The Experts below are selected from a list of 107688 Experts worldwide ranked by ideXlab platform
Chinyu Huang - One of the best experts on this subject based on the ideXlab platform.
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estimation and analysis of some generalized multiple change point Software Reliability models
IEEE Transactions on Reliability, 2011Co-Authors: Chinyu Huang, Michael R LyuAbstract:Software typically undergoes debugging during both a testing phase before product release, and an operational phase after product release. But it is noted that the fault detection and removal processes during Software development and operation are different. For example, the fault removal during operation occurs generally at a slower pace than development. In this paper, we derive a powerful, easily deployable technique for Software Reliability prediction and assessment in the testing and operational phases. We first review how several existing Software Reliability growth models (SRGM) based on non- homogeneous Poisson processes (NHPP) can be readily derived from a unified theory. With the unified theory, we further incorporate the concept of multiple change-points, i.e. points in time when the Software environment changes, into Software Reliability modeling. Several models are proposed and discussed under both ideal and imperfect debugging conditions. We estimate the parameters of the proposed models by employing real Software failure data, and give a fair comparison with some existing SRGM. Numerical results show that the proposed models can provide good Software Reliability prediction in the various stages of Software development and operation. Our approach is flexible; we can model various environments ranging from exponential-type to S-shaped NHPP models.
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Software Reliability analysis and assessment using queueing models with multiple change points
Computers & Mathematics With Applications, 2010Co-Authors: Chinyu Huang, Tsuiying HungAbstract:Over the past three decades, many Software Reliability growth models (SRGMs) have been proposed, and they can be used to predict and estimate Software Reliability. One common assumption of these conventional SRGMs is to assume that detected faults will be removed immediately. In reality, this assumption may not be reasonable and may not always occur. During debugging, developers need time to reproduce the failure, identify the root causes of faults, fix them, and then re-run the Software. From some experiments or observations, the fault correction rate may not be a constant and could be changed at certain points as time proceeds. Consequently, in this paper, we will investigate and study how to apply queueing models to describe the fault detection and correction processes during Software development. We propose an extended infinite server queueing model with multiple change-points to predict and assess Software Reliability. Experimental results based on real failure data show that the proposed model can depict the change of fault correction rates and predict the behavior of Software development more accurately than traditional SRGMs.
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Software Reliability analysis and measurement using finite and infinite server queueing models
IEEE Transactions on Reliability, 2008Co-Authors: Chinyu Huang, Weichih HuangAbstract:Software Reliability is often defined as the probability of failure-free Software operation for a specified period of time in a specified environment. During the past 30 years, many Software Reliability growth models (SRGM) have been proposed for estimating the Reliability growth of Software. In practice, effective debugging is not easy because the fault may not be immediately obvious. Software engineers need time to read, and analyze the collected failure data. The time delayed by the fault detection & correction processes should not be negligible. Experience shows that the Software debugging process can be described, and modeled using queueing system. In this paper, we will use both finite, and infinite server queueing models to predict Software Reliability. We will also investigate the problem of imperfect debugging, where fixing one bug creates another. Numerical examples based on two sets of real failure data are presented, and discussed in detail. Experimental results show that the proposed framework incorporating both fault detection, and correction processes for SRGM has a fairly accurate prediction capability.
