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QATCH - An adaptive framework for software product quality assessment

•An adaptive software quality model extraction methodology is proposed.•The respective software assessment framework is developed and available online.•Static analysis and benchmarking is adopted for automated threshold derivation.•A fuzzy AHP technique is introduced for model weights elicitation.•A...

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Bibliographic Details
Published in:Expert systems with applications 2017-11, Vol.86, p.350-366
Main Authors: Siavvas, Miltiadis G., Chatzidimitriou, Kyriakos C., Symeonidis, Andreas L.
Format: Article
Language:English
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Summary:•An adaptive software quality model extraction methodology is proposed.•The respective software assessment framework is developed and available online.•Static analysis and benchmarking is adopted for automated threshold derivation.•A fuzzy AHP technique is introduced for model weights elicitation.•A base quality model is generated and employed for the verification of the framework. 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.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2017.05.060