基于CGOM的软件费用模型研究

软件测试是提高软件质量的重要手段，对测试数据充分的测试准则是软件测试完成的标准．目前，绝大多数的测试准则并不考虑软件费用，容易为了满足测试准则而使用户承担昂贵的测试费用．该文提出了一种随机过程类非齐次泊松过程(NHPP)中的经典模型——G-O模型的改进模型，并基于此模型构造了一个综合了软件设计费用、软件测试费用、软件维护费用、软件失效造成的风险损失的软件费用模型．最后从软件费用出发，讨论了软件的最佳发布时间．     

A time/structure based software reliability model

The past 20 years have seen the formulation of numerous analytical software reliability models for estimating the reliability growth of a software product. The predictions obtained by applying these models tend to be optimistic due to the inaccuracies in the operational profile, and saturation effect of testing. Incorporating knowledge gained about some structural attribute of the code, such as test coverage, into the time-domain models can help alleviate this optimistic trend. In this paper we present an enhanced non-homogeneous Poisson process (ENHPP) model which incorporates explicitly the time-varying test-coverage function in its analytical formulation, and provides for defective fault detection and test coverage during the testing and operational phases. It also allows for a time varying fault detection rate. The ENHPP model offers a unifying framework for all the previously reported finite failure NHPP models via test coverage. We also propose the log-logistic coverage function which can capture an increasing/decreasing failure detection rate per fault, which cannot be accounted for by the previously reported finite failure NHPP models. We present a methodology based on the ENHPP model for reliability prediction earlier in the testing phase. Expressions for predictions in the operational phase of the software, software availability, and optimal software release times subject to various constraints such as cost, reliability, and availability are developed based on the ENHPP model. We also validate the ENHPP model based on four different coverage functions using five failure data sets. This revised version was published online in June 2006 with corrections to the Cover Date.     

An Empirical Method for Selecting Software Reliability Growth Models

Estimating remaining defects (or failures) in software can help test managers make release decisions during testing. Several methods exist to estimate defect content, among them a variety of software reliability growth models (SRGMs). SRGMs have underlying assumptions that are often violated in practice, but empirical evidence has shown that many are quite robust despite these assumption violations. The problem is that, because of assumption violations, it is often difficult to know which models to apply in practice. We present an empirical method for selecting SRGMs to make release decisions. The method provides guidelines on how to select among the SRGMs to decide on the best model to use as failures are reported during the test phase. The method applies various SRGMs iteratively during system test. They are fitted to weekly cumulative failure data and used to estimate the expected remaining number of failures in software after release. If the SRGMs pass proposed criteria, they may then be used to make release decisions. The method is applied in a case study using defect reports from system testing of three releases of a large medical record system to determine how well it predicts the expected total number of failures.     

Analyzing software measurement data with clustering techniques

For software quality estimation, software development practitioners typically construct quality-classification or fault prediction models using software metrics and fault data from a previous system release or a similar software project. Engineers then use these models to predict the fault proneness of software modules in development. Software quality estimation using supervised-learning approaches is difficult without software fault measurement data from similar projects or earlier system releases. Cluster analysis with expert input is a viable unsupervised-learning solution for predicting software modules' fault proneness and potential noisy modules. Data analysts and software engineering experts can collaborate more closely to construct and collect more informative software metrics.     

考虑故障相关的软件可靠性增长模型研究

软件可靠性增长模型是用来评估和预测软件可靠性的重要工具.目前,绝大多数的软件可靠性增长模型并没有考虑故障之间的相关性,也没有考虑测试环境和运行环境的区别.文中提出了一种随机过程类非齐次泊松过程(NHPP)中的考虑故障相关性、测试环境和运行环境差别的模型.在两组失效数据上的实验分析表明:对这两组失效数据,文中提出的模型比其他一些非齐次泊松过程类模型的拟合效果和预测效果更好.     