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an assessment of testing effort dependent Software Reliability growth models
IEEE Transactions on Reliability, 2007Co-Authors: Chinyu Huang, Syyen Kuo, Michael R LyuAbstract:Over the last several decades, many Software Reliability Growth Models (SRGM) have been developed to greatly facilitate engineers and managers in tracking and measuring the growth of Reliability as Software is being improved. However, some research work indicates that the delayed S-shaped model may not fit the Software failure data well when the testing-effort spent on fault detection is not a constant. Thus, in this paper, we first review the logistic testing-effort function that can be used to describe the amount of testing-effort spent on Software testing. We describe how to incorporate the logistic testing-effort function into both exponential-type, and S-shaped Software Reliability models. The proposed models are also discussed under both ideal, and imperfect debugging conditions. Results from applying the proposed models to two real data sets are discussed, and compared with other traditional SRGM to show that the proposed models can give better predictions, and that the logistic testing-effort function is suitable for incorporating directly into both exponential-type, and S-shaped Software Reliability models
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neural network based approaches for Software Reliability estimation using dynamic weighted combinational models
Journal of Systems and Software, 2007Co-Authors: Yushen Su, Chinyu HuangAbstract:Software Reliability is the probability of failure-free Software operation for a specified period of time in a specified environment. During the last three decades, many Software Reliability growth models (SRGMs) have been proposed and analyzed for measuring Software Reliability growth. SRGMs are mathematical models that represent Software failures as a random process and can be used to evaluate development status during testing. However, most of SRGMs depend on some assumptions or distributions. In this paper, we propose an artificial neural-network-based approach for Software Reliability estimation and modeling. We first explain the neural networks from the mathematical viewpoints of Software Reliability modeling. We will show how to apply neural network to predict Software Reliability by designing different elements of neural networks. Furthermore, we will use the neural network approach to build a dynamic weighted combinational model (DWCM). The applicability of proposed model is demonstrated through real Software failure data sets. The results obtained from the experiments show that the proposed model has a fairly accurate prediction capability.
Tadashi Dohi - One of the best experts on this subject based on the ideXlab platform.
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generalized logit regression based Software Reliability modeling with metrics data
Computer Software and Applications Conference, 2013Co-Authors: Daisuke Kuwa, Tadashi DohiAbstract:It is well known that multifactor Software Reliability modeling with Software metrics data is useful to predict the Software Reliability with higher accuracy, because it utilizes not only Software fault count data but also Software testing metrics data observed in the development process. In this paper we extend the existing logit regression-based Software Reliability model by introducing more generalized logistic type functions and improve the goodness-of-fit and predictive performances. In numerical examples with real Software development project data, it is shown that our generalized models can outperform the existing logit regression-based model and the Cox regression-based model significantly.
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towards quantitative Software Reliability assessment in incremental development processes
International Conference on Software Engineering, 2011Co-Authors: Toshiya Fujii, Tadashi Dohi, Takaji FujiwaraAbstract:The iterative and incremental development is becoming a major development process model in industry, and allows us for a good deal of parallelism between development and testing. In this paper we develop a quantitative Software Reliability assessment method in incremental development processes, based on the familiar non-homogeneous Poisson processes. More specifically, we utilize the Software metrics observed in each incremental development and testing, and estimate the associated Software Reliability measures. In a numerical example with a real incremental developmental project data, it is shown that the estimate of Software Reliability with a specific model can take a realistic value, and that the Reliability growth phenomenon can be observed even in the incremental development scheme.
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a multi factor Software Reliability model based on logistic regression
International Symposium on Software Reliability Engineering, 2010Co-Authors: Hiroyuki Okamura, Yusuke Etani, Tadashi DohiAbstract:This paper proposes a multi-factor Software Reliability model based on logistic regression and its effective statistical parameter estimation method. The proposed parameter estimation algorithm is composed of the algorithm used in the logistic regression and the EM (expectation-maximization) algorithm for discrete-time Software Reliability models. The multi-factor model deals with the metrics observed in testing phase (testing environmental factors), such as test coverage and the number of test workers, to predict the number of residual faults and other Reliability measures. In general, the multi-factor model outperforms the traditional Software Reliability growth model like discrete-time non-homogeneous models in terms of data-fitting and prediction abilities. However, since it has a number of parameters, there is the problem in estimating model parameters. Our modeling framework and its estimation method are quite simpler than the existing methods, and are promising for expanding the applicability of multi-factor Software Reliability model. In numerical experiments, we examine data-fitting ability of the proposed model by comparing with the existing multi-factor models. The proposed method provides the similar fitting ability to existing multi-factor models, although the computation effort of parameter estimation is low.
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gompertz Software Reliability model estimation algorithm and empirical validation
Journal of Systems and Software, 2009Co-Authors: Koji Ohishi, Hiroyuki Okamura, Tadashi DohiAbstract:Gompertz curve has been used to estimate the number of residual faults in testing phases of Software development, especially by Japanese Software development companies. Since the Gompertz curve is a deterministic function, the curve cannot be applied to estimating Software Reliability which is the probability that Software system does not fail in a prefixed time period. In this article, we propose a stochastic model called the Gompertz Software Reliability model based on non-homogeneous Poisson processes. The proposed model can be derived from the statistical theory of extreme-value, and has a similar asymptotic property to the deterministic Gompertz curve. Also, we develop an EM algorithm to determine the model parameters effectively. In numerical examples with Software failure data observed in real Software development projects, we evaluate performance of the Gompertz Software Reliability model in terms of Reliability assessment and failure prediction.
