Quality Metrics in Software Design: A Systematic Review

Hernandez-Gonzalez, Esmeralda Yamileth; Sánchez-García, Ángel J.; Cortés-Verdín, María Karen; Arriaga, Juan Carlos Pérez

doi:10.1109/conisoft.2019.00021

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…In [35], the design metrics found after performing a Systematic Literature Review (SLR) are presented. The authors mention that the metrics mainly measure the classes modeled in Unified Modeling Language (UML), with their respective attributes and methods, plus other aspects such as encapsulation, inheritance, polymorphism, cohesion, or coupling between classes were measured.…”

Section: Design Metricsmentioning

confidence: 99%

“…The authors mention that the metrics mainly measure the classes modeled in Unified Modeling Language (UML), with their respective attributes and methods, plus other aspects such as encapsulation, inheritance, polymorphism, cohesion, or coupling between classes were measured. The selected metrics used in this work are those identified in [35], which are a selection of the metrics of Chidamber and Kemerer, Lorenz and Kidd, Abreu and Melo, and unknown authors, as shown in Table 2.…”

Section: Design Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Software Estimation in the Design Stage with Statistical Models and Machine Learning: An Empirical Study

Sánchez-García,

González-Hernández,

Cortés-Verdín

et al. 2024

Mathematics

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Accurate estimation of software effort and time in the software development process is a key activity to achieve the necessary product quality. However, underestimation or overestimation of effort has become a key challenge for software development. One of the main problems is the estimation with metrics from late stages, because the product must already be finished to make estimates. In this paper, the use of statistical models and machine learning approaches for software estimation are used in early stages such as software design, and a data set is presented with metric values of design artifacts with 37 software projects. As results, models for the estimation of development time and effort are proposed and validated through leave-one-out cross-validation. Further, machine learning techniques were employed in order to compare software projects estimations. Through the statistical tests, it was proven that the errors were not statistically different with the regression models for effort estimation. However, with Random Forest the best statistical results were obtained for estimating development time.

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Section: Design Metricsmentioning

confidence: 99%

Section: Design Metricsmentioning

confidence: 99%