A bayesian belief network based model for predicting software faults in early phase of software development process

It is always better to have an idea about the future situation of a present work. Prediction of software faults in the early phase of software development life cycle can facilitate to the software personnel to achieve their desired software product. Early prediction is of great importance for optimizing the development cost of a software project. The present study proposes a methodology based on Bayesian belief network, developed to predict total number of faults and to reach a target value of total number of faults during early development phase of software lifecycle. The model has been carried out using the information from similar or earlier version software projects, domain expert’s opinion and the software metrics. Interval type-2 fuzzy logic has been applied for obtaining the conditional probability values in the node probability tables of the belief network. The output pattern corresponding to the total number of faults has been identified by artificial neural network using the input pattern from similar or earlier project data. The proposed Bayesian framework facilitates software personnel to gain the required information about software metrics at early phase for achieving targeted number of software faults. The proposed model has been applied on twenty six software project data. Results have been validated by different statistical comparison criterion. The performance of the proposed approach has been compared with some existing early fault prediction models.     

A fuzzy logic based approach for phase-wise software defects prediction using software metrics

ContextThe software defect prediction during software development has recently attracted the attention of many researchers. The software defect density indicator prediction in each phase of software development life cycle (SDLC) is desirable for developing a reliable software product. Software defect prediction at the end of testing phase may not be more beneficial because the changes need to be performed in the previous phases of SDLC may require huge amount of money and effort to be spent in order to achieve target software quality. Therefore, phase-wise software defect density indicator prediction model is of great importance.ObjectiveIn this paper, a fuzzy logic based phase-wise software defect prediction model is proposed using the top most reliability relevant metrics of the each phase of the SDLC.MethodIn the proposed model, defect density indicator in requirement analysis, design, coding and testing phase is predicted using nine software metrics of these four phases. The defect density indicator metric predicted at the end of the each phase is also taken as an input to the next phase. Software metrics are assessed in linguistic terms and fuzzy inference system has been employed to develop the model.ResultsThe predictive accuracy of the proposed model is validated using twenty real software project data. Validation results are satisfactory. Measures based on the mean magnitude of relative error and balanced mean magnitude of relative error decrease significantly as the software project size increases.ConclusionIn this paper, a fuzzy logic based model is proposed for predicting software defect density indicator at each phase of the SDLC. The predicted defects of twenty different software projects are found very near to the actual defects detected during testing. The predicted defect density indicators are very helpful to analyze the defect severity in different artifacts of SDLC of a software project.     

A fuzzy rule-based generation algorithm in interval type-2 fuzzy logic system for fault prediction in the early phase of software development

Reliability, a measure of software, deals in total number of faults count up to a certain period of time. The present study aims at estimating the total number of software faults during the early phase of software life cycle. Such estimation helps in producing more reliable software as there may be a scope to take necessary corrective actions for improving the reliability within optimum time and cost by the software developers. The proposed interval type-2 fuzzy logic-based model considers reliability-relevant software metric and earlier project data as model inputs. Type-2 fuzzy sets have been used to reduce uncertainties in the vague linguistic values of the software metrics. A rule formation algorithm has been developed to overcome inconsistency in the consequent parts of large number of rules. Twenty-six software project data help to validate the model, and a comparison has been provided to analyse the proposed model’s performance.     

Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network

This paper proposes an artificial neural network (ANN) based software reliability model trained by novel particle swarm optimization (PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.      

Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques

High-assurance and complex mission-critical software systems are heavily dependent on reliability of their underlying software applications. An early software fault prediction is a proven technique in achieving high software reliability. Prediction models based on software metrics can predict number of faults in software modules. Timely predictions of such models can be used to direct cost-effective quality enhancement efforts to modules that are likely to have a high number of faults. We evaluate the predictive performance of six commonly used fault prediction techniques: CART-LS (least squares), CART-LAD (least absolute deviation), S-PLUS, multiple linear regression, artificial neural networks, and case-based reasoning. The case study consists of software metrics collected over four releases of a very large telecommunications system. Performance metrics, average absolute and average relative errors, are utilized to gauge the accuracy of different prediction models. Models were built using both, original software metrics (RAW) and their principle components (PCA). Two-way ANOVA randomized-complete block design models with two blocking variables are designed with average absolute and average relative errors as response variables. System release and the model type (RAW or PCA) form the blocking variables and the prediction technique is treated as a factor. Using multiple-pairwise comparisons, the performance order of prediction models is determined. We observe that for both average absolute and average relative errors, the CART-LAD model performs the best while the S-PLUS model is ranked sixth.     

