Using regression trees to classify fault-prone software modules

Software faults are defects in software modules that might cause failures. Software developers tend to focus on faults, because they are closely related to the amount of rework necessary to prevent future operational software failures. The goal of this paper is to predict which modules are fault-prone and to do it early enough in the life cycle to be useful to developers. A regression tree is an algorithm represented by an abstract tree, where the response variable is a real quantity. Software modules are classified as fault-prone or not, by comparing the predicted value to a threshold. A classification rule is proposed that allows one to choose a preferred balance between the two types of misclassification rates. A case study of a very large telecommunications systems considered software modules to be fault-prone, if any faults were discovered by customers. Our research shows that classifying fault-prone modules with regression trees and the using the classification rule in this paper, resulted in predictions with satisfactory accuracy and robustness.     

A Multi-Objective Software Quality Classification Model Using Genetic Programming

A key factor in the success of a software project is achieving the best-possible software reliability within the allotted time & budget. Classification models which provide a risk-based software quality prediction, such as fault-prone & not fault-prone, are effective in providing a focused software quality assurance endeavor. However, their usefulness largely depends on whether all the predicted fault-prone modules can be inspected or improved by the allocated software quality-improvement resources, and on the project-specific costs of misclassifications. Therefore, a practical goal of calibrating classification models is to lower the expected cost of misclassification while providing a cost-effective use of the available software quality-improvement resources. This paper presents a genetic programming-based decision tree model which facilitates a multi-objective optimization in the context of the software quality classification problem. The first objective is to minimize the "Modified Expected Cost of Misclassification", which is our recently proposed goal-oriented measure for selecting & evaluating classification models. The second objective is to optimize the number of predicted fault-prone modules such that it is equal to the number of modules which can be inspected by the allocated resources. Some commonly used classification techniques, such as logistic regression, decision trees, and analogy-based reasoning, are not suited for directly optimizing multi-objective criteria. In contrast, genetic programming is particularly suited for the multi-objective optimization problem. An empirical case study of a real-world industrial software system demonstrates the promising results, and the usefulness of the proposed model     

Classification-tree models of software-quality over multiplereleases

This paper presents an empirical study that evaluates software-quality models over several releases, to address the question, “How long will a model yield useful predictions?” The classification and regression trees (CART) algorithm is introduced, CART can achieve a preferred balance between the two types of misclassification rates. This is desirable because misclassification of fault-prone modules often has much more severe consequences than misclassification of those that are not fault-prone. The case-study developed 2 classification-tree models based on 4 consecutive releases of a very large legacy telecommunication system. Forty-two software product, process and execution metrics were candidate predictors. Model 1 used measurements of the first release as the training data set; this model had 11 important predictors. Model 2 used measurements of the second release as the training data set; this model had 15 important predictors. Measurements of subsequent releases were evaluation data sets. Analysis of the models' predictors yielded insights into various software development practices. Both models had accuracy that would be useful to developers. One might suppose that software-quality models lose their value very quickly over successive releases due to evolution of the product and the underlying development processes. The authors found the models remained useful over all the releases studied     

A comparative study of pattern recognition techniques for qualityevaluation of telecommunications software

The extreme risks of software faults in the telecommunications environment justify the costs of data collection and modeling of software quality. Software quality models based on data drawn from past projects can identify key risk or problem areas in current similar development efforts. Once these problem areas are identified, the project management team can take actions to reduce the risks. Studies of several telecommunications systems have found that only 4-6% of the system modules were complex [LeGall et al. 1990]. Since complex modules are likely to contain a large proportion of a system's faults, the approach of focusing resources on high-risk modules seems especially relevant to telecommunications software development efforts. A number of researchers have recognized this, and have applied modeling techniques to isolate fault-prone or high-risk program modules. A classification model based upon discriminant analytic techniques has shown promise in performing this task. The authors introduce a neural network classification model for identifying high-risk program modules, and compare the quality of this model with that of a discriminant classification model fitted with the same data. They find that the neural network techniques provide a better management tool in software engineering environments. These techniques are simpler, produce more accurate models, and are easier to use     

