Factors in Software Quality. Volume I. Concepts and Definitions of Software Quality

McCall, Jim A.; Richards, Paul K.; Walters, Gene F.

doi:10.21236/ada049014

Cited by 165 publications

(151 citation statements)

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“…In 1977, Jim McCall [12] presented the first quality model toward the system developers and the system development process. McCall attempts to establish a link between users and developers by defining a number of software quality factors that reflect both the users' views and the developers' priorities.…”

Section: A a Brief History On Quality Modelsmentioning

confidence: 99%

An ISO 9126 Based Quality Model for the e-Learning Systems

Djouab¹,

Bari²

2016

IJIET

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Abstract-The emergence of the information and communication technology (ICT) in academia environment has contributed to the development of the e-learning systems. They have received a considerable attention in education and provided significant advantages such as convenience, flexibility and global learning community. While e-learning systems are growing significantly, there is a pressure on educational institutions and organizations to ensure the quality of e-learning systems. ISO 9126 is the most recognized and applied quality standard to specify and evaluate attributes of the software product. In this paper, ISO 9126 is extended with the specific characteristics of the e-learning software product. This extension is done by defining the quality characteristics of the e-learning system and integrating them in the ISO 9126 model. Results of this study will serve as a basis for the evaluation of an existing system. Index Terms-E-learning, ISO 9126, software quality model, quality characteristics, online education, high education.

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Section: A a Brief History On Quality Modelsmentioning

confidence: 99%

An ISO 9126 Based Quality Model for the e-Learning Systems

Djouab¹,

Bari²

2016

IJIET

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“…As discussed previously, software quality is intrinsically hierarchical [2,19,3,10]. Existing quality standards and other literature in the area tend to adopt the approach of starting from the high-level notion of quality and gradually decomposing it into more concrete notions that are individually more straightforward to understand and assess.…”

Section: Developing or Selecting A Hierarchy Of Quality Factorsmentioning

confidence: 99%

Using evidential reasoning to make qualified predictions of software quality

Walkinshaw

2013

Proceedings of the 9th International Conference on Predictive Models in Software Engineering

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Software quality is commonly characterised in a top-down manner. High-level notions such as quality are decomposed into hierarchies of sub-factors, ranging from abstract notions such as maintainability and reliability to lower-level notions such as test coverage or team-size. Assessments of abstract factors are derived from relevant sources of information about their respective lower-level sub-factors, by surveying sources such as metrics data and inspection reports. This can be difficult because (1) evidence might not be available, (2) interpretations of the data with respect to certain quality factors may be subject to doubt and intuition, and (3) there is no straightforward means of blending hierarchies of heterogeneous data into a single coherent and quantitative prediction of quality. This paper shows how Evidential Reasoning (ER) -a mathematical technique for reasoning about uncertainty and evidence -can address this problem. It enables the quality assessment to proceed in a bottomup manner, by the provision of low-level assessments that make any uncertainty explicit, and automatically propagating these up to higher-level 'belief-functions' that accurately summarise the developer's opinion and make explicit any doubt or ignorance.

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“…First, to make the model widely applicable, we based the unified model on broad classifications of nonfunctional/quality requirements in literature [2]- [13], unifying the different aspects of performance described in the individual classifications. From this unified model, we derive a content model including a notion of completeness for performance requirements.…”

Section: Introductionmentioning

confidence: 99%