* Starting from Need* Evolution of the Toyota Production System* Further Development* Genealogy of the Toyota Production System* The True Intention of the Ford System* Surviving the Low-Growth Period

Toyota production system : beyond large-scale production

Resena de la obra de Don A. Dillman, Jolene D. Smyth y Leah Melani Christian: Internet, Phone, Mail and Mixed-Mode Surveys. The Tailored Design Method. New Jersey: John Wiley and Sons

https://dialnet.unirioja.es/descarga/articulo/7287796.pdf

Internet, phone, mail and mixed-mode surveys: the tailored design method: reseña

Subsequently, an agreement on technical co-operation between ISO and CEN was approved by the ISO Council resolution 18/1990 and the CEN General Assembly resolution 3/1990. This agreement (called the Vienna Agreement) was published in June 1991. It is accompanied by common ISO-CEN “Guidelines for the TC/SC Chairmen and Secretariats for implementation”, approved in 1992 and revised in September 1998.

International Organization for Standardization (ISO)

App stores like Google Play and Apple AppStore have over 3 million apps covering nearly every kind of software and service. Billions of users regularly download, use, and review these apps. Recent studies have shown that reviews written by the users represent a rich source of information for the app vendors and the developers, as they include information about bugs, ideas for new features, or documentation of released features. The majority of the reviews, however, is rather non-informative just praising the app and repeating to the star ratings in words. This paper introduces several probabilistic techniques to classify app reviews into four types: bug reports, feature requests, user experiences, and text ratings. For this, we use review metadata such as the star rating and the tense, as well as, text classification, natural language processing, and sentiment analysis techniques. We conducted a series of experiments to compare the accuracy of the techniques and compared them with simple string matching. We found that metadata alone results in a poor classification accuracy. When combined with simple text classification and natural language preprocessing of the text--particularly with bigrams and lemmatization--the classification precision for all review types got up to 88---92 % and the recall up to 90---99 %. Multiple binary classifiers outperformed single multiclass classifiers. Our results inspired the design of a review analytics tool, which should help app vendors and developers deal with the large amount of reviews, filter critical reviews, and assign them to the appropriate stakeholders. We describe the tool main features and summarize nine interviews with practitioners on how review analytics tools including ours could be used in practice.

On the Automatic Classification of App Reviews.

On-Line Analytical Processing (OLAP) systems based on multidimensional databases are essential elements of decision support. However, most existing data is stored in ordinary relational OLTP databases, i.e., data has to be (re-) modeled as multidimensional cubes before the advantages of OLAP tools are available. In this paper we present an approach for the automatic construction of multidimensional OLAP database schemas from existing relational OLTP databases, enabling easy OLAP design and analysis for most existing data sources. This is achieved through a set of practical and effective algorithms for discovering multidimensional schemas from relational databases. The algorithms take a wide range of available metadata into account in the discovery process, including functional and inclusion dependencies, and key and cardinality information.

Discovering multidimensional structure in relational data

Companies dealing with Artificial Intelligence (AI) models in Autonomous Systems (AS) face several problems, such as users’ lack of trust in adverse or unknown conditions, gaps between software engineering and AI model development, and operation in a continuously changing operational environment. This work-in-progress paper aims to close the gap between the development and operation of trustworthy AI-based AS by defining an approach that coordinates both activities. We synthesize the main challenges of AI-based AS in industrial settings. We reflect on the research efforts required to overcome these challenges and propose a novel, holistic DevOps approach to put it into practice. We elaborate on four research directions: (a) increased users’ trust by monitoring operational AI-based AS and identifying self-adaptation needs in critical situations; (b) integrated agile process for the development and evolution of AI models and AS; (c) continuous deployment of different context-specific instances of AI models in a distributed setting of AS; and (d) holistic DevOps-based lifecycle for AI-based AS.

/pdf/developing-and-operating-artificial-intelligence-models-in-3aqrk4ejgw.pdf

Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems

The QuASD workshop aims at investigating product and process quality in the context of agile and rapid software development. The objective of the workshop is to exchange challenges, experiences, and solutions among researchers and practitioners to bring agile and rapid software development processes a step further to seamless integrating quality management activities into their practices. In this second edition of the workshop we expect to foster the exchange of ideas between researchers and industry and consolidate a research agenda and collaborations.

/pdf/1st-quasd-workshop-managing-quality-in-agile-and-rapid-3ue42czcux.pdf

1st QuASD Workshop: Managing Quality in Agile and Rapid Software Development Processes

Software quality is an essential competitive factor for the success of software companies today. Increasing software quality levels of software products and services requires an adequate integration of quality requirements (QRs) in the software life-cycle, which is still scarcely supported in current rapid software development (RSD) approaches. RSD refers to the organizational capability to develop, release, and learn from software in rapid cycles. Q-Rapids (Quality-aware Rapid Software Development) research project aims to support decision-makers for QRs management in RSD. The goal of this survey is to explore how software development organizations using RSD manage QRs. The survey focuses on: (a) how data is gathered for assessing decisions related to product evolution, (b) how QRs are managed, and (c) the considered QRs in their products. We received 30 responses, from them we can conclude that most of the companies gather data both automatically and manually; QRs are managed together with functional requirements, sometimes or very often functionality gets priority over quality; and, the most reported QRs are relia-bility, performance, and security.

/pdf/how-practitioners-manage-quality-requirements-in-rapid-3ecxxyah64.pdf

How Practitioners Manage Quality Requirements in Rapid Software Development: A Survey

Background: Accessing relevant data on the product, process, and usage perspectives of software as well as integrating and analyzing such data is crucial for getting reliable and timely actionable insights aimed at continuously managing software quality in Rapid Software Development (RSD). In this context, several software analytics tools have been developed in recent years. However, there is a lack of explainable software analytics that software practitioners trust. Aims: We aimed at creating a quality model (called Q-Rapids quality model) for actionable analytics in RSD, implementing it, and evaluating its understandability and relevance. Method: We performed workshops at four companies in order to determine relevant metrics as well as product and process factors. We also elicited how these metrics and factors are used and interpreted by practitioners when making decisions in RSD. We specified the Q-Rapids quality model by comparing and integrating the results of the four workshops. Then we implemented the Q-Rapids tool to support the usage of the Q-Rapids quality model as well as the gathering, integration, and analysis of the required data. Afterwards we installed the Q-Rapids tool in the four companies and performed semi-structured interviews with eight product owners to evaluate the understandability and relevance of the Q-Rapids quality model. Results: The participants of the evaluation perceived the metrics as well as the product and process factors of the Q-Rapids quality model as understandable. Also, they considered the Q-Rapids quality model relevant for identifying product and process deficiencies (e.g., blocking code situations). Conclusions: By means of heterogeneous data sources, the Q-Rapids quality model enables detecting problems that take more time to find manually and adds transparency among the perspectives of system, process, and usage.

A Quality Model for Actionable Analytics in Rapid Software Development.

This work was supported in part by CNPq (Grant: 141602/2017-1, 312634/2018-8), FAPESP (Grant: 2018/07437-9, 2018/24173-5, 2017/22107-2, 2017/06195-9), and CoSmart Spanish project (contract TIN2017-83964-R).

Silverio Martínez-Fernández

Papers

Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems

1st QuASD Workshop: Managing Quality in Agile and Rapid Software Development Processes

How Practitioners Manage Quality Requirements in Rapid Software Development: A Survey

A Quality Model for Actionable Analytics in Rapid Software Development.

Approaches for describing reference architectures: A systematic mapping study