Collaborative Requirement Management System Developed for CMMI-Coherent Software Engineering

2007 ◽  
Author(s):  
Justin J.Y. Lin ◽  
Yung-Sung Lin
Keyword(s):  
Software Engineering ◽  
Management System ◽  
Requirement Management

Metrics for Concept-Oriented Knowledge Bases

1998 ◽  
Vol 08 (02) ◽  
pp. 161-188 ◽  
Author(s):  
Timothy C. Lethbridge
Keyword(s):  
Knowledge Management ◽  
Software Engineering ◽  
Knowledge Base ◽  
Management System ◽  
Software Metrics ◽  
Knowledge Engineering ◽  
Knowledge Bases ◽  
Knowledge Management System ◽  
The Other ◽  
Develop Software

Metrics are widely researched and used in software engineering; however there is little analogous work in the field of knowledge engineering. In other words, there are no widely-known metrics that the developers of knowledge bases can use to monitor and improve their work. In this paper we adapt the GQM (Goals-Questions-Metrics) methodology that is used to select and develop software metrics. We use the methodology to develop a series of metrics that measure the size and complexity of concept-oriented knowledge bases. Two of the metrics measure raw size; seven measure various aspects of complexity on scales of 0 to 1, and are shown to be largely independent of each other. The remaining three are compound metrics that combine aspects of the other nine in an attempt to measure the overall 'difficulty' or 'complexity' of a knowledge base. The metrics have been implemented and tested in the context of a knowledge management system called CODE4.

scholarly journals The Knowledge Repository Management System Architecture of Digital Knowledge Engineering using Machine Learning to Promote Software Engineering Competencies

2019 ◽  
Vol 14 (12) ◽  
pp. 42 ◽  
Author(s):  
Nattaphol Thanachawengsakul ◽  
Panita Wannapiroon ◽  
Prachyanun Nilsook
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
System Architecture ◽  
Management System ◽  
Knowledge Engineering ◽  
Research Approach ◽  
Storage Unit ◽  
Knowledge Repository ◽  
Application Service ◽  
Digital Knowledge

The knowledge repository management system architecture of digital knowledge engineering using machine learning (KRMS-SWE) to promote software engineering competencies is comprised of four parts, as follows: 1) device service, 2) application service, 3) module service of the KRMS-SWE and 4) machine learning service and storage unit. The knowledge creation, storage, testing and assessing of students’ knowledge in software engineering is carried out using a knowledge verification process with machine learning and divided into six steps, as follows: pre-processing, filtration, stemming, indexing, data mining and interpretation and evaluation. The overall result regarding the suitability of the KRMS-SWE is assessed by five experts who have high levels of experience in related fields. The findings reveal that this research approach can be applied to the future development of the KRMS-SWE.

Sign in / Sign up

Export Citation Format

Share Document