Software Reliability Evaluation Research Articles

불완전 결함 발견과 구문 반복 실행을 고려한 커버리지 기반 신뢰성 성장 모형

최근 소프트웨어 신뢰성을 평가하기 위해 신뢰성 측도와 커버리지 간의 관계가 연구되고 있다. 특히 커버리지에 기반한 소프트웨어 신뢰성 성장 모델에서 평균치 함수는 소프트웨어의 신뢰성 성장을 나타내는데 매우 중요한 역할을 한다. 본 논문은 커버리지에 기반한 기존 모형들의 문제점을 평균치 함수와 그 모형이 근거하는 가정을 바탕으로 파악하고, 그 문제점을 해결하기 위한 새로운 평균치 함수를 제안한다. 제안된 새로운 평균치 함수는 불완전 결함 발견과 구문의 반복 실행이 허용되는 일반적인 테스팅 환경에서 도출된 결과이다. 마지막으로 실제 데이터에 제안된 모형을 적용하여 그 성능을 평가한다. Recently relationships between reliability measures and the coverage have been developed for evaluation of software reliability. Particularly the mean value function of the coverage-based software reliability growth model is important because of its key role in rep-resenting the software reliability growth. In this paper, we first review the problems of the existing mean value functions with respect to the assumptions on which they are based. Then a new mean value function is proposed. The new mean value function is developed for a general testing environment in which imperfect fault detection and repeated construct execution are allowed. Finally performance of the proposed model is empirically evaluated by applying it to a real data set.

A reliability growth model for modular software

AbstractThis paper presents a probabilistic model for evaluating the reliability of software having modular structure. In particular, it proposes a new reliability‐evaluating model based on the structure of software by integrating the Jelinski–Moranda model, which is the most basic black box model, and the Littlewood model, which is a representative white box model. The performance of the proposed model is compared and studied in reference to the benchmark model of Cheung and the dependency of the architecture and the reliability growth in software reliability evaluation is discussed. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 87(5): 43–53, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20075

SREPT: software reliability estimation and prediction tool

Several tools have been developed for the estimation of software reliability. However, they are highly specialized in the approaches they implement and the particular phase of the software life-cycle in which they are applicable. There is an increasing need for a tool that can be used to track the quality of a software product during the software life-cycle, right from the architectural phase all the way up to the operational phase of the software. Also the conventional techniques for software reliability evaluation, which treat the software as a monolithic entity, are inadequate to assess the reliability of heterogeneous systems, which consist of a large number of globally distributed components. Architecture-based approaches are essential to assess the reliability and performance of such systems. This paper presents the high-level design of a software reliability estimation and prediction tool (SREPT), that offers a unified framework consisting of techniques (including the architecture-based approach) to assist in the evaluation of software reliability during all phases of the software life-cycle.

Qualitative and quantitative reliability assessment

During the development of a software system, the supplier must efficiently monitor the development activities; comply with the delivery schedule; predict when a specified level of reliability will be reached, or at least check how well the software will satisfy the customer's requirements; and reduce maintenance efforts. On the other hand, the customer needs a reliable product, delivered on time and at the lowest price. Our method helps reach these goals. It is based on the analysis and evaluation of software reliability by processing failure data collected on a software product during its development and operation. Traditionally, system reliability efforts have not focused on software reliability. We believe that failure prediction can be improved if software reliability modeling is integrated into an overall approach. The method we propose is based on the combined use of descriptive analyses, trend analyses, and reliability models to control testing activities, evaluate software reliability, and plan maintenance.

Improving Software Quality in a Space Application

Computerized systems are getting more and more used in spacecraft, both for mission control and measurement equipment control. But space missions require high investments and sometimes cannot be delayed or repeated because are related to special natural events. For these reasons a computerized system failure could produce irreparable consequences as in the Phobos I case. In this paper an activity of software quality improvement and software reliability evaluation is presented. The activity is presently in progress and concerns software that will be used to control a measurement equipment during a Russian-European space mission on Mars. A brief description of the mission and of the measurement equipment is given. Then the adopted methodologies of fault avoidance, and detection and of failure detection and contain-ment are analyzed together with their selection criteria. Finally the paper presents the software reliability evaluation activity that will be performed using an experimental model especially developed for critical application.