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a new paradigm for Software Reliability modeling from nhpp to nhgp
Pacific Rim International Symposium on Dependable Computing, 2008Co-Authors: Tomotaka Ishii, Tadashi DohiAbstract:Non-homogeneous gamma process (NHGP) models with typical Reliability growth patterns are developed for Software Reliability assessment in order to overcome a weak point of the usual non-homogeneous Poisson process (NHPP) models. Though the analytical treatment of NHGPs as stochastic point processes is not so easy in general, they have an advantage to involve the NHPPs as well as a gamma renewal process as special cases, and are rather tractable on parameter estimation by means of the method of maximum likelihood. We perform the goodness-of fit test for several NHGP-based Software Reliability models (SRMs) and compare them with the existing NHPP-based ones. Throughout a numerical example with a real Software fault data, it is shown that the NHGP-based SRMs can provide the better goodness-of-fit performances in earlier testing phases than the NHPP-based ones, but approach to them gradually as the testing time goes on. This implies that our new Software Reliability modeling framework with flexibility can describe better the Software-fault detection phenomenon when the less information on Software fault data is available.
Michael R Lyu - One of the best experts on this subject based on the ideXlab platform.
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estimation and analysis of some generalized multiple change point Software Reliability models
IEEE Transactions on Reliability, 2011Co-Authors: Chinyu Huang, Michael R LyuAbstract:Software typically undergoes debugging during both a testing phase before product release, and an operational phase after product release. But it is noted that the fault detection and removal processes during Software development and operation are different. For example, the fault removal during operation occurs generally at a slower pace than development. In this paper, we derive a powerful, easily deployable technique for Software Reliability prediction and assessment in the testing and operational phases. We first review how several existing Software Reliability growth models (SRGM) based on non- homogeneous Poisson processes (NHPP) can be readily derived from a unified theory. With the unified theory, we further incorporate the concept of multiple change-points, i.e. points in time when the Software environment changes, into Software Reliability modeling. Several models are proposed and discussed under both ideal and imperfect debugging conditions. We estimate the parameters of the proposed models by employing real Software failure data, and give a fair comparison with some existing SRGM. Numerical results show that the proposed models can provide good Software Reliability prediction in the various stages of Software development and operation. Our approach is flexible; we can model various environments ranging from exponential-type to S-shaped NHPP models.
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an assessment of testing effort dependent Software Reliability growth models
IEEE Transactions on Reliability, 2007Co-Authors: Chinyu Huang, Syyen Kuo, Michael R LyuAbstract:Over the last several decades, many Software Reliability Growth Models (SRGM) have been developed to greatly facilitate engineers and managers in tracking and measuring the growth of Reliability as Software is being improved. However, some research work indicates that the delayed S-shaped model may not fit the Software failure data well when the testing-effort spent on fault detection is not a constant. Thus, in this paper, we first review the logistic testing-effort function that can be used to describe the amount of testing-effort spent on Software testing. We describe how to incorporate the logistic testing-effort function into both exponential-type, and S-shaped Software Reliability models. The proposed models are also discussed under both ideal, and imperfect debugging conditions. Results from applying the proposed models to two real data sets are discussed, and compared with other traditional SRGM to show that the proposed models can give better predictions, and that the logistic testing-effort function is suitable for incorporating directly into both exponential-type, and S-shaped Software Reliability models
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Software Reliability engineering a roadmap
International Conference on Software Engineering, 2007Co-Authors: Michael R LyuAbstract:Software Reliability engineering is focused on engineering techniques for developing and maintaining Software systems whose Reliability can be quantitatively evaluated. In order to estimate as well as to predict the Reliability of Software systems, failure data need to be properly measured by various means during Software development and operational phases. Moreover, credible Software Reliability models are required to track underlying Software failure processes for accurate Reliability analysis and forecasting. Although Software Reliability has remained an active research subject over the past 35 years, challenges and open questions still exist. In particular, vital future goals include the development of new Software Reliability engineering paradigms that take Software architectures, testing techniques, and Software failure manifestation mechanisms into consideration. In this paper, we review the history of Software Reliability engineering, the current trends and existing problems, and specific difficulties. Possible future directions and promising research subjects in Software Reliability engineering are also addressed.
