Jaya Algorithm and Artificial Neural Network Based Approach for Object- Oriented Software Quality Analysis (original) (raw)
Related papers
Quality Prediction in Object Oriented System by Using ANN: A Brief Survey
At present quality of software systems is a major issue, still well defined criteria to measure it needs to be established. The object-oriented (OO) systems, which is different from procedural paradigm requires valid and effective metrics to assess quality of the software. There is considerable research interest in developing and applying sophisticated techniques to construct models for estimation. The different soft computing techniques such as Artificial Neural Networks (ANN), fuzzy inference systems and adaptive neuro-fuzzy inference systems are available for the prediction purpose. However among these techniques, ANN possesses the advantages of predicting software quality accurately and identifies the defects by efficient discovery mechanisms. This paper aims to survey various research methodologies proposed to predict quality of OO metrics by using neural network approach.
Maintainability assessment is an essential aspect of software development. However it is a cumbersome process. Many methodologies are proposed so far to estimate maintainability of software. Artificial neural network is one of the sophisticated techniques which have a great predictive capability. This paper investigated the application of neural networks to predict maintainability of the software using object-oriented metrics. In this study maintenance effort was chosen as the dependent variable and principal components of object-oriented metrics as the independent variables. Maintainability is predicted using Multi Layer Perceptron (MLP) neural network. The results of the study are also compared with other models and it is revealed that the presented model is more useful than the previous one.
Validating the Effectiveness of Object-Oriented Metrics for Predicting Maintainability
Procedia Computer Science, 2015
In this study, empirically investigates the relationship of existing class level object-oriented metrics with a quality parameter i.e., maintainability. Here, different subset of Object-Oriented software metrics have been considered to provide requisite input data to design the models for predicting maintainability using Neuro-Genetic algorithm (hybrid approach of neural network and genetic algorithm). This technique is applied to estimate maintainability on two different case studies such as Quality Evaluation System (QUES) and User Interface System (UIMS). The performance parameters of this technique are evaluated based on the basis of Mean absolute error (MAE), Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), and Standard Error of the Mean (SEM). The results reported that the identified subset metrics demonstrated an improved maintainability prediction with higher accuracy.
Software Quality Estimation through Object Oriented Design Metrics
IJCSNS, 2011
Software metrics are required to measure quality in terms of software performance and reliability related characteristics like dependencies, coupling and cohesion etc. It provides a way to measure the progress of code during development and having direct relationship with cost and time incurred in the software design and development at their later stages. These major issues must be checked and informed early in the development stage, so that reliability of any software product could be ensured for any large and complex software project. Object oriented software metrics directly focuses on the issues like complexity, reliability and robustness of the software developed using object oriented design methodologies. It reflects the time, cost and effort that would be incurred in development at later stage. While the software in its development stage, it is desirable that the complexity levels at every stage should be minimized to make the end product more reliable and manageable. Object oriented metrics provides all parameters through which one can estimate the complexities and quality related issues of any software at their early stages of development. In this paper, authors have studied three object oriented metrics namely MOOD Metrics, CK Metrics, and QMOOD Metrics and given a case study to show, how these metrics are useful in determining the quality of any software designed by using object oriented paradigm.
Estimation of Software Quality using Object Oriented Design Metrics
IJIRCCE, 2014
In software development industry the steps towards corrective actions for successful software development process comes too late resulting in ineffectiveness, late delivery, over budget and poor quality with reduced capabilities. An early estimation towards software post-release quality can be a useful remedy to maximize the business result by shortening the time and increasing the probability of project success. The development team is also a beneficiary of the software quality estimation technique as they get an early warning regarding the quality of their product. Software quality estimation has been proved to be one of the most upcoming as well as interesting research topics of the decade which aims to identify and minimize the error prone tasks to minimize the development cost. Traditional software metrics aims at the procedure-oriented development because of which it cannot fullfill the requirement of object-oriented software resulting in popularity of object-oriented design metrics in industrial software development environment as it helps in the development of higher quality products with low cost over their maintenance. Object-oriented metrics is capable of providing all the parameters to estimate the complexity and quality related issues at the early development stage of a software. In this paper we have studied and analyzed the object – oriented metrics namely MOOD Metrics, CK Metrics, and QMOOD Metrics and present the case study of how they are useful in determining the software quality developed implementing object-oriented paradigm.
