scholarly journals Ontology Fixing by Using Software Engineering Technology

2020 ◽  
Vol 10 (18) ◽  
pp. 6328
Author(s):  
Gabriela R. Roldan-Molina ◽  
Jose R. Mendez ◽  
Iryna Yevseyeva ◽  
Vitor Basto-Fernandes
Keyword(s):  
Software Engineering ◽  
User Interfaces ◽  
Design Patterns ◽  
Application Programming Interface ◽  
Scientific Software ◽  
Engineering Technology ◽  
Ontology Evaluation ◽  
Ontology Quality ◽  
Quality Assessments

This paper presents OntologyFixer, a web-based tool that supports a methodology to build, assess, and improve the quality of ontology web language (OWL) ontologies. Using our software, knowledge engineers are able to fix low-quality OWL ontologies (such as those created from natural language documents using ontology learning processes). The fixing process is guided by a set of metrics and fixing mechanisms provided by the tool, and executed primarily through automated changes (inspired by quick fix actions used in the software engineering domain). To evaluate the quality, the tool supports numerical and graphical quality assessments, focusing on ontology content and structure attributes. This tool follows principles, and provides features, typical of scientific software, including user parameter requests, logging, multithreading execution, and experiment repeatability, among others. OntologyFixer architecture takes advantage of model view controller (MVC), strategy, template, and factory design patterns; and decouples graphical user interfaces (GUI) from ontology quality metrics, ontology fixing, and REST (REpresentational State Transfer) API (Application Programming Interface) components (used for pitfall identification, and ontology evaluation). We also separate part of the OntologyFixer functionality into a new package called OntoMetrics, which focuses on the identification of symptoms and the evaluation of the quality of ontologies. Finally, OntologyFixer provides mechanisms to easily develop and integrate new quick fix methods.

scholarly journals Improving bioinformatics software quality through incorporation of software engineering practices

2022 ◽  
Vol 8 ◽  
pp. e839
Author(s):  
Adeeb Noor
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Software Quality ◽  
Formal Education ◽  
Scientific Software ◽  
Cultural Changes ◽  
Bioinformatics Software ◽  
Software Engineers ◽  
Engineering Practices

Background Bioinformatics software is developed for collecting, analyzing, integrating, and interpreting life science datasets that are often enormous. Bioinformatics engineers often lack the software engineering skills necessary for developing robust, maintainable, reusable software. This study presents review and discussion of the findings and efforts made to improve the quality of bioinformatics software. Methodology A systematic review was conducted of related literature that identifies core software engineering concepts for improving bioinformatics software development: requirements gathering, documentation, testing, and integration. The findings are presented with the aim of illuminating trends within the research that could lead to viable solutions to the struggles faced by bioinformatics engineers when developing scientific software. Results The findings suggest that bioinformatics engineers could significantly benefit from the incorporation of software engineering principles into their development efforts. This leads to suggestion of both cultural changes within bioinformatics research communities as well as adoption of software engineering disciplines into the formal education of bioinformatics engineers. Open management of scientific bioinformatics development projects can result in improved software quality through collaboration amongst both bioinformatics engineers and software engineers. Conclusions While strides have been made both in identification and solution of issues of particular import to bioinformatics software development, there is still room for improvement in terms of shifts in both the formal education of bioinformatics engineers as well as the culture and approaches of managing scientific bioinformatics research and development efforts.

Pragmatic Software Engineering for Computational Science

2014 ◽  
pp. 663-694
Author(s):  
David Worth ◽  
Chris Greenough ◽  
Shawn Chin
Keyword(s):  
Software Engineering ◽  
Best Practices ◽  
Best Practice ◽  
Scientific Software ◽  
Research Article ◽  
Development Life Cycle ◽  
Software Development Life Cycle ◽  
Tools And Techniques ◽  
Software Engineering Tools

The purpose of this chapter is to introduce scientific software developers to software engineering tools and techniques that will save them much blood, sweat, and tears and allow them to demonstrate the quality of their software. By introducing ideas around the software development life cycle, source code analysis, documentation, and testing, and touching on best practices, this chapter demonstrates ways in which scientific software can be improved and future developments made easier. This is not a research article on current software engineering methods, nor does it attempt to specify best practices. Its aim is to introduce components that can be built into a tailored process. The chapter draws upon ideas of best practice current in software engineering, but recommends using these only selectively. This is done by presenting details of tools that can be used to implement these ideas and a set of case studies to demonstrate their use.

scholarly journals Realization of best practices in software engineering and scientific writing through ready-to-use project skeletons

