Object-Oriented Metrics in Practice: Using Software Metrics to Characterize, Evaluate, and Improve the Design of Object-Oriented Systems

Mann, C.J.H.

doi:10.1108/k.2007.06736eae.001

Cited by 23 publications

(67 citation statements)

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“…The software design has been enhanced over the past few years to make it highly efficient, effective, and reliable. It is highly essential to ensure reliability, stability, scalability, and cost-effectiveness while designing software, which has become one of the hottest areas in the research domain [4,5]. The cost-effective nature of the software can be ensured by the concept called reusability.…”

Section: Reduced Development Cost Increased Reliability Better Qos De...mentioning

confidence: 99%

Artificial Intelligence Model for Software Reusability Prediction System

Subha¹,

Haldorai²,

Ramu³

2023

Intelligent Automation &Amp; Soft Computing

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The most significant invention made in recent years to serve various applications is software. Developing a faultless software system requires the software system design to be resilient. To make the software design more efficient, it is essential to assess the reusability of the components used. This paper proposes a software reusability prediction model named Flexible Random Fit (FRF) based on aging resilience for a Service Net (SN) software system. The reusability prediction model is developed based on a multilevel optimization technique based on software characteristics such as cohesion, coupling, and complexity. Metrics are obtained from the SN software system, which is then subjected to min-max normalization to avoid any saturation during the learning process. The feature extraction process is made more feasible by enriching the data quality via outlier detection. The reusability of the classes is estimated based on a tool called Soft Audit. Software reusability can be predicted more effectively based on the proposed FRF-ANN (Flexible Random Fit -Artificial Neural Network) algorithm. Performance evaluation shows that the proposed algorithm outperforms all the other techniques, thus ensuring the optimization of software reusability based on aging resilient. The model is then tested using constraint-based testing techniques to make sure that it is perfect at optimizing and making predictions.

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Section: Reduced Development Cost Increased Reliability Better Qos De...mentioning

confidence: 99%

Artificial Intelligence Model for Software Reusability Prediction System

Subha¹,

Haldorai²,

Ramu³

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

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“…NOC: Number of classes. This measure and the following one can be used as indicators of how much effort shall be required to understand the source code of a framework (Lanza and Marinescu, 2006). The grater this value, the more difficult it is to understand a framework.…”

Section: Size Measuresmentioning

confidence: 99%

“…NOI: Number of interfaces. It is commonly agreed that the larger the number of interfaces, the easier to a adapt a framework (Lanza and Marinescu, 2006).…”

Section: Size Measuresmentioning

confidence: 99%

“…This has motivated many researchers to devise individual measures that are correlated to the effort required to maintain and adapt a piece of software (McCabe, 1976;Li and Henry, 1993;Chidamber and Kemerer, 1994;Henderson-Sellers, 1996;Briand et al, 1998;Sheldon et al, 2002;Martin, 2002;Bocco et al, 2005;Mouchawrab et al, 2005;Lanza and Marinescu, 2006;Lajios, 2009;Herraiz et al, 2009;Risi et al, 2013). Many of them have been validated in real-world projects (Lanza and Marinescu, 2006;Tempero et al, 2008;Balmas et al, 2009;Burger and Hummel, 2012;Mordal-Manet et al, 2013). Thus, these measures can be effectively used in practice to analyse the maintainability and adaptability of integration frameworks.…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we have managed to assemble a catalogue of twenty-five different measures in the literature that can help forecast how maintainable an integration framework is, and we also propose a statistically sound method to analyse the results. We have classified these measures into four categories based on the model proposed by Lanza and Marinescu (2006), namely: size, coupling, complexity, and inheritance. We also report on an analysis regarding Camel, Spring Integration, Mule, and Guaraná that was carried out in an industrial context.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A methodology to evaluate the maintainability of enterprise application integration frameworks

Frantz¹,

Corchuelo

Roos‐Frantz³

2015

IJWET

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Consulting companies that specialise in Enterprise Application Integration commonly require adapting existing frameworks to specific domains. Currently, there are many such frameworks available, most of which provide a materialisation of the well-known catalogue of patterns that was devised by Hohpe and Woolf. The decision regarding which framework must be used is critical since adaptation costs are not negligible. In this article, we report on a methodology that helps practitioners make a decision regarding which framework should be selected. To the best of our knowledge, there is not a previous methodology in the literature. Its salient features are that we have assembled a catalogue of measures regarding which there is a consensus in the literature that they are clearly aligned with the effort required to maintain a piece of software and we propose a statistically-sound method to produce a rank. We illustrate our proposal with an industrial case study that we have performed using five open-source frameworks.

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MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network

Zhang

Dong

2021

J Software Evolu Process

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Code smell is the structural design defect that makes programs difficult to understand, maintain, and evolve. Existing works of code smell detection mainly focus on prevalent code smells, such as feature envy, god class, and long method. Few works have been done on detecting brain class/method. Furthermore, existing deep‐learning‐based approaches leverage the CNN model to improve accuracy by barely increasing the number of layers, which may cause a problem of gradient degradation. To this end, this paper proposes a novel approach called MARS to detect brain class/method. MARS improves the gradient degradation by employing an improved residual network. It increases the weight value of those important code metrics to label smelly samples by introducing a metric–attention mechanism. To support the training of MARS, a dataset called BrainCode is generated by extracting more than 270,000 samples from 20 real‐world applications. MARS is evaluated on BrainCode and compared to other machine‐learning‐based and deep‐learning‐based approaches. The experimental results demonstrate that the average accuracy of MARS is 2.01 % higher than that of the existing approaches, which improves state‐of‐the‐art.

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Object-Oriented Metrics in Practice: Using Software Metrics to Characterize, Evaluate, and Improve the Design of Object-Oriented Systems

Cited by 23 publications

References 0 publications

Artificial Intelligence Model for Software Reusability Prediction System

Artificial Intelligence Model for Software Reusability Prediction System

A methodology to evaluate the maintainability of enterprise application integration frameworks

MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network

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