Data Mining Practical Machine Learning Tools and Techniques

Purpose – This study aims to understand and compare how the mechanism of innovative processes in
the information technology (IT) industry – the most innovative industry worldwide – is shaped in Poland
and the USA in terms of tacit knowledge awareness and sharing driven by a culture of knowledge and
learning, composed of a learning climate and mistake acceptance
Design/methodology/approach – Study samples were drawn from the IT industry in Poland (n = 350)
and the USA (n = 370) and analyzed using the structural equation modeling method
Findings – True learning derives from mistake acceptance As a result of a risk-taking attitude and critical
thinking, the IT industry in the USA is consistently innovation-oriented Specifically, external innovations are
highly correlated with internal innovations Moreover, a knowledge culture supports a learning culture via a
learning climate A learning climate is an important facilitator for learning from mistakes
Originality/value – This study revealed that a high level of mistake acceptance stimulates a risk-taking
an attitude that offers a high level of tacit knowledge awareness as a result of critical thinking, but critical
thinking without readiness to take a risk is useless for tacit knowledge capturing

/pdf/do-mistakes-acceptance-foster-innovation-polish-and-us-cross-2uqbdtej0h.pdf

Do mistakes acceptance foster innovation? Polish and US cross-country study of tacit knowledge sharing in IT

Petri net simulation of multi-department emergency response to avert domino effects in chemical industry accidents

IMA health state evaluation using deep feature learning with quantum neural network

Code remains largely hand-made by humans and, as such, writing code is prone to error. Many previous studies have focused on the technical reasons for these errors and provided developers with increasingly sophisticated tools. Few studies have looked in detail at why code errors have been made from a human perspective. We use Human Error Theory to frame our exploratory study and use semi-structured interviews to uncover a preliminary understanding of the errors developers make while coding. We look particularly at the Skill-Based (SB) errors reported by 27 professional software developers. We found that the complexity of the development environment is one of the most frequently reported reasons for errors. Maintaining concentration and focus on a particular task also underpins many developer errors. We found that developers struggle with effective mitigation strategies for their errors, reporting strategies largely based on improving their own willpower to concentrate better on coding tasks. We discuss how using Reason's Swiss Cheese model may help reduce errors during software development. This model ensures that layers of tool, process and management mitigation are in place to prevent developer errors from causing system failures.

/pdf/how-software-developers-mitigate-their-errors-when-4lk4ygcid0.pdf

How Software Developers Mitigate their Errors when Developing Code

Software defect prevention based on human error theories

Expert systems are widely used for software fault diagnosis. Knowledge in these Expert Systems could be learned from different ways, such as human expert, machine learning and etc‥ The scale of the knowledge is increasing quickly, due to the development of testing tools and techniques. However, it is a big challenge to integrate the knowledge from different sources. In this paper, a novel knowledge fusion method based on D-S theory is provided to meet the challenge and its application on practical software fault diagnosis shows satisfied results.

Knowledge fusion based on D-S theory and its application on Expert System for software fault diagnosis

Software fault prediction (SFP) is an active issue in software engineering (SE). At present, machine learning (ML) has been successfully applied to SFP classification problems. However, one of the challenges for building software fault prediction models (SFPM) is processing high dimensional datasets, which include many irrelevant and redundant features. To address this issue, feature selection techniques, mainly contain wrapper methods and filter methods, are used. In the paper, we report an empirical study aimed at providing a novel approach to select feature for SFP.

A Novel Feature Selection Method for Software Fault Prediction Model

A Modeling Language Based on UML for Modeling Simulation Testing System of Avionic Software

An IPC-based Prolog design pattern for integrating backward chaining inference into applications or embedded systems

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