Estimating software readiness using predictive models

In this study, defect tracking is used as a proxy method to predict software readiness. The number of remaining defects in an application under development is one of the most important factors that allow one to decide if a piece of software is ready to be released. By comparing predicted number of faults and number of faults discovered in testing, software manager can decide whether the software is likely ready to be released or not.The predictive model developed in this research can predict: (i) the number of faults (defects) likely to exist, (ii) the estimated number of code changes required to correct a fault and (iii) the estimated amount of time (in minutes) needed to make the changes in respective classes of the application. The model uses product metrics as independent variables to do predictions. These metrics are selected depending on the nature of source code with regards to architecture layers, types of faults and contribution factors of these metrics. The use of neural network model with genetic training strategy is introduced to improve prediction results for estimating software readiness in this study. This genetic-net combines a genetic algorithm with a statistical estimator to produce a model which also shows the usefulness of inputs.The model is divided into three parts: (1) prediction model for presentation logic tier (2) prediction model for business tier and (3) prediction model for data access tier. Existing object-oriented metrics and complexity software metrics are used in the business tier prediction model. New sets of metrics have been proposed for the presentation logic tier and data access tier. These metrics are validated using data extracted from real world applications. The trained models can be used as tools to assist software mangers in making software release decisions.     

一种加入单改变点的Schneidewind模型研究

Schneidewind 模型已经被广泛研究和应用到很多软件可靠性预测中去。很多软件可靠性增长模型都假设软件所有的失效有相同的查错率,并且在失效发生时,查错率也不发生变化。但实际中,查错率会依赖于多种因素,也会因为软件需求的变化、测试团队的变动而发生变化。本文提出通过几何图形的观测通过对 Schneidewind 模型加入单个改变点来改进模型,并通过实验证明此方法对可靠性精度的提高有一定作用。同时,本文也说明了此方法应用的优点及其局限性。     

Considering the fault dependency concept with debugging time lag in software reliability growth modeling using a power function of testing time

Since the early 1970s tremendous growth has been seen in the research of software reliability growth modeling.In general, software reliability growth models (SRGMs) are applicable to the late stages of testing in software development and they can provide useful information about how to improve the reliability of software products.A number of SRGMs have been proposed in the literature to represent time-dependent fault identification/removal phenomenon;still new models are being proposed that could fit a greater number of reliability growth curves.Often,it is assumed that detected faults axe immediately corrected when mathematical models are developed.This assumption may not be realistic in practice because the time to remove a detected fault depends on the complexity of the fault,the skill and experience of the personnel,the size of the debugging team,the technique,and so on.Thus,the detected fault need not be immediately removed,and it may lag the fault detection process by a delay effect factor.In this paper,we first review how different software reliability growth models have been developed,where fault detection process is dependent not only on the number of residual fault content but also on the testing time,and see how these models can be reinterpreted as the delayed fault detection model by using a delay effect factor.Based on the power function of the testing time concept,we propose four new SRGMs that assume the presence of two types of faults in the software:leading and dependent faults.Leading faults are those that can be removed upon a failure being observed.However,dependent faults are masked by leading faults and can only be removed after the corresponding leading fault has been removed with a debugging time lag.These models have been tested on real software error data to show its goodness of fit,predictive validity and applicability.     

