Data Mining Practical Machine Learning Tools and Techniques

Software testing is an important process in product development of software companies to ensure the product quality. The developed application must satisfy the customer needs and meets the industrial standards without any compromise. Thus, verification of products through manual test engineer and validate the product once it meets all the necessary requirements. The issues in manual testing is its high computation and analysis time, accuracy and reliability. In order to reduce the issues in manual testing automatic testing is introduced. Though it is also an application which requires parameters to test the given product. Efficient tuning of the product could be achieved through automatic testing. This proposed research work provides an optimized automatic software testing model through differential evolution and ant colony optimization as a hybrid model to achieve improved accuracy and reliability in software testing. Conventional models like artificial neural network and particle swarm optimization are compared with proposed model to validate the reliability of proposed model.

https://www.irojournals.com/jucct/V2/I3/02.pdf

Reliable Automated Software Testing Through Hybrid Optimization Algorithm

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

Software defect prevention based on human error theories

A hazard analysis via an improved timed colored petri net with time-space coupling safety constraint

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.

Bin Liu

Papers

Software defect prevention based on human error theories

A hazard analysis via an improved timed colored petri net with time-space coupling safety constraint

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

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