A Framework for Assessing the Software Reusability using Fuzzy Logic Approach for Aspect Oriented Software

Singh, Pradeep; Sangwan, Om Prakash; Singh, Amar Pal; Pratap, Amrendra

doi:10.5815/ijitcs.2015.02.02

Cited by 14 publications

(10 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their publication reveals the potential for the use of fuzzy logic. Singh et al determine four quality metrics-(1) separation of concerns, (2) coupling, (3) cohesion, and (4) size-to describe the reliability of aspect-oriented software systems while they use fuzzy modelling [102]. The linguistic rules were constructed on the basis of the opinions of experts.…”

Section: Adaptive Methods In Quality Modellingmentioning

confidence: 99%

Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

2021

View full text Add to dashboard Cite

Execution tracing is a tool used in the course of software development and software maintenance to identify the internal routes of execution and state changes while the software operates. Its quality has a high influence on the duration of the analysis required to locate software faults. Nevertheless, execution tracing quality has not been described by a quality model, which is an impediment while measuring software product quality. In addition, such a model needs to consider uncertainty, as the underlying factors involve human analysis and assessment. The goal of this study is to address both issues and to fill the gap by defining a quality model for execution tracing. The data collection was conducted on a defined study population with the inclusion of software professionals to consider their accumulated experiences; moreover, the data were processed by genetic algorithms to identify the linguistic rules of a fuzzy inference system. The linguistic rules constitute a human-interpretable rule set that offers further insights into the problem domain. The study found that the quality properties accuracy, design and implementation have the strongest impact on the quality of execution tracing, while the property legibility is necessary but not completely inevitable. Furthermore, the quality property security shows adverse effects on the quality of execution tracing, but its presence is required to some extent to avoid leaking information and to satisfy legal expectations. The created model is able to describe execution tracing quality appropriately. In future work, the researchers plan to link the constructed quality model to overall software product quality frameworks to consider execution tracing quality with regard to software product quality as a whole. In addition, the simplification of the mathematically complex model is also planned to ensure an easy-to-tailor approach to specific application domains.

show abstract

Section: Adaptive Methods In Quality Modellingmentioning

confidence: 99%

Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

2021

View full text Add to dashboard Cite

show abstract

“…An extensive literature review has been conducted on software metrics prediction in order to develop the proposed model. Authors in [16] studied an evolution of the aspect oriented programming which is followed by the software reusability analysis using fuzzy logic technique by taking four metrics of a software namely SOC, size, coupling and cohesion.…”

Section: Related Workmentioning

confidence: 99%

A Software Quality Prediction Model for Aspect Oriented System using Neuro-Fuzzy Approach

2022

IJSEM

View full text Add to dashboard Cite

Nowadays, the usage of software has increased exponentially in various fields like education systems, industries, health systems, and many others. Various software architectures are already available in the market e.g. modular oriented, component-based, object-oriented, aspect-oriented, etc. Aspect-Oriented (AO) system software has gained much attention due to its superior features to the aforementioned systems. However, AO systems face the challenges of being complex and hard testing environment, a quality assessment of these systems is necessary. In this paper, a software quality estimation model for aspect oriented system using neuro-fuzzy approach has been developed. For which, a detailed study on aspect oriented systems has been accomplished in terms of various attributes affecting the quality of these software. In this paper, a framework of software quality prediction model has been designed using the adaptive neuro-fuzzy inference engine (ANFIS) approach. Data of 200 software pieces have been collected in this study where 150 software data is used to train the ANFIS model whereas 50 software data is used for testing purposes. The quality estimated by the proposed ANFIS model is compared with the actual quality of these software data and quantitative analysis is performed in terms of error measures. Finally, it was found that the proposed ANFIS model worked better in terms of MSE, MRE, MARE, MBRE, and MIBRE error measures.

show abstract

“…[1] Aspect-oriented approach makes the classification of simple programs, so that relieve concerns won't be more complex design and improve understanding of the system. [2] Aspect-oriented produce more quality products and more efficient, and to add more capabilities to the existing system. Aspect oriented programming, all the data, methods, and classes related to a concern within one one aspect of the module and the system is optimal.…”

Section: Introductionmentioning

confidence: 99%

Comparison of ANFIS with MLP ANN in Measuring the Reliability based on Aspect Oriented Software

Zavvar¹,

Ramezani²

2015

IJMECS

View full text Add to dashboard Cite

In fact, Reliability as the qualities metric is the probability success or The probability that a system or set of tasks without failure for a specified constraints of time and space, as specified in the design and operating conditions specified temperature, humidity, vibration and action. A relatively new methodologies for developing complex software systems engineering is an aspectoriented software systems, that provides the new methods for the separation of concerns multiple module configuration or intervention and automatic integration them with a system. In this paper, using MLP artificial neural networks and adaptive fuzzy neural network assess the reliability of the aspect oriented software and at the end, two methods were compared with each other. After examination, the root means square error method based on artificial neural networks, fuzzy neural network-based method of 0.024262 and 0.021874 to be adaptive. The results show that the method is based on adaptive fuzzy neural networks with low error in the estimation of reliability, performance is better than the MLP artificial neural network approach.

show abstract

A Framework for Assessing the Software Reusability using Fuzzy Logic Approach for Aspect Oriented Software

Cited by 14 publications

References 28 publications

Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

A Software Quality Prediction Model for Aspect Oriented System using Neuro-Fuzzy Approach

Comparison of ANFIS with MLP ANN in Measuring the Reliability based on Aspect Oriented Software

Contact Info

Product

Resources

About