A fuzzy-filtered neuro-fuzzy framework for software fault prediction for inter-version and inter-project evaluation

Fault Prediction is the most required measure to estimate the software quality and reliability. Several methods, measures, aspects and testing methodologies are available to evaluate the software fault. In this paper, a fuzzy-filtered neuro-fuzzy framework is introduced to predict the software faults for internal and external software projects. The suggested framework is split into three primary phases. At the earlier phase, the effective metrics or measures are identified, which can derive the accurate decision on prediction of software fault. In this phase, the composite analytical observation of each software attribute is calculated using Information Gain and Gain Ratio measures. In the second phase, these fuzzy rules are applied on these measures for selection of effective and high-impact features. In the last phase, the Neuro-fuzzy classifier is applied on fuzzy-filtered training and testing sets. The proposed framework is applied to identify the software faults based on inter-version and inter-project evaluation. In this framework, the earlier projects or project-versions are considered as training sets and the new projects or versions are taken as testing sets. The experimentation is conducted on nine open source projects taken from PROMISE repository as well as on PDE and JDT projects. The approximation is applied on internal version-specific fault prediction and external software projects evaluation. The comparative analysis is performed against Decision Tree, Random Tree, Random Forest, Naive Bayes and Multilevel Perceptron classifiers. This prediction result signifies that the proposed framework has gained the higher accuracy, lesser error rate and significant AUC and GM for inter-project and inter-version evaluations.     

Can traditional fault prediction models be used for vulnerability prediction?

Finding security vulnerabilities requires a different mindset than finding general faults in software—thinking like an attacker. Therefore, security engineers looking to prioritize security inspection and testing efforts may be better served by a prediction model that indicates security vulnerabilities rather than faults. At the same time, faults and vulnerabilities have commonalities that may allow development teams to use traditional fault prediction models and metrics for vulnerability prediction. The goal of our study is to determine whether fault prediction models can be used for vulnerability prediction or if specialized vulnerability prediction models should be developed when both models are built with traditional metrics of complexity, code churn, and fault history. We have performed an empirical study on a widely-used, large open source project, the Mozilla Firefox web browser, where 21% of the source code files have faults and only 3% of the files have vulnerabilities. Both the fault prediction model and the vulnerability prediction model provide similar ability in vulnerability prediction across a wide range of classification thresholds. For example, the fault prediction model provided recall of 83% and precision of 11% at classification threshold 0.6 and the vulnerability prediction model provided recall of 83% and precision of 12% at classification threshold 0.5. Our results suggest that fault prediction models based upon traditional metrics can substitute for specialized vulnerability prediction models. However, both fault prediction and vulnerability prediction models require significant improvement to reduce false positives while providing high recall.     

An effective fault prediction model developed using an extreme learning machine with various kernel methods

System analysts often use software fault prediction models to identify fault-prone modules during the design phase of the software development life cycle. The models help predict faulty modules based on the software metrics that are input to the models. In this study, we consider 20 types of metrics to develop a model using an extreme learning machine associated with various kernel methods. We evaluate the effectiveness of the mode using a proposed framework based on the cost and efficiency in the testing phases. The evaluation process is carried out by considering case studies for 30 object-oriented software systems. Experimental results demonstrate that the application of a fault prediction model is suitable for projects with the percentage of faulty classes below a certain threshold, which depends on the efficiency of fault identification (low: 47.28%; median: 39.24%; high: 25.72%). We consider nine feature selection techniques to remove the irrelevant metrics and to select the best set of source code metrics for fault prediction.     

软件可靠性组合预测模型研究

根据灰色模型、谐波分析和时间序列分析理论,对软件测试阶段的失效数据构成的时间序列进行分析,得到软件可靠性组合预测模型。结合实际数据,给出了具体的实现方法。数据试验结果表明,与单一时间序列预测模型相比,该模型具有较高的预测精度和很好的模型适应性。     

基于支持向量机的面向对象软件易发性故障预测

本文考虑软件故障严重程度,并采用C＆K面向对象度量集,以支持向量机分析方法为数学工具,建立一种基于面向对象软件易发性故障预测模型。实验结果表明,与基于朴素贝叶斯的预测模型、随机预测模型和NNge预测模型相比,本文提出的预测模型对于高严重程度故障、低严重程度故障以及未划分故障严重程度的情形均获得较好的预测效果。     