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。     

Software Quality Analysis of Unlabeled Program Modules With Semisupervised Clustering

Software quality assurance is a vital component of software project development. A software quality estimation model is trained using software measurement and defect (software quality) data of a previously developed release or similar project. Such an approach assumes that the development organization has experience with systems similar to the current project and that defect data are available for all modules in the training data. In software engineering practice, however, various practical issues limit the availability of defect data for modules in the training data. In addition, the organization may not have experience developing a similar system. In such cases, the task of software quality estimation or labeling modules as fault prone or not fault prone falls on the expert. We propose a semisupervised clustering scheme for software quality analysis of program modules with no defect data or quality-based class labels. It is a constraint-based semisupervised clustering scheme that uses k-means as the underlying clustering algorithm. Software measurement data sets obtained from multiple National Aeronautics and Space Administration software projects are used in our empirical investigation. The proposed technique is shown to aid the expert in making better estimations as compared to predictions made when the expert labels the clusters formed by an unsupervised learning algorithm. In addition, the software quality knowledge learnt during the semisupervised process provided good generalization performance for multiple test data sets. An analysis of program modules that remain unlabeled subsequent to our semisupervised clustering scheme provided useful insight into the characteristics of their software attributes     

面向运行时间预测和容错感知的网格资源分配

提出与描述了一种面向任务运行时间预测和容错感知（Fault-Aware）的网格资源分配策略,采用主动容错的方式,在资源出错之前尽量提前避免它出错或异常的情况发生。该策略把网格中任务的运行时间（runtime）预测和资源的在线时间（uptime）预测结合起来,相对于普通的调度策略具有比较高的资源利用率。在具体的CoBRA网格中间件中实现了该容错感知调度,描述了实现该容错感知调度策略模块的功能。测试过程中选择了睡眠任务技术,划分四种不同的场景进行实验,把该容错感知资源分配与普通的FCFS调度策略进行比较,结果证明在可变化的资源可用性的情况下系统可以加快应用的整体执行时间,具有很小的偏差。     

On deterministic chaos in software reliability growth models

Software reliability growth models attempt to forecast the future reliability of a software system, based on observations of the historical occurrences of failures. This allows management to estimate the failure rate of the system in field use, and to set release criteria based on these forecasts. However, the current software reliability growth models have never proven to be accurate enough for widespread industry use. One possible reason is that the model forms themselves may not accurately capture the underlying process of fault injection in software; it has been suggested that fault injection is better modeled as a chaotic process rather than a random one. This possibility, while intriguing, has not yet been evaluated in large-scale, modern software reliability growth datasets.We report on an analysis of four software reliability growth datasets, including ones drawn from the Android and Mozilla open-source software communities. These are the four largest software reliability growth datasets we are aware of in the public domain, ranging from 1200 to over 86,000 observations. We employ the methods of nonlinear time series analysis to test for chaotic behavior in these time series; we find that three of the four do show evidence of such behavior (specifically, a multifractal attractor). Finally, we compare a deterministic time series forecasting algorithm against a statistical one on both datasets, to evaluate whether exploiting the apparent chaotic behavior might lead to more accurate reliability forecasts.     

The impact of software evolution and reuse on software quality

This paper presents a case study of a software project in the maintenance phase. The case study was based on a sample of modules, representing about 1.3 million lines of code, from a very large telecommunications system. Software quality models were developed to predict the number of faults expected from the coding through operations phases. Since modules from the prior release were often reused to develop a new release, one model incorporated reuse data as additional independent variables. We compare this model's performance to a similar model without reuse data.Software quality models often have product metrics as the only input data for predicting quality. There is an implicit assumption that all the modules have had a similar development history, so that product attributes are the primary drivers of different quality levels. Reuse of software as components and software evolution do not fit this assumption very well, and consequently, traditional models for such environments may not have adequate accuracy. Focusing on the software maintenance phase, this study demonstrated that reuse data can significantly improve the predictive accuracy of software quality models.     

