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QATCH - An adaptive framework for software product quality assessment
- Source :
- Expert Systems with Applications. 86:350-366
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- The subjectivity that underlies the notion of quality does not allow the design and development of a universally accepted mechanism for software quality assessment. This is why contemporary research is now focused on seeking mechanisms able to produce software quality models that can be easily adjusted to custom user needs. In this context, we introduce QATCH, an integrated framework that applies static analysis to benchmark repositories in order to generate software quality models tailored to stakeholder specifications. Fuzzy multi-criteria decision-making is employed in order to model the uncertainty imposed by experts’ judgments. These judgments can be expressed into linguistic values, which makes the process more intuitive. Furthermore, a robust software quality model, the base model, is generated by the system, which is used in the experiments for QATCH system verification. The paper provides an extensive analysis of QATCH and thoroughly discusses its validity and added value in the field of software quality through a number of individual experiments.
- Subjects :
- Computer science
02 engineering and technology
computer.software_genre
Software
Artificial Intelligence
Software sizing
0202 electrical engineering, electronic engineering, information engineering
Software quality analyst
Software verification and validation
Software design description
business.industry
General Engineering
Software development
020207 software engineering
Static analysis
Software metric
Software quality
Computer Science Applications
Software framework
Software deployment
Goal-Driven Software Development Process
Software construction
Personal software process
Package development process
Software design
020201 artificial intelligence & image processing
Data mining
Software engineering
business
computer
Software quality control
Software verification
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 86
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........679c7f552caded9be2a32384e3af752e