Count Models for Software Quality Estimation

Identifying which software modules, during the software development process, are likely to be faulty is an effective technique for improving software quality. Such an approach allows a more focused software quality & reliability enhancement endeavor. The development team may also like to know the number of faults that are likely to exist in a given program module, i.e., a quantitative quality prediction. However, classification techniques such as the logistic regression model (lrm) cannot be used to predict the number of faults. In contrast, count models such as the Poisson regression model (prm), and the zero-inflated Poisson (zip) regression model can be used to obtain both a qualitative classification, and a quantitative prediction for software quality. In the case of the classification models, a classification rule based on our previously developed generalized classification rule is used. In the context of count models, this study is the first to propose a generalized classification rule. Case studies of two industrial software systems are examined, and for each we developed two count models, (prm, and zip), and a classification model (lrm). Evaluating the predictive capabilities of the models, we concluded that the prm, and the zip models have similar classification accuracies as the lrm. The count models are also used to predict the number of faults for the two case studies. The zip model yielded better fault prediction accuracy than the prm. As compared to other quantitative prediction models for software quality, such as multiple linear regression (mlr), the prm, and zip models have a unique property of yielding the probability that a given number of faults will occur in any module     

视频数据库多级访问控制

针对视频数据库中涉及敏感信息的视频数据分级保护问题,提出视频数据库多级访问控制模型。在该模型中,设计用户身份辨别及身份强度算法,其结果作为用户安全等级隶属函数的输入,该函数值为用户安全等级隶属度,并与视频数据安全等级隶属度一起作为授权规则中安全等级隶属度比较函数的输入,其函数值结合时间元素能够灵活地实现多级访问控制。与已有的访问控制模型相比,该模型最突出的特点是实现动态授权和视频数据分级保护。     

一种面向嵌入式软件体系结构的形式化建模方法

为了解决MARTE（Modeling and Analysis of Real Time and Embedded systems）在建立嵌入式软件模型时不够精确的问题,结合Object-Z和PTA（Probabilistic Timed Automation）的优点,本文提出了一种集成的形式化建模方法--PTA-OZ.该方法不仅能够对嵌入式软件模型的静态语义和动态语义进行精确描述,而且通过模型转换规则,能够将MARTE模型转换为PTA-OZ模型.并对模型转换的语义一致性进行了验证,证明本文方法在转换过程能够保持结构语义和行为语义的一致性.最后通过实例模型描述从嵌入式软件建模到属性检验的过程.     

Composition of Java-based router elements and its application to generalized video multicast

We describe a software router capable of flexible service composition through plug and play of specialized Java software modules. These Java modules - previously developed for network simulation in the J-Sim project - are leveraged for actual deployment on our router through a JSocket class of objects. Our system provides significant software engineering benefits of simplified code development and safe composition/reuse of various router components. These benefits have proved highly useful in implementing new network services for emerging application needs. In particular, we present a paradigm of generalized multicast with application to large-scale video streaming. We detail the performance of our prototype implementation in terms of efficiency (when compared to a native C implementation) and its ability to satisfy the dynamic resource capabilities of a heterogeneous set of receiver end systems, including mobile handheld devices.     

Statistical analysis of the geometric de-eutrophicationsoftware-reliability model

A detailed statistical analysis of the Moranda geometric de-eutrophication software-reliability model, which appears to be a particular case of a class of general models with proportional failure rates, is given. Statistical inference on the unknown parameters is discussed. The distribution of the maximum-likelihood estimator of the main parameter provides exact confidence intervals and a novel reliability-growth test. Explicit estimators based on a least-squares method are proposed. The model is satisfactorily applied to real software error data. The geometric de-eutrophication model presents interesting theoretical and practical aspects. It is conceptually well founded and the parameters, which have useful practical interpretation, can be estimated by methods which provide prediction with good statistical properties     

CCNet: CNN model with channel attention and convolutional pooling mechanism for spatial image steganalysis

