak Turhan,Xiaofeng Wang,Sira Vegas
0+阅读 · 3月4日
Karim M. Abadir,Alessandra Luati,Paolo Paruolo
0+阅读 · 3月4日
Soumya Wadhwa,Khyathi Raghavi Chandu,Eric Nyberg
6+阅读 · 2018年6月18日
相关资讯
已删除
AI掘金志
4+阅读 · 2019年7月8日
Transferring Knowledge across Learning Processes
CreateAMind
6+阅读 · 2019年5月18日
逆强化学习-学习人先验的动机
CreateAMind
4+阅读 · 2019年1月18日
RL 真经
CreateAMind
4+阅读 · 2018年12月28日
A Technical Overview of AI & ML in 2018 & Trends for 2019
待字闺中
10+阅读 · 2018年12月24日
disentangled-representation-papers
CreateAMind
20+阅读 · 2018年9月12日
LibRec 精选:推荐的可解释性[综述]
LibRec智能推荐
5+阅读 · 2018年5月4日
【推荐】直接未来预测:增强学习监督学习
机器学习研究会
6+阅读 · 2017年11月24日
【推荐】RNN/LSTM时序预测
机器学习研究会
21+阅读 · 2017年9月8日
【推荐】深度学习目标检测概览
机器学习研究会
9+阅读 · 2017年9月1日
Top