Software Reliability Prediction Model Research Articles

Application of Nonlinear Big Data Analysis Techniques in Computer Software Reliability Prediction

This research addresses the difficulties of underfitting, overfitting, and convergence to local minima in artificial neural networks for software dependability prediction. The work specifically focuses on enhancing the performance of the conventional PSO-SVM model for software reliability prediction. The analysis of the conventional PSO-SVM model and the special features of software reliability prediction serve as the foundation. An improved PSO-SVM software reliability prediction model is developed and the PSO-SVM model and a Backpropagation (BP) prediction model are compared experimentally. The critical metrics assessed include training error, and efficiency. The experimental results reveal that the training error of the enhanced PSO-LSSVM prediction model diminishes rapidly, levelling off after approximately 200 training generations. The BP prediction model requires 1,733 generations to meet training requirements. Furthermore, the improved PSO-LSSVM prediction model demonstrates significantly higher training efficiency than the BP prediction model. The optimized prediction model exhibits superior adaptability to small sample sizes, swift training, and high prediction accuracy, making it a more suitable choice for software reliability prediction applications.

Development of Pi Sigma Neural Network Model for the Prediction of Software Reliability Using 5 NASA Failure Datasets

Software reliability models are usually used to model the failure of software systems and prediction of its reliability potential. These models are however plagued with less accuracy, efficiency, and resource-effectiveness. Some soft computing methods have not yet been implemented to investigate their effectiveness and robustness for software fault prediction. Pi Sigma Neural Network (PSNN) software reliability prediction model was developed in this study for a better understanding of the modelling of software systems defects and reliability validated on 5 NASA promise datasets after carrying out data analysis using Seaborn on Python, working with raw data, pre-processed data with min-max normalization, Synthetic Minority Oversampling Technique (SMOTE) to overcome class imbalance problem between defective and non-defective modules, and then correlational analysis with varying thresholds (0.8, 0.85, 0.9 and 0.95) to reduce noise and get key features. The results obtained using the PSNN model showed for all the datasets good average performance for recall being highest at 79.8% based on no threshold, precision at 76.2% on 0.9 threshold, f1-score with 75.6% on 0.95 threshold and accuracy at 74.8% with the same 0.95 threshold. A model based on recall is good at fault finding. Modifying the structure and architecture of the PSNN, like using a voting ensemble algorithm of varied combinations of PSNNs and using a firefly algorithm to optimize in the future, will improve the Neural Network technique

A Comparative Analysis of SVM and ELM Classification on Software Reliability Prediction Model

By creating an effective prediction model, software defect prediction seeks to predict potential flaws in new software modules in advance. However, unnecessary and duplicated features can degrade the model’s performance. Furthermore, past research has primarily used standard machine learning techniques for fault prediction, and the accuracy of the predictions has not been satisfactory. Extreme learning machines (ELM) and support vector machines (SVM) have been demonstrated to be viable in a variety of fields, although their usage in software dependability prediction is still uncommon. We present an SVM and ELM-based algorithm for software reliability prediction in this research, and we investigate factors that influence prediction accuracy. These worries incorporate, first, whether all previous disappointment information ought to be utilized and second, which type of disappointment information is more fitting for expectation precision. In this article, we also examine the accuracy and time of SVM and ELM-based software dependability prediction models. Then, after the comparison, we receive experimental results that demonstrate that the ELM-based reliability prediction model may achieve higher prediction accuracy with other parameters, such as specificity, recall, precision, and F1-measure. In this article, we also propose a model for how feature selection utilization with ELM and SVM. For testing, we used NASA Metrics datasets. Further, in both technologies, we are implementing feature selection techniques to get the best result in our experiment. Due to the imbalance in our dataset, we initially applied the resampling method before implementing feature selection techniques to obtain the highest accuracy.