Image steganalysis based on convolutional neural networks(CNN) has attracted great attention. However, existing networks lack attention to regional features with complex texture, which makes the ability of discrimination learning miss in network. In this paper, we described a new CNN designed to focus on useful features and improve detection accuracy for spatial-domain steganalysis. The proposed model consists of three modules: noise extraction module, noise analysis module and classification module. A channel attention mechanism is used in the noise extraction module and analysis module, which is realized by embedding the SE(Squeeze-and-Excitation) module into the residual block. Then, we use convolutional pooling instead of average pooling to aggregate features. The experimental results show that detection accuracy of the proposed model is significantly better than those of the existing models such as SRNet, Zhu-Net and GBRAS-Net. Compared with these models, our model has better generalization ability, which is critical for practical application.     

Redex: The ranging equipment diagnostic expert system

This paper describes REDEX, an advanced prototype expert system that diagnoses hardware failures in the Ranging Equipment (RE) at NASA's Ground Network tracking stations. REDEX will help the RE technician identify faulty circuit cards or modules that must be replaced, and thereby reduce troubleshooting time. It features a highly graphical user interface that uses color block diagrams and layout diagrams to illustrate the location of a fault. A semantic network knowledge representation technique was used to model the design structure of the RE. A catalog of generic troubleshooting rules was compiled to represent heuristics that are applied in diagnosing electronic equipment. Specific troubleshooting rules were identified to represent additional diagnostic knowledge that is unique to the RE Over 50 genetic and 250 specific troubleshooting rules have been derived. REDEX is implemented in Prolog on an IBM PC AT-compatible workstation. Block diagram graphics displays are color-coded to identify signals that have been monitored or inferred to have nominal values, signals that are out of tolerance, and circuit cards and functions that are diagnosed as faulty. A hypertext-like scheme is used to allow the user to easily navigate through the space of diagrams and tables. Over 50 graphic and tabular displays have been implemented. REDEX is currently being evaluated in a stand-alone mode using simulated RE fault scenarios. It will soon be interfaced to the RE and tested in an online environment. When completed and fielded, REDEX will be a concrete example of the application of expert systems technology to the problem of improving performance and reducing the lifestyle costs of operating NASA's communications networks in the 1990's.     

Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases

This paper introduces the Inverted Hierarchical Neuro-Fuzzy BSP System (HNFB/sup -1/), a new neuro-fuzzy model that has been specifically created for record classification and rule extraction in databases. The HNFB/sup -1/ is based on the Hierarchical Neuro-Fuzzy Binary Space Partitioning Model (HNFB), which embodies a recursive partitioning of the input space, is able to automatically generate its own structure, and allows a greater number of inputs. The new HNFB/sup -1/ allows the extraction of knowledge in the form of interpretable fuzzy rules expressed by the following: If x is A and y is B, then input pattern belongs to class Z. For the process of rule extraction in the HNFB/sup -1/ model, two fuzzy evaluation measures were defined: 1) fuzzy accuracy and 2) fuzzy coverage. The HNFB/sup -1/ has been evaluated with different benchmark databases for the classification task: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders, and Heart Disease. When compared with several other pattern classification models and algorithms, the HNFB/sup -1/ model has shown similar or better classification performance. Nevertheless, its performance in terms of processing time is remarkable. The HNFB/sup -1/ converged in less than one minute for all the databases described in the case study.     

A Comprehensive Empirical Study of Count Models for Software Fault Prediction

Count models, such as the Poisson regression model, and the negative binomial regression model, can be used to obtain software fault predictions. With the aid of such predictions, the development team can improve the quality of operational software. The zero-inflated, and hurdle count models may be more appropriate when, for a given software system, the number of modules with faults are very few. Related literature lacks quantitative guidance regarding the application of count models for software quality prediction. This study presents a comprehensive empirical investigation of eight count models in the context of software fault prediction. It includes comparative hypothesis testing, model selection, and performance evaluation for the count models with respect to different criteria. The case study presented is that of a full-scale industrial software system. It is observed that the information obtained from hypothesis testing, and model selection techniques was not consistent with the predictive performances of the count models. Moreover, the comparative analysis based on one criterion did not match that of another criterion. However, with respect to a given criterion, the performance of a count model is consistent for both the fit, and test data sets. This ensures that, if a fitted model is considered good based on a given criterion, then the model will yield a good prediction based on the same criterion. The relative performances of the eight models are evaluated based on a one-way anova model, and Tukey's multiple comparison technique. The comparative study is useful in selecting the best count model for estimating the quality of a given software system     