EVALUATION OF METRICS AND ASSESSMENT OF QUALITY OF OBJECT ORIENTED SOFTWARE
Quality assessment of software is big issue for software development team The reason is variations of designed software in size and methodology. A huge number of metrics has designed to assess quality of software up to a level. In this paper we are discussing Object oriented metrics used to assess quality of software at design level as well as at code level. Although correct assessment of software quality is not possible but using Object oriented metrics quality can be assessed up to limit. The main focus of research is to assess software quality at design level because design level quality assessment effects coding, testing, maintaining phase of software development life cycle. First, for evaluating metrics of design level UML diagram is used as an input. A Java Parser is designed for parsing the XML code of UML diagram .Second, Quality of same software projects also assessed at code level using same formula as at the design level. At code level Eclipse with Metrics 1.3.6 is used for assessing quality. We observed that software quality at code level moves around CC (Cyclometer Complexity), LCOM (Lack Cohesion of Methods) and LOCM (Lines of Code of method).And we find out that for increasing quality of software, CC and LCOM and LOCM are low. For decreasing quality CC, LCOM and LOCL are high.
Improvement and Implementation of Software Quality by Using Software Metrics
Without the software development and software product knowledge it's very complicated to understand, keep away from improvement in the quality of software. There should be some dimension process to forecast the software development, and to appraise software products and its quality. In This paper provides a brief view on Software Metrics, Software Quality and Software Metrics techniques that will forecast and evaluate the specified superiority factors of software which will relate to quality. It additional discusses regarding the Quality as given through the principles like ISO, principal elements necessary for the Software Metrics and Software Quality as the measurement method to forecast the Quality in the Software. Java source code evolution are using for Software Metrics, like Defect Metrics, Size Metrics, and Complexity Metrics. Presented experiments are proving that, the software quality can be analyzed, observed, and enhanced through software metrics usage.
Adaptive Neuro-Fuzzy Inference System for Assessing the Maintainability of the Software
2017 Ninth International Conference on Advanced Computing (ICoAC), 2017
Measuring software maintainability at an earlier stage is a non-trivial task as it decides the software life cycle cost and customer satisfaction. Software designing is carried out using many object-oriented (OO) techniques. Among these, class modeling is one of the frequently used techniques. An enhanced Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to assess the maintainability of the software at the design level. For measuring the maintainability, the metrics derived from the UML class diagram are used. The metrics namely coupling, and size are used as inputs for the proposed ANFIS based model. The size metric represents the structural complexity of the code whereas the coupling metrics represent the degree of interdependence between the software modules. The membership functions and the neural network parameters are determined based on the low mean square error value. The performance of the ANFIS model is evaluated using Root Mean Squared Error (RMSE), Coefficient of determination (R2) and Adj R2 techniques. Also, the performance of the proposed model is compared with Artificial Neural Network (ANN) model and the classical Fuzzy Inference System (FIS) model. The outcome of the ANFIS model reveals that it results in better performance when compared with ANN and FIS techniques.
Metrics and Measure are closely interrelated to each other. Measure is defined as way of defining amount, dimension, capacity or size of some attribute of a product in quantitative manner while Metric is unit used for measuring attribute. Software quality is one of the major concerns that need to be addressed and measured. Object oriented (OO) systems require effective metrics to assess quality of software. The paper is designed to identify attributes and measures that can help in determining and affecting quality attributes. The paper conducts empirical study by taking public dataset KC1 from NASA project database. It is validated by applying statistical techniques like correlation analysis and regression analysis. After analysis of data, it is found that metrics SLOC, RFC, WMC and CBO are significant and treated as quality indicators while metrics DIT and NOC are not significant. The results produced from them throws significant impact on improving software quality.
Improvement of Object Oriented Design Quality Measurement Using Fuzzy Ahp
ISAHP proceedings, 2016
A new method for defining rank of quality over a number of object oriented software applications has been developed. The method is to interpret a number of metric values obtained from measuring object oriented properties of executable Java codes into a single quantitative value that represents its quality. OO metrics are treated as the multi criteria. Their values are converted to pairwise comparison matrix according to the AHP scheme. Fuzzy logic is applied to address the limitation of existing methods. It has been proved that Fuzzy-AHP method results in more accurate and more consistent judgment in defining relative quality compared to AHP.