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a hierarchical mixture model for Software Reliability prediction
International Conference on Intelligent Computing, 2007Co-Authors: Qian Yin, Ping Guo, Michael R LyuAbstract:It is important to develop general prediction models in current Software Reliability research. In this paper, we propose a hierarchical mixture of Software Reliability models (HMSRM) for Software Reliability prediction. This is an application of the hierarchical mixtures of experts (HME) architecture. In HMSRM, individual Software Reliability models are used as experts. During the training of HMSRM, an Expectation-Maximizing (EM) algorithm is employed to estimate the parameters of the model. Experiments illustrate that our approach performs quite well in the later stages of Software development, and better than single classical Software Reliability models. We show that the method can automatically select the most appropriate lower-level model for the data and performances are well in prediction.
Chenggang Bai - One of the best experts on this subject based on the ideXlab platform.
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does Software Reliability growth behavior follow a non homogeneous poisson process
Information & Software Technology, 2008Co-Authors: Kaiyuan Cai, Chenggang Bai, Tao JingAbstract:It is widely believed in Software Reliability community that Software Reliability growth behavior follows a non-homogeneous Poisson process (NHPP) based on analyzing the behavior of the mean of the cumulative number of observed Software failures. In this paper we present two controlled Software experiments to examine this belief. The behavior of the mean of the cumulative number of observed Software failures and that of the corresponding variance are examined simultaneously. Both empirical observations and statistical hypothesis testing suggest that Software Reliability behavior does not follow a non-homogeneous Poisson process in general, and does not fit the Goel-Okumoto NHPP model in particular. Although this new finding should be further tested on other Software experiments, it is reasonable to cast doubt on the validity of the NHPP framework for Software Reliability modeling. The importance of the work presented in this paper is not only for the new finding which is distinctly different from existing popular belief of Software Reliability modeling, but also for the adopted research approach which is to examine the behavior of the mean and that of the corresponding variance simultaneously on basis of controlled Software experiments.
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an experimental study of adaptive testing for Software Reliability assessment
Journal of Systems and Software, 2008Co-Authors: Kaiyuan Cai, Changhai Jiang, Chenggang BaiAbstract:Adaptive testing is a new form of Software testing that is based on the feedback and adaptive control principle and can be treated as the Software testing counterpart of adaptive control. Our previous work has shown that adaptive testing can be formulated and guided in theory to minimize the variance of an unbiased Software Reliability estimator and to achieve optimal Software Reliability assessment. In this paper, we present an experimental study of adaptive testing for Software Reliability assessment, where the adaptive testing strategy, the random testing strategy and the operational profile based testing strategy were applied to the Space program in four experiments. The experimental results demonstrate that the adaptive testing strategy can really work in practice and may noticeably outperform the other two. Therefore, the adaptive testing strategy can serve as a preferable alternative to the random testing strategy and the operational profile based testing strategy if high confidence in the Reliability estimates is required or the real-world operational profile of the Software under test cannot be accurately identified.
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bayesian network based Software Reliability prediction with an operational profile
Journal of Systems and Software, 2005Co-Authors: Chenggang BaiAbstract:This paper uses a Bayesian network to model Software Reliability prediction with an operational profile. Due to the complexity of Software products and development processes, Software Reliability models need to possess the ability to deal with multiple parameters. A Bayesian network exhibits a strong ability to adapt to problems involving complex variant factors. A special kind of Bayesian network named a Markov Bayesian network has been applied successfully into modeling Software Reliability prediction. However, the existing research did not pay enough attention to the fact that the failure characteristics of many Software systems often depend on the specific operation performed. In this paper, an extended Markov Bayesian network is developed to model Software Reliability prediction with an operational profile. The extended Markov Bayesian network proposed in the paper is focused on discrete-time failure data. Methods to solve the network are proposed, and an example is used to illustrate the utilization of the model.
