User-perceived reusability estimation based on analysis of software repositories

Papamichail, Michail D.; Diamantopoulos, Themistoklis; Chrysovergis, Ilias; Samlidis, Philippos; Symeonidis, Andreas L.

doi:10.1109/maltesque.2018.8368459

Cited by 11 publications

(8 citation statements)

References 12 publications

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“…Random Forest is a supervised learning algorithm used in both classification and regression but is most commonly used in classification [8].…”

Section: A Random Forest (Rf)mentioning

confidence: 99%

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Software Reusability Estimation based on Dynamic Metrics using Soft Computing Techniques

Duhan¹,

Bhatia²

2022

IJC

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Dynamic metrics capture the run time features of object-oriented languages, i.e., runtime polymorphism, dynamic binding, etc., that are not covered by static metrics. Therefore, in this paper, we derived a new approach to measuring the software reusability of a design pattern based on dynamic metrics. To achieve this, the authors proposed a model based on five parameters, i.e., polymorphism, inheritance, number of children, coupling, and complexity, to measure the reusability factor by using various soft computing techniques, i.e., Fuzzy, Neural Network, and Neuro-Fuzzy. Further, we also compared the proposed model with four existing machine learning algorithms. Lastly, we found that the proposed model using the neuro-fuzzy technique is trained well and predicts well with MAE (Mean absolute error) 0.003 and RMSE (Root mean square error) 0.009 based on dynamic metrics. Hence, it is concluded that dynamic metrics are a better predictor of the reusability factor than static metrics.

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“…Random Forest is a supervised learning algorithm used in both classification and regression but is most commonly used in classification [8].…”

Section: A Random Forest (Rf)mentioning

confidence: 99%

“…Therefore, this study evaluates the reusability of object-oriented software systems using the fuzzy, neural network, and neuro-fuzzy (NF) approach [7]. Lastly, we have also used four famous existing machine learning algorithms [8,9] that are described below.…”

Section: Introductionmentioning

confidence: 99%

Software Reusability Estimation based on Dynamic Metrics using Soft Computing Techniques

Duhan¹,

Bhatia²

2022

IJC

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“…Other techniques to prioritize and select the most suitable candidate, besides interoperability (Tran et al, 1997), is to take into account the functional and non-functional requirements (Tran et al, 1997) of the specific domain. By evaluating reusable components through metrics that indicate the acceptance of a component by the developers (Papamichail et al, 2018), we can effectively estimate the reusability, leading to a more suitable selection prior to integrating it. (Fremantle, 2015).…”

Section: Software Reusementioning

confidence: 99%

Examining the reuse potentials of IoT application frameworks

Smiari

Bibi

Feitosa

2020

Journal of Systems and Software

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“…The assessment depends onwhether the values of the metrics exceed certain thresholds,as defined in current literature. When several thresholds areexceeded, the returned reusability score is lower (Papamichail, et al, 2018). In (Papamichail et al, 2018), a reusability score has been formulated by employing information from GitHub stars and forks, which denote the extent to which software components are adopted by developers.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Optimization Driven RideNN for Software Reusability Estimation

Vamkudoth¹,

P²

2020

Journal of Engineering Research is an international, peer-revie

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Measuring the software reusability has become a prime concern in maintaining the quality of the software. Several techniques use software related metrics and measure the reusability factor of the software, but still face a lot of challenges. This work develops the software reusability estimation model for efficiently measuring the quality of the software components over time. Here, the Rider based Neural Network has been used along with the hybrid optimization algorithm for defining the reusability factor. Initially, nine software related metrics are extracted from the software. Then, a holoentropy based log function identifies the Measuring the software reusability has become a prime concern in maintaining the quality of the software. Several techniques use software related metrics and measure the reusability factor of the software, but still face a lot of challenges. This work develops the software reusability estimation model for efficiently measuring the quality of the software components over time. Here, the Rider based Neural Network has been used along with the hybrid optimization algorithm for defining the reusability factor. Initially, nine software related metrics are extracted from the software. Then, a holoentropy based log function identifies the normalized metric function and provides it to the proposed Cat Swarm Rider Optimization based Neural Network (C-RideNN) algorithm for the software reusability estimation. The proposed C-RideNN algorithm uses the existing Cat Swarm Optimization (CSO) along with the Rider Neural Network (RideNN) for the training purpose. Experimentation results of the proposed C-RideNN are evaluated based on metrics, such as Magnitude of Absolute Error (MAE), Mean Magnitude of the Relative Error (MMRE), and Standard Error of the Mean (SEM). The simulation results reveal that the proposed C-RideNN algorithm has improved performance with 0.0570 as MAE, 0.0145 as MMRE, and 0.6133 as SEM.

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User-perceived reusability estimation based on analysis of software repositories

Cited by 11 publications

References 12 publications

Software Reusability Estimation based on Dynamic Metrics using Soft Computing Techniques

Software Reusability Estimation based on Dynamic Metrics using Soft Computing Techniques

Examining the reuse potentials of IoT application frameworks

Hybrid Optimization Driven RideNN for Software Reusability Estimation

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