Global stability of generalized additive fuzzy systems

The paper explores the stability of a class of feedback fuzzy systems. The class consists of generalized additive fuzzy systems that compute a system output as a convex sum of linear operators, continuous versions of these systems are globally asymptotically stable if all rule matrices are stable (negative definite). So local rule stability leads to global system stability. This relationship between local and global system stability does not hold for the better known discrete versions of feedback fuzzy systems. A corollary shows that it does hold for the discrete versions in the special but practical case of diagonal rule matrices. The paper first reviews additive fuzzy systems and then extends them to the class of generalized additive fuzzy systems. It also derives the basic ratio structure of additive fuzzy systems and shows how supervised learning can tune their parameters     

Incorporating duration and region association information in trajectory classification

Trajectory classification is the process of predicting the class label of moving objects based on their trajectories and other features. Existing works on building trajectory classification model discover features by using spatial distribution and shape of sub-trajectory. However, they do not utilise duration and region association information available in trajectory data during feature generation. In this study, trajectory features are generated using spatial distribution, duration and region association information of trajectories. In particular, two types of features, region rules and path rules, are generated from trajectories for classification. Region rules consider the spatial distribution of trajectories, the time spent (duration) by the trajectories in the region and the association information with other regions. Path rules differentiate objects based on their travelling patterns and speed. Efficient algorithms are devised to obtain region rules and path rules. Based on the discovered rule, trajectory classification model is built to predict the class label of new trajectory. Experimental results on various real-world data-sets show that incorporating duration and region association information in trajectory classification improves accuracy.     

Concept d’extensivité pour un réseau de Clos et probabilité de blocage pour un système à trois étages

The authors introduce the concept of extensivity which is useful in the study of switching networks models of reduced size. They calculate blocking probability for different routing rules and deduce an evaluation of this different routing rules. The results are compared to the Lee formula in the case of random hunting rule.     

A New Methodology for Predicting Software Reliability in the Random Field Environments

This paper presents a new methodology for predicting software reliability in the field environment. Our work differs from some existing models that assume a constant failure detection rate for software testing and field operation environments, as this new methodology considers the random environmental effects on software reliability. Assuming that all the random effects of the field environments can be captured by a unit-free environmental factor,$eta$, which is modeled as a random-distributed variable, we establish a generalized random field environment (RFE) software reliability model that covers both the testing phase and the operating phase in the software development cycle. Based on the generalized RFE model, two specific random field environmental reliability models are proposed for predicting software reliability in the field environment: the$gamma$-RFE model, and the$beta$-RFE model. A set of software failure data from a telecommunication software application is used to illustrate the proposed models, both of which provide very good fittings to the software failures in both testing and operation environments. This new methodology provides a viable way to model the user environments, and further makes adjustments to the reliability prediction for similar software products. Based on the generalized software reliability model, further work may include the development of software cost models and the optimum software release policies under random field environments.     

嵌入式智能云控制系统的原理与设计

云是用语言值表示的某个定性概念与其定量表示之间的不确定性转换模型.基于云模型的定性知识推理,以概念为基本表示,从数据库中挖掘出定性知识,构造规则发生器.多条定性规则构成规则库,当输入一个特定的条件激活多条定性规则时,通过推理引擎,实现带有不确定性的推理和控制.以往实现云控制器,主要依赖计算机及相关软件,限制了其进一步的推广应用.针对基于Quartus Ⅱ设计软件和Nios Ⅱ处理器的可编程片上系统进行设计开发,将云控制器在目标电路板的FPGA芯片中进行系统集成,实现以嵌入式云控制器为核心的智能控制系统,具有接口灵活、扩展性强、便于实现等优点.为基于云模型的智能控制系统的工业化应用,提供了硬件设计基础.