User Stories record what must be built in projects that use agile practices.
User Stories serve both to estimate effort, generally measured in Story Points,
and to plan what should be done in a Sprint. Therefore, it is essential to
train software engineers on how to create simple, easily readable, and
comprehensive User Stories. For that reason, we designed, implemented, applied,
and evaluated a web application called User Story Tutor (UST). UST checks the
description of a given User Story for readability, and if needed, recommends
appropriate practices for improvement. UST also estimates a User Story effort
in Story Points using Machine Learning techniques. As such UST may support the
continuing education of agile development teams when writing and reviewing User
Stories. UST's ease of use was evaluated by 40 agile practitioners according to
the Technology Acceptance Model (TAM) and AttrakDiff. The TAM evaluation
averages were good in almost all considered variables. Application of the
AttrakDiff evaluation framework produced similar good results. Apparently, UST
can be used with good reliability. Applying UST to assist in the construction
of User Stories is a viable technique that, at the very least, can be used by
agile developments to complement and enhance current User Story creation.

User Story Tutor (UST) 是一个检查用户故事描述可读性、提供改进建议、使用机器学习技术估计用户故事工作量的 Web 应用程序，可用于敏捷开发团队继续教育和增强当前用户故事创建的有效技术。

用户故事导师（UST）支持敏捷软件开发者

User Story Tutor (UST) to Support Agile Software Developers

Many different worldwide initiatives are promoting the transformation from
machine dominant manufacturing to digital manufacturing. Thus, to achieve a
successful transformation to Industry 4.0 standard, manufacturing enterprises
are required to implement a clear roadmap. However, Small and Medium
Manufacturing Enterprises (SMEs) encounter many barriers and difficulties
(economical, technical, cultural, etc.) in the implementation of Industry 4.0.
Although several works deal with the incorporation of Industry 4.0 technologies
in the area of the product and supply chain life cycles, which SMEs could use
as reference, this is not the case for the customer life cycle. Thus, we
present two contributions that can help the software engineers of those SMEs to
incorporate Industry 4.0 technologies in the context of the customer life
cycle. The first contribution is a methodology that can help those software
engineers in the task of creating new software services, aligned with Industry
4.0, that allow to change how customers interact with enterprises and the
experiences they have while interacting with them. The methodology details a
set of stages that are divided into phases which in turn are made up of
activities. It places special emphasis on the incorporation of semantics
descriptions and 3D visualization in the implementation of those new services.
The second contribution is a system developed for a real manufacturing
scenario, using the proposed methodology, which allows to observe the
possibilities that this kind of systems can offer to SMEs in two phases of the
customer life cycle: Discover & Shop, and Use & Service.

制造企业在实施工业 4.0 时遇到许多困难，我们提供了一种方法论和一个系统以帮助中小制造企业的软件工程师在客户生命周期中引入工业 4.0 技术，并特别强调了语义描述和 3D 可视化的重要性。

朝向工业 4.0 的实施：基于方法论的面向客户生命周期的方法

Towards the implementation of Industry 4.0: A methodology-based approach  oriented to the customer life cycle

The use of machine learning or artificial intelligence (ML/AI) holds
substantial potential toward improving many functions and needs of the public
sector. In practice however, integrating ML/AI components into public sector
applications is severely limited not only by the fragility of these components
and their algorithms, but also because of mismatches between components of
ML-enabled systems. For example, if an ML model is trained on data that is
different from data in the operational environment, field performance of the ML
component will be dramatically reduced. Separate from software engineering
considerations, the expertise needed to field an ML/AI component within a
system frequently comes from outside software engineering. As a result,
assumptions and even descriptive language used by practitioners from these
different disciplines can exacerbate other challenges to integrating ML/AI
components into larger systems. We are investigating classes of mismatches in
ML/AI systems integration, to identify the implicit assumptions made by
practitioners in different fields (data scientists, software engineers,
operations staff) and find ways to communicate the appropriate information
explicitly. We will discuss a few categories of mismatch, and provide examples
from each class. To enable ML/AI components to be fielded in a meaningful way,
we will need to understand the mismatches that exist and develop practices to
mitigate the impacts of these mismatches.

本文研究了在 ML/AI 系统集成中，不同领域的从业人员（数据科学家、软件工程师和运营人员）所做的隐含假设，并从几个不同的角度提供了实例，以便更好地理解系统匹配的不同类别。为了使 ML/AI 组件有意义地推广，我们需要了解现有的不匹配之处，并制定应对这些不匹配影响的措施。