一个考虑多种排错延迟的NHPP类软件可靠性增长模型

软件可靠性增长模型通常假设软件的测试环境与软件实际运行的现场环境相同,期望利用测试阶段获得的失效数据评估软件在现场运行时的失效行为。多数非齐次泊松过程类软件可靠性增长模型假设软件故障被发现后立即被排除,这点假设无论是在测试环境还是在现场环境下都很难实现。根据故障对测试过程的影响,故障的排错时间可被分为多种。提出了一个考虑多种排错延迟的软件可靠性增长模型,讨论了基于这个模型的故障排除效率函数,指出从用户角度出发讨论软件可靠性时必须考虑重复性故障。     

Estimation of reliability and cost relationship for architecture-based software

In this paper, we propose a new method to estimate the relationship between software reliability and software development cost taking into account the complexity for developing the software system and the size of software intended to develop during the implementation phase of the software development life cycle. On the basis of estimated relationship, a set of empirical data has been used to validate the correctness of the proposed model by comparing the result with the other existing models. The outcome of this work shows that the method proposed here is a relatively straightforward one in formulating the relationship between reliability and cost during implementation phase.     

Combining the requirement information for software defect estimation in design time

This paper analyzes the ability of requirement metrics for software defect prediction. Statistical significance tests are used to compare six machine learning algorithms on the requirement metrics, design metrics, and combination of both metrics in our analysis. The experimental results show the effectiveness of the predictor built on the combination of the requirement and design metrics in the early phase of the software development process.     

遗传优化支持向量机的软件可靠性预测模型

软件可靠性预测在软件开发的早期就能预测出哪些模块有出错倾向。提出一种改进的支持向量机来进行软件可靠性预测。针对支持向量机参数难选择的问题,将遗传算法引入到支持向量机的参数选择中,构造基于遗传算法优化支持向量机的软件可靠性预测模型,并用主成分分析的方法对软件度量数据进行降维,通过仿真实验,证明该模型比支持向量机、BP神经网络、分类回归树和聚类分析等预测模型具有更高的预测精度。     

Comparative analysis of statistical and machine learning methods for predicting faulty modules

The demand for development of good quality software has seen rapid growth in the last few years. This is leading to increase in the use of the machine learning methods for analyzing and assessing public domain data sets. These methods can be used in developing models for estimating software quality attributes such as fault proneness, maintenance effort, testing effort. Software fault prediction in the early phases of software development can help and guide software practitioners to focus the available testing resources on the weaker areas during the software development. This paper analyses and compares the statistical and six machine learning methods for fault prediction. These methods (Decision Tree, Artificial Neural Network, Cascade Correlation Network, Support Vector Machine, Group Method of Data Handling Method, and Gene Expression Programming) are empirically validated to find the relationship between the static code metrics and the fault proneness of a module. In order to assess and compare the models predicted using the regression and the machine learning methods we used two publicly available data sets AR1 and AR6. We compared the predictive capability of the models using the Area Under the Curve (measured from the Receiver Operating Characteristic (ROC) analysis). The study confirms the predictive capability of the machine learning methods for software fault prediction. The results show that the Area Under the Curve of model predicted using the Decision Tree method is 0.8 and 0.9 (for AR1 and AR6 data sets, respectively) and is a better model than the model predicted using the logistic regression and other machine learning methods.     

不完美排错软件可靠性增长模型效用量化研究

为了进一步提升现有非齐次泊松过程类软件可靠性增长模型的拟合和预测性能,首先从故障总数增长趋势角度对不完美排错模型进行深入研究,提出两个一般性不完美排错框架模型,分别考虑了总故障数量函数与累计检测故障函数间的线性关系与微分关系,并求得累计检测的故障数量与软件中总故障数量函数表达式;其次,在六组真实的失效数据集上对比了提出的两种一般性不完美排错模型和六种不完美排错模型拟合预测性能表现。实例验证结果表明,提出的一般性不完美排错框架模型在大多数失效数据集上都具有优秀的拟合和预测性能,证明了新建模型的有效性和实用性;通过对提出的模型与其他不完美排错模型在数据集上的性能的深入分析,为实际应用中不完美排错模型的选择提出了建议。