A Neuro-Fuzzy Hybridized Approach for Software Reliability Prediction

Context: Reliability prediction is critical for software engineers in the current challenging scenario of increased demand for high-quality software. Even though various software reliability prediction models have been established so far, there is always a need for a more accurate model in today's competitive environment for producing high-quality software. Objective: This paper proposes a neuro-fuzzy hybridized method by integrating self-organized- map (SOM) and fuzzy time series (FTS) forecasting for the reliability prediction of a software system. Methodology: In the proposed approach, a well-known supervised clustering algorithm SOM is incorporated with FTS forecasting for developing a hybrid model for software reliability prediction. To validate the proposed approach, an experimental study is done by applying proposed neuro-fuzzy method on a software failure dataset. In addition, a comparative study was conducted for evaluating the performance of the proposed method by comparing it with some of the existing FTS models. Results: Experimental outcomes show that the proposed approach performs better than the existing FTS models. Conclusion: The results show that the proposed approach can be used efficiently in the software industry for software reliability prediction.

SOM-FTS: A Hybrid Model for Software Reliability Prediction and MCDM-Based Evaluation

The objective of this study is to propose a hybrid model based on self-organized maps (SOM) and fuzzy time series (FTS) for predicting the reliability of software systems. The proposed SOM-FTS model is compared with eleven traditional machine learning-based models. The problem of selecting a suitable software reliability prediction model is represented as a multi-criteria decision-making (MCDM) problem. Twelve software reliability prediction models, including the proposed SOM-FTS model, are evaluated using three MCDM methods, four performance measures, and three software failure datasets. The results show that the proposed SOM-FTS model is the most suitable model among the twelve software reliability prediction models on the basis of MCDM ranking.

A Comprehensive Fault Prediction Model for Improving Software Reliability

We present a Comprehensive Fault Prediction Model (FPM) for Software Reliability in this article, which can estimate the greatest quantity of faults in a software. The proposed model implemented on a “Non-Homogeneous Poisson Process Model (NHPPM)” and includes fault reliant identification, rate of failure, and the software defect present after release. We looked at programmers' abilities, software performance, and flawless debugging as deciding factors for FPM. The Software Engineering Team Assessment and Prediction (SETAP) dataset is used for analysis of proposed FPM. The selected dataset is composed of sequential value which are linearly arranged over a given time duration. The attributes are analyzed to establish software reliability prediction model and comparison of proposed model is carried out with similar algorithms. The proposed FPM is executed in “Jira” and is compared with the present FPMs proposed in the literature.Results demonstrate comparatively less cumulative faults, and reduced residual errors which depicts high prediction accuracy and improved software reliability.

Software Reliability Prediction by using Deep Learning Technique

The importance of software systems and their impact on all sectors of society is undeniable. Furthermore, it is increasing every day as more services get digitized. This necessitates the need for evolution of development and quality processes to deliver reliable software. For reliable software, one of the important criteria is that it should be fault-free. Reliability models are designed to evaluate software reliability and predict faults. Software reliability prediction is always an area of interest in the field of software engineering. Prediction of software reliability can be done using numerous available models but with the inception of computational intelligence techniques, researchers are exploring new techniques such as machine learning, genetic algorithm, deep learning, etc. to develop better prediction models. In the current study, a software reliability prediction model is developed using a deep learning technique over twelve real datasets from different repositories. The results of the proposed model are analyzed and found quite encouraging. The results are also compared with previous studies based on various performance metrics.

