Entropy based Software Reliability Growth Modelling for Open Source Software Evolution

During Open Source Software (OSS) development, users submit "new features (NFs)", "feature improvements (IMPs)" and bugs to fix. A proportion of these issues get fixed before the next software release. During the introduction of NFs and IMPs, the source code files change. A proportion of these source code changes may result in generation of bugs. We have developed calendar time and entropy-dependent mathematical models to represent the growth of OSS based on the rate at which NFs are added, IMPs are added, and bugs introduction rate.The empirical validation has been conducted on five products, namely "Avro, Pig, Hive, jUDDI and Whirr" of the Apache open source project. We compared the proposed models with eminent reliability growth models, Goel and Okumoto (1979) and Yamada et al. (1983) and found that the proposed models exhibit better goodness of fit

Applying Neural Network Approach with Imperialist Competitive Algorithm for Software Reliability Prediction

Software systems exist in different critical domains. Software reliability assessment has become a critical issue due to the variety levels of software complexity. Software reliability, as a sub-branch of software quality, has been exploited to evaluate to what extend the desired software is trustable. To overcome the problem of dependency to human power and time limitation for software reliability prediction, researchers consider soft computing approaches such as Neural Network and Fuzzy Logic. These techniques suffer from some limitations including lack of analyzing mathematical foundations, local minima trapping and convergence problem. This study develops a novel model for software reliability prediction through the combination of Multi-Layer Perceptron Neural Network (MLP) and Imperialist Competitive Algorithm (ICA). The proposed model has solved some of the problems of existing methods such as convergence problem and demanding on huge number of data. This model can be used in complicated software systems. The results prove that both training and testing phases of this model outperform existing approaches in terms of predicting the number of software failures

Improving software reliability growth model selection ranking using particle swarm optimization

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. � 2005 - 2017 JATIT & LLS