Over the last three decades, many quality models have been proposed for software systems. These models employ software metrics to assess different quality attributes. However, without adequate thresholds, it is very hard to associate plausible interpretations with software quality attributes. Many attempts are reported in the literature to identify meaningful thresholds for software metrics. However, these attempts fail to clearly map the proposed thresholds to the assessment of software quality attributes. This paper aims at bridging this gap and provides a methodology for quality assessment models based on software metric thresholds. By doing so, software products can be easily ranked according to specific quality levels. Our methodology defines software metric thresholds to generate ordinal data. Then, the ordinal data is combined with a weighting scheme based on the Pearson correlation coefficient. The resulting weights are assigned to data categories in each software metric. Thanks to these weights, project quality levels are straightforwardly estimated. To assess the effectiveness of our software metric thresholding framework, we carry out an empirical study. The reported results clearly show that the proposed framework has a significant impact on the assessment and evaluation of the software product quality.
|Journal||Automated Software Engineering|
|State||Published - Nov 2022|
Bibliographical noteFunding Information:
M. Alqmase and M. Alshayeb would like to acknowledge the support provided by the Deanship of Scientific Research at King Fahd University of Petroleum and Minerals. L. Ghouti acknowledges the support of Prince Sultan University.
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
- Machine learning
- Metric thresholds
- Product maturity model
- Quality assessment
- Software metrics
ASJC Scopus subject areas