ENHANCED ALGORITHM OF ARTIFICIAL BEE COLONY (ABC) TO OPTIMIZE MODELS OF SYSTEM RELIABILITY

In recent times, computer software applications are increasingly becoming an essential basis in several multipurpose domains including medicine, engineering, transportation etc. Consequently, with such wide implementation of software, the imperative need of ensuring certain software quality physiognomies such as efficiency, reliability and stability has ascended. To measure such software quality features, we have to wait until the software is executed, tested and put to use for a certain period of time. Numerous software metrics are presented in this study to circumvent this long and expensive process, and they proved to be awesome method of estimating software reliability models. For this purpose, software reliability prediction models are built. These are used to establish a relationship between internal sub-characteristics such asinheritance, coupling, size, etc. and external software quality attributes such as maintainability, stability, etc. Usingsuchrelationships, one canbuildamodelinordertoestimatethereliabilityofnewsoftware system.Suchmodelsaremainlyconstructedbyeitherstatisticaltechniquessuchasregression,or machine learningtechniquessuchasC4.5andneuralnetworks.The prototype presented isinvigoratedemployingprocedures of machine learninginparticularrule-basedmodels.Thesehaveawhite-boxnaturewhich accordsthecataloguingandmakingthemgood-looktoexpertsinthedomain. In this paper, wesuggest a powerfulinnovative heuristic based on Artificial Bee Colony (ABC) to enhance rule-based software reliability prediction models. The presented approach is authenticated on data describing reliability of classes in an Object-Oriented system. We compare our models to others constructed using other well-established techniques such as C4.5, Genetic Algorithms (GA), Simulated Annealing (SA), Tabu Search (TS), multi-layer perceptron with back-propagation,multi-lay perceptron hybridized with ABC and the majority classifier. Results show that, in most cases, the propose technique out- performs the others in different aspects.

Evaluating the Impact of Prediction Techniques: Software Reliability Perspective

Maintaining software reliability is the key idea for conducting quality research. This can be done by having less complex applications. While developers and other experts have made significant efforts in this context, the level of reliability is not the same as it should be. Therefore, further research into the most detailed mechanisms for evaluating and increasing software reliability is essential. A significant aspect of growing the degree of reliable applications is the quantitative assessment of reliability. There are multiple statistical as well as soft computing methods available in literature for predicting reliability of software. However, none of these mechanisms are useful for all kinds of failure datasets and applications. Hence finding the most optimal model for reliability prediction is an important concern. This paper suggests a novel method to substantially pick the best model of reliability prediction. This method is the combination of analytic hierarchy method (AHP), hesitant fuzzy (HF) sets and technique for order of preference by similarity to ideal solution (TOPSIS). In addition, using the different iterations of the process, procedural sensitivity was also performed to validate the findings. The findings of the software reliability prediction models prioritization will help the developers to estimate reliability prediction based on the software type.

Failure prediction by regularized fuzzy learning with intelligent parameters selection

Software failure time prediction, which is critical in software dependable evaluation, is among one of the most critical issues in software quality assurance research. Conventional software reliability predictions often use single models to illustrate the overall characteristic of the process of software failure. However, those models often fail to produce good prediction performance owing to the constant changes in software failure patterns during different time periods. Fuzzy systems like Takagi–Sugeno–Kang are multimodel approaches that combine simple submodels to represent the overall characteristic of dynamic nonlinear complex systems. This paper presents a novel Fuzzy system based software reliability prediction model, called regularized extreme learning adaptive neuro-fuzzy inference system, and employs the quantum-inspired binary gravitational search algorithm to determine its suitable regularization parameters. The new model can effectively reduce randomness, computational complexity as well as avoid local optimization when searching for optimal parameters. An experimentation using four real-world software failure data sets was carried out to compare the performance of the proposed model with that of certain representative software reliability prediction models. The results showed that the proposed model with the optimized parameters exhibits superior performance.

Software reliability prediction model with realistic assumption using time series (S)ARIMA model

Software reliability is the important attribute for complex computing systems to provide reliability could cause series issues such as extra cost, development delay and image of the software solution providers. Hence, ensuring the reliability of software before deliver to the customer is essential part for the company. Finding the error in right time with reasonable degree of accuracy helps to prevent the consequences. Several software reliability growth models developed and used to measure the trustworthiness based on development and testing phases with unrealistic assumption over the environment and applied Block box methodologies while constructing model. This paper presents well established statistical time series (S)ARIMA approach for developing a forecasting model that able to provide significantly improved reliability prediction. Using real time publicly available software failure sets, the prediction of proposed model is developed and compared with previously available reliability models.