基于统计学的软件系统预恢复时间阈值计算策略

软件系统的预恢复是一种预防和主动的容错技术。本文提出了一种基于统计学的软件系统自恢复时间阈值计算算法。该算法基于满足软件系统可用性概率最大化的思想，在系统性能衰退时间分布未知的情况下，根据一定量的性能衰退的检测数据，计算出优化的软件系统自恢复时间闽值。仿真实验结果表明计算结果合理、稳定性好，能有效地应用于实际系统中。     

On the edge. Regression testability

A systematic approach to the regression testing aspect of software maintainability is presented. It is stated that regression testing is important at the unit, integration, and system testing levels. Software development teams usually have responsibility for unit and integration testing, but do not consistently apply regression testing at these levels when they make changes and often do not even systematically retain test data. System or functional testers, on the other hand, are systematic about keeping test data and applying regression testing. This costs more than detecting regression error earlier. Results from a research project to evaluate regression testing concepts are discussed     

一种软件可靠性增长模型选择与综合方法

软件可靠性增长模型可以预测软件在将来某个时刻的可靠性,以此作为软件是否发布的依据.而目前常见的各种模型对不同失效数据集的预测能力并不一致.提出了一种软件可靠性增长模型选择和应用的框架,利用可靠性模型评价准则,对特定的失效数据集选择优选模型集,根据优选模型集利用神经网络较好的学习预测能力计算可靠性.利用此方法对实际软件项目中的失效数据进行了分析,并验证了它的有效性.     

Improving test efficiency through system test prioritization

Software testing is an expensive process consuming at least 50% of the total development cost. Among the types of testing, system testing is the most expensive and complex. Companies are frequently faced with budgetary constraints, which may limit their ability to effectively complete testing efforts before delivering a software product. We build upon prior test case prioritization research and present a system-level approach to test case prioritization called Prioritization of Requirements for Test (PORT). PORT prioritizes system test cases based on four factors for each requirement: customer priority, implementation complexity, fault proneness, and requirements volatility. Test cases for requirements with higher priority based upon a weighted average of these factors are executed earlier in system test. An academic feasibility study and three post hoc industrial studies were conducted. Results indicate that PORT can be used to improve the rate of failure detection when compared with a random and operational profile-driven random approach. Furthermore, we investigated the contribution of the prioritization factors towards the improved rate of failure detection and found customer priority was the most significant contributor. Tool support is provided for the PORT scheme which allows for automatic collection of the four factor values and the resultant test case prioritization.     

Software architecture reliability analysis using failure scenarios

With the increasing size and complexity of software in embedded systems, software has now become a primary threat for the reliability. Several mature conventional reliability engineering techniques exist in literature but traditionally these have primarily addressed failures in hardware components and usually assume the availability of a running system. Software architecture analysis methods aim to analyze the quality of software-intensive system early at the software architecture design level and before a system is implemented. We propose a Software Architecture Reliability Analysis Approach (SARAH) that benefits from mature reliability engineering techniques and scenario-based software architecture analysis to provide an early software reliability analysis at the architecture design level. SARAH defines the notion of failure scenario model that is based on the Failure Modes and Effects Analysis method (FMEA) in the reliability engineering domain. The failure scenario model is applied to represent so-called failure scenarios that are utilized to derive fault tree sets (FTS). Fault tree sets are utilized to provide a severity analysis for the overall software architecture and the individual architectural elements. Despite conventional reliability analysis techniques which prioritize failures based on criteria such as safety concerns, in SARAH failure scenarios are prioritized based on severity from the end-user perspective. SARAH results in a failure analysis report that can be utilized to identify architectural tactics for improving the reliability of the software architecture. The approach is illustrated using an industrial case for analyzing reliability of the software architecture of the next release of a Digital TV.     

