Software quality prediction using random particle swarm optimization (PSO) (original) (raw)
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2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), 2016
This paper provides mathematical implications of software quality prediction using different software metrics and the optimized values are obtained by particle swarm optimization (PSO). Then the main focus of this research work is to find the final optimum score based on the previous software metrics used. Then we apply PSO for checking the optimized value based on the results obtained by the Fmeasure(FM), Odd ratio(OR) and Power(PO). We have applied random particle so that fixed biasness is removed through this algorithm. The algorithm used in our approach is shown below. The results are convincing and uniform in different iteration.
Improving software reliability growth model selection ranking using particle swarm optimization
Journal of theoretical and applied information technology, 2017
Reliability of software always related to software failures and a number of software reliability growth models (SRGMs) have been proposed past few decades to predict software reliability. Different characteristics of SRGM leading to the study and practices of SRGM selection for different domains. Appropriate model must be chosen for suitable domain in order to predict the occurrence of the software failures accurately then help to estimate the overall cost of the project and delivery time. In this paper, particle swarm optimization (PSO) method is used to optimize a parameter estimation and distance based approach (DBA) is used to produce SRGM model selection ranking. The study concluded that the use of PSO for optimizing the SRGM's parameter has provided more accurate reliability prediction and improved model selection rankings. The model selection ranking methodology can facilitate a software developer to concentrate and analyze in making a decision to select suitable SRGM during testing phases.
Information and Software Technology, 2011
Context: Assessing software quality at the early stages of the design and development process is very difficult since most of the software quality characteristics are not directly measurable. Nonetheless, they can be derived from other measurable attributes. For this purpose, software quality prediction models have been extensively used. However, building accurate prediction models is hard due to the lack of data in the domain of software engineering. As a result, the prediction models built on one data set show a significant deterioration of their accuracy when they are used to classify new, unseen data. Objective: The objective of this paper is to present an approach that optimizes the accuracy of software quality predictive models when used to classify new data. Method: This paper presents an adaptive approach that takes already built predictive models and adapts them (one at a time) to new data. We use an ant colony optimization algorithm in the adaptation process. The approach is validated on stability of classes in object-oriented software systems and can easily be used for any other software quality characteristic. It can also be easily extended to work with software quality predictive problems involving more than two classification labels. Results: Results show that our approach out-performs the machine learning algorithm C4.5 as well as random guessing. It also preserves the expressiveness of the models which provide not only the classification label but also guidelines to attain it. Conclusion: Our approach is an adaptive one that can be seen as taking predictive models that have already been built from common domain data and adapting them to context-specific data. This is suitable for the domain of software quality since the data is very scarce and hence predictive models built from one data set is hard to generalize and reuse on new data.
A Novel Technique of Optimization for Software Metric Using PSO.pdf
Software Metrics present the key role of Software Development. Cost, Productivity and Quality are specific area of measurement in software field. The uncertainties which a bugs of effort estimation, researchers used the optimization to reduce it. Software cost estimation optimize, on basis of existing data set, in this paper we emphasize on COCOMO model with NASA18 data set. Software Effort Cost estimation is the process for measurement precisely the amount of effort required to complete the project. Regression rigorous method for estimation and Particle Swarm Optimization (PSO) is the austere method to work on the cost effort of software metricsin the modern era.
Reliability modeling using Particle Swarm Optimization
Software quality includes many attributes including reliability of a software. Prediction of reliability of a software in early phases of software development will enable software practitioners in developing robust and fault tolerant systems. The purpose of this paper is to predict software reliability, by estimating the parameters of Software Reliability Growth Models (SRGMs). SRGMs are the mathematical models which generally reflect the properties of the process of fault detection during testing. Particle Swarm Optimization (PSO) has been applied to several optimization problems and has showed good performance. PSO is a popular machine learning algorithm under the category of Swarm Intelligence. PSO is an evolutionary algorithm like Genetic Algorithm (GA). In this paper we propose the use of PSO algorithm to the SRGM parameter estimation problem, and then compare the results with those of GA. The results are validated using data obtained from 16 projects. The results obtained from PSO have high predictive ability which is reflected by low error predictions. The results obtained using PSO are better than those obtained from GA. Hence, PSO may be used to estimate SRGM parameters.
Predicting Fault Proneness of Classes Trough a Multiobjective Particle Swarm Optimization Algorithm
2008
Software testing is a fundamental software engineering activity for quality assurance that is also traditionally very expensive. To reduce efforts of testing strategies, some design metrics have been used to predict the fault-proneness of a software class or module. Recent works have explored the use of machine learning (ML) techniques for fault prediction. However most used ML techniques can not deal with unbalanced data and their results usually have a difficult interpretation. Because of this, this paper introduces a multi-objective particle swarm optimization (MOPSO) algorithm for fault prediction. It allows the creation of classifiers composed by rules with specific properties by exploring Pareto dominance concepts. These rules are more intuitive and easier to understand because they can be interpreted independently one of each other. Furthermore, an experiment using the approach is presented and the results are compared to the other techniques explored in the area.
International Journal of Intelligent Engineering and Systems, 2018
This paper develops a technique by using Jaya algorithm and feed-forward neural network to determine the quality of object-oriented software by using Chidamber & Kemerer (CK) along with Li & Henry metrics. The technique basically focuses on the maintainability factor of software quality which in turn depends upon the software complexity. The software complexity is directly proportional to the number of changes done per class which is determined by the technique. The analysis has been done on UIMS (User Interface Management System) and QUES (Quality Evaluation System) datasets by using the mean absolute error as the analysis parameter. The reduction in the mean absolute error as compared to the existing state of art techniques along with the individual component of proposed technique proves the significance of the technique.
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.
International Journal of Software Engineering and Its Applications
The costs of finding and correcting software defects have been the most expensive activity in software development. The accurate prediction of defect prone software modules can help the software testing effort, reduce costs, and improve the software testing process by focusing on fault-prone module. Recently, static code attributes are used as defect predictors in software defect prediction research, since they are useful, generalizable, easy to use, and widely used. However, two common aspects of data quality that can affect performance of software defect prediction are class imbalance and noisy attributes. In this research, we propose the combination of particle swarm optimization and bagging technique for improving the accuracy of the software defect prediction. Particle swarm optimization is applied to deal with the feature selection, and bagging technique is employed to deal with the class imbalance problem. The proposed method is evaluated using the data sets from NASA metric data repository. Results have indicated that the proposed method makes an impressive improvement in prediction performance for most classifiers.