Shunji Osaki - One of the best experts on this subject based on the ideXlab platform.
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an infinite server queueing approach for describing Software Reliability growth unified modeling and estimation framework
Asia-Pacific Software Engineering Conference, 2004Co-Authors: Tadashi Dohi, Shunji Osaki, Kishor S TrivediAbstract:In general, the Software Reliability models based on the nonhomogeneous Poisson processes (NHPPs) are quite popular to assess quantitatively the Software Reliability and its related dependability measures. Nevertheless, it is not so easy to select the best model from a huge number of candidates in the Software testing phase, because the predictive performance of Software Reliability models strongly depends on the fault-detection data. The asymptotic trend of Software fault-detection data can be explained by two kinds of NHPP models; finite fault model and infinite fault model. In other words, one needs to make a hypothesis whether the Software contains a finite or infinite number of faults, in selecting the Software Reliability model in advance. In this article, we present an approach to treat both finite and infinite fault models in a unified modeling framework. By introducing an infinite server queueing model to describe the Software debugging behavior, we show that it can involve representative NHPP models with a finite and an infinite number of faults. Further, we provide two parameter estimation methods for the unified NHPP based Software Reliability models from both standpoints of Bayesian and nonBayesian statistics. Numerical examples with real fault-detection data are devoted to compare the infinite server queueing model with the existing one under the same probability circumstance.
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Analysis of hypergeometric distribution Software Reliability model
Proceedings 12th International Symposium on Software Reliability Engineering, 2001Co-Authors: Tadashi Dohi, Shunji Osaki, N. Wakana, K.s. TriveditAbstract:The article gives detailed mathematical results on the hypergeometric distribution Software Reliability model (HGDSRM) proposed by Y. Tohma et al. (1989; 1991). In the above papers, Tohma et al. developed the HGDSRM as a discrete-time stochastic model and derived a recursive formula for the mean cumulative number of Software faults detected up to the i-th (>0) test instance in testing phase. Since their model is based on only the mean value of the cumulative number of faults, it is impossible to estimate not only the Software Reliability but also the other probabilistic dependability measures. We introduce the concept of cumulative trial processes, and describe the dynamic behavior of the HGDSRM exactly. In particular, we derive the probability mass function of the number of Software faults detected newly at the i-th test instance and its mean as well as the Software Reliability defined as the probability that no faults are detected up to an arbitrary time. In numerical examples with real Software failure data, we compare several HGDSRMs with different model parameters in terms of least squared sum and show that the mathematical results obtained here are very useful to assess the Software Reliability with the HGDSRM.
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Software Reliability measurement in imperfect debugging environment and its application
Reliability Engineering & System Safety, 1993Co-Authors: Shigeru Yamada, Koichi Tokuno, Shunji OsakiAbstract:Abstract In practice, debugging operations during the testing phase of Software development are not always performed perfectly. In other words, not all the Software faults detected are corrected and removed. Generally, this is called imperfect debugging. In this paper, we discuss a Software Reliability growth model considering imperfect debugging. Defining a random variable representing the cumulative number of faults corrected up to a specified testing time, this model is described by a semi-Markov process. Then, several quantitative measures are derived for Software Reliability assessment in an imperfect debugging environment. The application of this model to optimal Software release problems is also discussed. Finally, numerical illustrations for Software Reliability measurement and optimal Software release policies are presented.
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imperfect debugging models with fault introduction rate for Software Reliability assessment
International Journal of Systems Science, 1992Co-Authors: Koichi Tokuno, Shunji OsakiAbstract:In general it is considered to be unrealistic in Software Reliability modelling to assume that the faults detected by Software testing are perfectly removed without introducing new faults. In this paper we propose two Software Reliability assessment models with imperfect debugging by assuming that new faults are sometimes introduced when the faults originally latent in a Software system are corrected and removed during the testing phase. It is assumed that the fault detection rate is proportional to the sum of the numbers of faults remaining originally in the system and faults introduced by imperfect debugging. These two models are described by a nonhomogeneous Poisson process. Several quantitative measures for Reliability assessment are derived, and the maximum likelihood estimations of unknown model parameters are presented. Finally, numerical examples of Software Reliability analysis based on these two models are shown.