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction (MOMPSO)

Software testing plays a pivotal role in entire software development lifecycle. It provides researchers with extensive opportunities to develop novel methods for the optimized and cost-effective test suite Although implementation of such a cost-effective test suite with regression testing is being under exploration still it contains lot of challenges and flaws while incorporating with any of the new regression testing algorithm due to irrelevant test cases in the test suite which are not required. These kinds of irrelevant test cases might create certain challenges such as code-coverage in the test suite, fault-tolerance, defects due to uncovered-statements and overall-performance at the time of execution. With this objective, the proposed a new Modified Particle Swarm optimization used for multi-objective test suite optimization. The experiment results involving six subject programs show that MOMPSO method can outer perform with respect to both reduction rate (90.78% to 100%) and failure detection rate (44.56% to 55.01%). Results proved MOMPSO outperformed the other stated algorithms.     

A hypothetical application of the Statistical Modeling and Estimation of Reliability Functions for Software (SMERFS) to Knowledge-Based Systems (KBSs)

Software reliability models are applied to conventional software to compute several important quantities. Two of these are the rate at which software fails and the number of software faults remaining in software. The software failure rate and the number of faults remaining in software are measures of software trustworthiness or reliability. Software reliability models, however, have never been applied to knowledge-based systems to determine these quantities. This paper reports a hypothetical application of the Statistical Modeling and Estimation of Reliability Functions for Software (SMERFS) to knowledge-based systems. SMERFS, developed by the Naval Surface Warfare Center (NSWC), contains a representative set of software reliability models “chosen for their performance in comparative studies and their ability to handle data collected from various testing environments.”     

Using Bounded Model Checking for Coverage Analysis of Safety-Critical Software in an Industrial Setting

Testing and Bounded Model Checking (BMC) are two techniques used in Software Verification for bug-hunting. They are expression of two different philosophies: testing is used on the compiled code and it is more suited to find errors in common behaviors, while BMC is used on the source code to find errors in uncommon behaviors of the system. Nowadays, testing is by far the most used technique for software verification in industry: it is easy to use and even when no error is found, it can release a set of tests certifying the (partial) correctness of the compiled system. In the case of safety critical software, in order to increase the confidence of the correctness of the compiled system, it is often required that the provided set of tests covers 100% of the code. This requirement, however, substantially increases the costs associated to the testing phase, since it often involves the manual generation of tests. In this paper we show how BMC can be productively applied to the Software Verification process in industry. In particular, we show how to productively use a Bounded Model Checker for C programs (CBMC) as an automatic test generator for the Coverage Analysis of Safety Critical Software. In particular, we experimented CBMC on a subset of the modules of the European Train Control System (ETCS) of the European Rail Traffic Management System (ERTMS) source code, an industrial system for the control of the traffic railway, provided by Ansaldo STS. The Code of the ERTMS/ETCS, with thousands of lines, has been used as trial application with CBMC obtaining a set of tests satisfying the target 100% code coverage, requested by the CENELEC EN50128 guidelines for software development of safety critical systems. The use of CBMC for test generation led to a dramatic increase in the productivity of the entire Software Development process by substantially reducing the costs of the testing phase. To the best of our knowledge, this is the first time that BMC techniques have been used in an industrial setting for automatically generating tests achieving full coverage of Safety-Critical Software. The positive results demonstrate the maturity of Bounded Model Checking techniques for automatic test generation in industry.     

考虑测试环境和实际运行环境的软件可靠性增长模型

软件可靠性增长模型中测试阶段和操作运行阶段环境的不同导致了两个阶段故障检测率的不同.非齐次泊松过程类软件可靠性增长模型是评价软件产品可靠性指标的有效工具.在一些非齐次泊松过程类模型中,有些学者提出了常量的环境因子,用来描述测试环境和运行环境的差别.实际上,环境因子应该是随时间变化的变量.考虑了运行阶段和测试阶段环境的不同,根据实测数据得到了变化的环境因子,并且根据测试阶段的故障检测率和变化的环境因子,转化得到了操作运行阶段的故障检测率.考虑到故障的排除效率和故障引入率,从而建立了一个既考虑运行环境和测试环境差别,又考虑故障排除效率和故障引入率的非齐次泊松过程类软件可靠性增长模型（PTEO-SRGM）.在两组失效数据上的实验分析表明,对这组失效数据,PTEO-SRGM模型比G-O模型等模型的拟合效果和预测能力更好.