2021 ◽  
Vol 53 (10) ◽  
Author(s):  
Michael Haider ◽  
Michael Riesch ◽  
Christian Jirauschek
Keyword(s):  
Software Engineering ◽  
Best Practices ◽  
Software Design ◽  
Design Patterns ◽  
Development Process ◽  
Scientific Output ◽  
Scientific Writing ◽  
Scientific Software ◽  
Software Design Patterns ◽  
Academic Publications

AbstractEfforts in providing high-quality scientific software are hardly rewarded, as scientific output is typically measured in terms of publications in high ranking journals. As a result, scientific software is often developed without proper documentation and support of modern software design patterns. Ready-to-use project skeletons can be employed to accelerate the development process, while at the same time taking care of the implementation of best practices in software engineering. In this work, we revisit best practices in software engineering and review existing project skeletons. Special emphasis is given on the realization of best practices. Finally, we present a new project skeleton for scientific writing in "Image missing", which takes care of the attainment of best practices, adapted for being used in academic publications.

scholarly journals Metric Based Ontology Quality Evaluation

2019 ◽  
Vol 8 (6) ◽  
pp. 3072-3077
Keyword(s):  
Semantic Web ◽  
Quality Evaluation ◽  
Ontology Evaluation ◽  
New Knowledge ◽  
Key Factor ◽  
Ontology Quality ◽  
Structural Metrics

Ontology helps semantic web to process and understand large amount of data available in Internet. Ontology uses concepts and their relationship with each other to represent knowledge within a domain. The represented knowledge can be analysed, inferred and reused to make decisions and to derive new knowledge. The developed ontology has to be assessed for quality before using or reusing it. Evaluation becomes a key factor to determine the quality of ontology. Different approaches and methods are used to ensure the quality desired by the user. This article identifies various aspects of ontology, provides a framework for metric based ontology evaluation, elucidates components in the framework and develops a tool based on the framework. The framework checks the syntax, structural and semantic measures of ontology. While a reasoner takes care of the syntax and parser errors, the structural metrics analyses the taxonomy of ontology. Semantic measures deal with the distance of concepts in ontology. Further, competency questions are used to do custom based quality checking of a particular domain. This article provides a systematic way to identify and measure the quality of ontology based on metrics.

Pragmatic Software Engineering for Computational Science

2012 ◽  
pp. 119-149 ◽  
Author(s):  
David Worth ◽  
Chris Greenough ◽  
Shawn Chin
Keyword(s):  
Software Engineering ◽  
Best Practices ◽  
Best Practice ◽  
Scientific Software ◽  
Research Article ◽  
Development Life Cycle ◽  
Software Development Life Cycle ◽  
Tools And Techniques ◽  
Software Engineering Tools

The purpose of this chapter is to introduce scientific software developers to software engineering tools and techniques that will save them much blood, sweat, and tears and allow them to demonstrate the quality of their software. By introducing ideas around the software development life cycle, source code analysis, documentation, and testing, and touching on best practices, this chapter demonstrates ways in which scientific software can be improved and future developments made easier. This is not a research article on current software engineering methods, nor does it attempt to specify best practices. Its aim is to introduce components that can be built into a tailored process. The chapter draws upon ideas of best practice current in software engineering, but recommends using these only selectively. This is done by presenting details of tools that can be used to implement these ideas and a set of case studies to demonstrate their use.

JAVASCRIPT ASYNCHRONOUS PROGRAMMING

2019 ◽  
Vol 128 (2B) ◽  
pp. 5-16
Author(s):  
Tran Thanh Luong ◽  
Le My Canh
Keyword(s):  
Design Patterns ◽  
Source Code ◽  
Object Oriented ◽  
Exception Handling ◽  
Code Smells ◽  
Asynchronous Programming ◽  
Event Driven ◽  
Programming Patterns ◽  
The Impact

JavaScript has become more and more popular in recent years because its wealthy features as being dynamic, interpreted and object-oriented with first-class functions. Furthermore, JavaScript is designed with event-driven and I/O non-blocking model that boosts the performance of overall application especially in the case of Node.js. To take advantage of these characteristics, many design patterns that implement asynchronous programming for JavaScript were proposed. However, choosing a right pattern and implementing a good asynchronous source code is a challenge and thus easily lead into less robust application and low quality source code. Extended from our previous works on exception handling code smells in JavaScript and exception handling code smells in JavaScript asynchronous programming with promise, this research aims at studying the impact of three JavaScript asynchronous programming patterns on quality of source code and application.

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