A software reliability evaluation tool: Sret

AbstractThe evaluation of reliability is very important in developing software efficiently. This paper describes the construction of a software development‐assist tool, which executes the reliability evaluation of the software quantitatively in the testing phase. In this tool, the following models are employed for the analysis of the software error data. The stochastic models based on nonhomogeneous Poisson processes are exponential, delayed S‐shaped and inflection S‐shaped software reliability growth types. The deterministic regression models are logistic and Gomperz curve models. The procedure for evaluating the software reliability is as follows: The data observed in the testing phase are read in. The unknown parameters in each model are estimated, and the goodness‐of‐fit test of the estimated result is examined. The results of the estimation for the reliability evaluation measures as well as the observed data are displayed in the form of curves. The forementioned procedures are implemented in a program package, using BASIC language operating on MS‐DOS. Using the software reliability evaluation tool developed in this study, an executing example is presented based on the actually observed software error data.

A statistical methodology for the study of the software failure process and its application to the ARGOS center

The authors propose a stepwise statistical methodology for the study of operating system reliability and associated tools. An example of the application of this method for the ARGOS data processing center of France's CNES is presented. It is shown that each evaluation of software reliability is considered as a special case. Two major consequences are that the reliability models need improvement and that every evaluation must be supported by a statistical analysis of the product and process characteristics.

A discrete software reliability growth model with two types of errors

AbstractAs the social missions of computer systems increase, it becomes more important to develop high‐reliability software. For this reason software systems are tested repeatedly to remove latent errors in the testing phase during software development. One of the most interesting methods is to evaluate software reliability by using test data observed in the error‐detection process. In this paper we model the error‐detection process as a stochastic model and introduce several effective measures to evaluate software reliability. In modeling we use the number of test run trials as a unit of the error‐detection period and assume that the cumulative number of software errors detected in an arbitrary testing time interval is a nonhomogeneous Poisson process. Further, we consider the difficulty of error detection because the reliability of software is evaluated from the characteristics and frequency of the errors. Applying the model to the actual test data, we perform the goodness‐of‐fit test and infer the software reliability measures. Finally, we discuss an optimum software release problem based on the software reliability index.

Software Reliability Growth Models with Testing-Effort

Many software reliability growth models have been proposed in the past decade. Those models tacitly assume that testing-effort expenditures are constant throughout software testing. This paper develops realistic software reliability growth models incorporating the effect of testing-effort. The software error detection phenomenon in software testing is modeled by a nonhomogeneous Poisson process. The software reliability assessment measures and the estimation methods of parameters are investigated. Testing-effort expenditures are described by exponential and Rayleigh curves. Least-squares estimators and maximum likelihood estimators are used for the reliability growth parameters. The software reliability data analyses use actual data. The software reliability growth models with testing-effort can consider the relationship between the software reliability growth and the effect of testing-effort. Thus, the proposed models will enable us to evaluate software reliability more realistically.

Software Reliability Growth Modeling: Models and Applications

This paper summarizes existing software reliability growth models (SRGM's) described by nonhomogeneous Poisson processes. The SRGM's are classified in terms of the software reliability growth index of the error detection rate per error. The maximum-likelihood estimations based on the SRGM's are discussed for software reliability data analysis and software reliability evaluation. Using actual software error data observed by software testing, application examples of the existing SRGM's are illustrated.

On the Uncertainty in the Correctness of Computer Programs

The use of digital computers in critical process control systems requires the formal assessment of the system reliability. Failures can be due to either component malfunctions or design faults. Only the latter are relevant in evaluating software reliability. Although it is preferable to prove whether the program meets its specification, this is not yet practical for real-time control programs. Further, the specification itself can be incorrect or incomplete due to the complex requirements.