Software Matrices Selection for a SDLC Based Software Reliability Prediction Model

Software reliability is one of the essential factors of quality in software engineering like other quality attributes as functionality, usability, maintainability, performance, serviceability, documentation etc. From last few years, several software reliability models have been developed. There is lack of relevant literature which focuses on processes related to SDLC. A SDLC based structure for measurement of reliability has been proposed. Identified software reliability measures which are majorly take place in all levels of early software development phase of SDLC. Considering all measures for reliability estimation will be costly and time taking. So measures are identified which are taking place at each development phase and have high synthetic weight according to selecting criteria based on expert judgment and multi criteria decision making technique. Based on the grading, top ranked measures like completeness, error distribution, fault density etc are identified. Use of recommended metrics will make software reliability estimation more effective and reliable

The world changes

This issue is the first issue of STVR since Jeff Offutt stood down as joint Editor-in-Chief. Jeff was Editor-in-Chief and then joint Editor-in-Chief from 2007 to 2019. This has been an interesting and challenging period for academic journals, with many changes to how journals operate. During this period, Jeff significantly strengthened the journal and has been at the forefront of developments within the field. As Jeff mentioned in the last editorial, there are still many challenges, but Jeff has left STVR in an excellent position. I also hope that we will soon start receiving his papers again! On a personal note, I was honoured when Jeff invited me to be joint EiC, and I welcomed the opportunity to work with him. I accepted immediately and have never regretted the decision. It has been a pleasure to work with Jeff: not only is he a major figure in the field, he is also charming and humble. Jeff also genuinely cares about the discipline and those working within it. As an example, he has spent a significant amount of time providing invaluable advice to junior researchers and students. Although I was sad to see Jeff step down, I am really pleased to be able to welcome Professor Tao Xie as joint Editor-in-Chief of STVR. I first met Tao at a conference (some years ago), and I was immediately impressed by him as a person but also by the work of his that we discussed. This work addressed a real problem in a practical manner but was also based on sound intuition and theory. He has since become a leading figure in the field. Jeff and I were convinced that Tao was the right person to take over and were delighted when he accepted our invitation. Tao has already shown great energy and enthusiasm, and in his upcoming editorial, he will say a little more about some of the ideas of his that we will be implementing. This issue contains two papers. Our first paper is “An Improved Software Reliability Prediction Model by Using High Precision Error Iterative Analysis Method” by Gul Jabeen, Ping Luo and Wasif Afzal (recommended by Min Xie). This concerns a classical problem, which is that of trying to model how the reliability of a piece of software will change during development. Techniques that achieve this can, for example, be used to help plan the software release. The authors use neural networks to help model the error and so make a model more accurate. One of the benefits of the approach is that it can potentially be used with any parametric software reliability prediction model. The second paper is “An End-user-centric Test Generation Methodology for Performance Evaluation of Mobile Networked Applications” by Mustafa Al-tekreeti, Atef Abdrabou and Kshirasagar Naik (recommended by Jane Hayes). The paper concerns the testing of mobile network applications, with a focus on performance (waiting time delay) and its impact on user experience. The authors introduce new test coverage criteria associated with two types of delays and also use metamorphic testing to address the oracle problem.

An improved software reliability prediction model by using high precision error iterative analysis method

SummarySoftware reliability deals with the probability that software will not cause the failure of a system in a specified time interval. Software reliability growth models (SRGMs) are used to predict future behaviour from known characteristics of software, like historical failures. With the increasing demand to deliver quality software, more accurate SRGMs are required to estimate the software release time and cost of the testing effort. Software failure predictions at early phases also provide an opportunity for investing in proper quality assurance and upfront resource planning. Up till now, many parametric software reliability growth models (PSRGMs) have been proposed. However, several limitations of them mean that their predictive capacities differ from one dataset to others. In this paper, to enhance the prediction accuracy of existing PSRGMs, a high precision error iterative analysis method (HPEIAM) has been proposed based on the residual errors. In HPEIAM, residual errors from the estimated results of SRGMs are considered as another source of data that can combine the residual error modification with artificial neural network sign estimator. The repeated computation of residual errors by SRGMs improves and corrects the prediction accuracy up to the expected level. The performance of HPEIAM is tested with several PSRGMs using two sets of real software failure data based on three performance criteria. Moreover, we have compared the estimated failures predicted by HPEIAM with genetic algorithm (GA)‐based prediction improvement. The results demonstrate that HPEIAM gives an improvement in goodness‐of‐fit and predictive performance for every PSRGM in initial few iterations.

