How have Japanese companies become world leaders in the automotive and electronics industries, among others? What is the secret of their success? Two leading Japanese business experts, Ikujiro Nonaka and Hirotaka Takeuchi, are the first to tie the success of Japanese companies to their ability to create new knowledge and use it to produce successful products and technologies. In The Knowledge-Creating Company, Nonaka and Takeuchi provide an inside look at how Japanese companies go about creating this new knowledge organizationally. The authors point out that there are two types of knowledge: explicit knowledge, contained in manuals and procedures, and tacit knowledge, learned only by experience, and communicated only indirectly, through metaphor and analogy. U.S. managers focus on explicit knowledge. The Japanese, on the other hand, focus on tacit knowledge. And this, the authors argue, is the key to their success--the Japanese have learned how to transform tacit into explicit knowledge. To explain how this is done--and illuminate Japanese business practices as they do so--the authors range from Greek philosophy to Zen Buddhism, from classical economists to modern management gurus, illustrating the theory of organizational knowledge creation with case studies drawn from such firms as Honda, Canon, Matsushita, NEC, Nissan, 3M, GE, and even the U.S. Marines. For instance, using Matsushita's development of the Home Bakery (the world's first fully automated bread-baking machine for home use), they show how tacit knowledge can be converted to explicit knowledge: when the designers couldn't perfect the dough kneading mechanism, a software programmer apprenticed herself withthe master baker at Osaka International Hotel, gained a tacit understanding of kneading, and then conveyed this information to the engineers. In addition, the authors show that, to create knowledge, the best management style is neither top-down nor bottom-up, but rather what they call "middle-up-down," in which the middle managers form a bridge between the ideals of top management and the chaotic realities of the frontline. As we make the turn into the 21st century, a new society is emerging. Peter Drucker calls it the "knowledge society," one that is drastically different from the "industrial society," and one in which acquiring and applying knowledge will become key competitive factors. Nonaka and Takeuchi go a step further, arguing that creating knowledge will become the key to sustaining a competitive advantage in the future. Because the competitive environment and customer preferences changes constantly, knowledge perishes quickly. With The Knowledge-Creating Company, managers have at their fingertips years of insight from Japanese firms that reveal how to create knowledge continuously, and how to exploit it to make successful new products, services, and systems.

The knowledge-creating company

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

A key aspect of variability management in software product families is the explicit representation of the variability. Experiences at several industrial software development companies have shown that a software variability model should do four things: (1) uniformly represent variation points as first-class entities in all abstraction layers (ranging from features to code), (2) allow for the hierarchical organization of the variability, (3) allow for the first-class representation of simple (i.e., one-to-one) and complex (i.e., n-to-m) dependencies, and (4) allow for modeling the relations between dependencies. Existing variability modeling approaches support the first two requirements, but lack support for the latter two. The contribution of this paper is a framework for variability modeling-COVAMOF-that provides support for all four requirements.

COVAMOF: A framework for modeling variability in software product families

It’s a fact of life that organizations love to reorganize. Reorganizations have a profound effect on the way product lines are governed. We introduce the concept of the Responsibility, Authority, and Accountability (RAA) network. An RAA network assists in the governance process of product lines for internal information systems, even in the face of massive reorganization. Armour (“Reorg Cycle” [1]) describes the pressures of reorganization to balance the “dimensions of organization” (e.g., geography, customers, product technology); we apply polarity management to balance the dimensions. Armour describes the difficulty of applying hierarchical organization charts—“single dimension management structures”—to the above “multidimensional environments”; we apply lean RAA networks to span organization charts and provide the multidimensional view needed for product lines. Armour observes that network organization approaches “do not have a good track record”; our experience is that lean, resilient RAA networks document the product line’s governance architecture. These governance architecture patterns are applied repeatedly to the strata of products in the product line. We present the governance architect’s RAA to define, monitor, and sustain governance health using these tools: polarity maps, polarity networks, RAA maps, and RAA networks.

Software Product Lines

Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence” literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student’s success, through which student attributes to focus on, up to which machine learning method is more appropriate to the given problem. This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.

https://link.springer.com/content/pdf/10.1186%2Fs41239-020-0177-7.pdf

Predicting academic success in higher education: literature review and best practices

The prevention of students dropping out is considered very important in many educational institutions. In this paper we describe the results of an educational data analytics case study focused on detection of dropout of System Engineering (SE) undergraduate students after 7 years of enrollment in a Colombian university. Original data is extended and enriched using a feature engineering process. Our experimental results showed that simple algorithms achieve reliable levels of accuracy to identify predictors of dropout. Decision Trees, Logistic Regression and Na¨ive Bayes results were compared in order to propose the best option. Also, Watson Analytics is evaluated to establish the usability of the service for a non expert user. Main results are presented in order to decrease the dropout rate by identifying potential causes. In addition, we present some findings related to data quality to improve the students data collection process.

Applying Data Mining Techniques to Predict Student Dropout: A Case Study

The prevention of students dropping out is considered very important in many educational institutions. In this paper we describe the results of an educational data analytics case study focused on detection of dropout of Systems Engineering (SE) undergraduate students after 6 years of enrollment in a Colombian university. Original data is extended and enriched using a feature engineering process. Our experimental results showed that simple algorithms achieve reliable levels of accuracy to identify predictors of dropout. Decision Trees, Logistic Regression, Naive Bayes and Random Forest results were compared in order to propose the best option. Also, Watson Analytics is evaluated to establish the usability of the service for a non expert user. Main results are presented in order to decrease the dropout rate by identifying potential causes. In addition, we present some findings related to data quality to improve the students data collection process.

Predicting Student Drop-Out Rates Using Data Mining Techniques: A Case Study

[Context and Motivation] It is common for teams to take shortcuts during software development that, in the future, will lead to maintainability issues and affect productivity and development cost. Different types of technical debt may affect software projects, including those associated with software documentation. Although there are many studies on technical debt, few focus on documentation debt in an industrial environment. [Question/Problem] We aimed to identify how software practitioners perceive the occurrence of documentation debt in their projects. We present a combined analysis of the results from two complementary studies: a survey (InsighTD) and an interview-based case study. [Principal Ideas/Results] We provide a list of causes and effects of documentation debt, along with practices that can be used to deal with it during software development projects. [Contribution] We find that documentation debt is strongly related to requirements issues. Moreover, we propose a theoretical framework, which provides a comprehensive depiction of the documentation debt phenomenon.

Hearing the Voice of Software Practitioners on Causes, Effects, and Practices to Deal with Documentation Debt

ICT capabilities for supporting collaborative work on business processes within the digital content industry

Architectural tactics are decisions to efficiently solve quality attributes in software architecture. Security is a complex quality property due to its strong dependence on the application domain. However, the selection of security tactics in the definition of software architecture is guided informally and depends on the experience of the architect. This study presents a methodological approach to address and specify the quality attribute of security in architecture design applying security tactics. The approach is illustrated with a case study about a Tsunami Early Warning System.

Dario Correal

Papers

Applying Data Mining Techniques to Predict Student Dropout: A Case Study

Predicting Student Drop-Out Rates Using Data Mining Techniques: A Case Study

Hearing the Voice of Software Practitioners on Causes, Effects, and Practices to Deal with Documentation Debt

ICT capabilities for supporting collaborative work on business processes within the digital content industry

A methodological approach to apply security tactics in software architecture design