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.

A Brief Review of Software Reliability Prediction Models

A Brief Review of Software Reliability Prediction Models

Towards early software reliability prediction for computer forensic tools (case study).

Versatility, flexibility and robustness are essential requirements for software forensic tools. Researchers and practitioners need to put more effort into assessing this type of tool. A Markov model is a robust means for analyzing and anticipating the functioning of an advanced component based system. It is used, for instance, to analyze the reliability of the state machines of real time reactive systems. This research extends the architecture-based software reliability prediction model for computer forensic tools, which is based on Markov chains and COSMIC-FFP. Basically, every part of the computer forensic tool is linked to a discrete time Markov chain. If this can be done, then a probabilistic analysis by Markov chains can be performed to analyze the reliability of the components and of the whole tool. The purposes of the proposed reliability assessment method are to evaluate the tool’s reliability in the early phases of its development, to improve the reliability assessment process for large computer forensic tools over time, and to compare alternative tool designs. The reliability analysis can assist designers in choosing the most reliable topology for the components, which can maximize the reliability of the tool and meet the expected reliability level specified by the end-user. The approach of assessing component-based tool reliability in the COSMIC-FFP context is illustrated with the Forensic Toolkit Imager case study.

Software reliability prediction via relevance vector regression

The aim of software reliability prediction is to estimate future occurrences of software failures to aid in maintenance and replacement. Relevance vector machines (RVMs) are kernel-based learning methods that have been successfully adopted for regression problems. However, they have not been widely explored for use in reliability applications. This study employs a RVM-based model for software reliability prediction so as to capture the inner correlation between software failure time data and the nearest m failure time data. We present a comparative analysis in order to evaluate the RVMs effectiveness in forecasting time-to-failure for software products. In addition, we use the Mann-Kendall test method to explore the trend of predictive accuracy as m varies. The reasonable value range of m is achieved through paired T-tests in 10 frequently used failure datasets from real software projects.

Neuro-genetic approach on logistic model based software reliability prediction

In this paper, we propose a multi-layer feedforward artificial neural network (ANN) based logistic growth curve model (LGCM) for software reliability estimation and prediction. We develop the ANN by designing different activation functions for the hidden layer neurons of the network. We explain the ANN from the mathematical viewpoint of logistic growth curve modeling for software reliability. We also propose a neuro-genetic approach for the ANN based LGCM by optimizing the weights of the network using proposed genetic algorithm (GA). We first train the ANN using back-propagation algorithm (BPA) to predict software reliability. After that, we use the proposed GA to train the ANN by globally optimizing the weights of the network. The proposed ANN based LGCM is compared with the traditional Non-homogeneous Poisson process (NHPP) based software reliability growth models (SRGMs) and ANN based software reliability models. We present the comparison between the two training algorithms when they are applied to train the proposed ANN to predict software reliability. The applicability of the different approaches is explained through three real software failure data sets. Experimental results demonstrate that the proposed ANN based LGCM has better fitting and predictive capability than the other NHPP and ANN based software reliability models. It is also noted that when the proposed GA is employed as the learning algorithm to the ANN, the proposed ANN based LGCM gives more fitting and prediction accuracy i.e. the proposed neuro-genetic approach to the LGCM provides utmost predictive validity. Proposed model can be applied during software testing time to get better software reliability estimation and prediction than the other traditional NHPP and ANN based software reliability models.

A multifaceted model for software reliability prediction during testing

A multifaceted model for software reliability prediction during testing