• Journal of the Korea Computer Industry Society
• /
• v.4 no.4
• /
• pp.553-560
• /
• 2003

Can presume number of software failure or remaining fault that is expected with test data that is collected by decided time using SRGM that is studied until present. Therefore, can forecast software reliability achievement degree and software reliability use step. But, reliability evaluation according to if choose any model can change. Therefore, we present SRGM that consider test cost to error detection and error delete cost as SRGM that consider error delete cost in this research. Using this SRGM, can presume number of remaining fault in software, reliability and optimal release time.

• Journal of the Korea Society of Computer and Information
• /
• v.3 no.2
• /
• pp.1-6
• /
• 1998

The evaluation of reliability is very important in the development process of software. There may be lack of trustfulness on the results that come from the analysis and evaluation of reliability of softwares which do not divide the test phases. At this point, this article studies how to evaluate the reliability dividing the test phases in order to settle the these problems. In doing so, I apply the fault data to be found in Unit Test, Integration Test, Validation Test and System Test to SRGM(Software Reliability Growth Model), Exponential SRGM, Delayed S-shaped SRGM and Inflection S-shaped SRGM. The result is that Inflection S-shaped is best suitable with Unit Test Delayed S-shaped is best suitable with Integration and Validation Test, and Exponential SRGM is best suitable with System test. In this respect, I can show that the results of this study on parameter estimation, difference square summation, number of fault remained is superior to the established methods.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2003.05c
• /
• pp.1741-1744
• /
• 2003

본 논문에서는 NHPP 에 근거한 N 버전 프로그래밍 시스템의 SRGM 을 제안한다. 비록 많은 연구 논문에서 NVP, 시스템 신뢰도에 대해서 연구노력을 기울여 왔지만 그들 대부분이 안정된 신뢰도에 대해서만 고려해 왔다. 테스트 및 디버깅 동안 결함이 발견되면 디버깅 노력은 결함을 제거하는데 집중된다. 소프트웨어가 너무 복잡하므로 이러한 결함을 성공적으로 제거한다는 것이 쉽지 않으며, 또 다른 새로운 결함이 소프트웨어에 도입될 수도 있다. 일반화된 NHPP 모델을 NVP 시스템에 적용하여 새로운 NVP-SRGM이 수립된다. 제어시스템에 대한 단순화된 소프트웨어 제어에서 이러한 새로운 소프트웨어 신뢰도 모델을 어떻게 적용하는지를 보여주고 있다. 소프트웨어 신뢰도평가에 s 신뢰도 구간을 준비하였다. 이 소프트웨어 신뢰도 모텔은 신뢰도를 평가하는데 쓰일 수가 있어서 NVP 시스템의 성능을 예측하는데 쓰일 수 있다. 일반적인 산업사회에 적용하여 상용화하기 위해서는 내결함 소프트웨어의 신뢰도를 정량화하기 위해 제안된 NVP-SRGM을 충분히 인증하는데 좀더 적용이 필요하다. NVP 신뢰도 성장 모델링을 하는 이러한 종류의 첫 모델로서 제안된 NVP-SRGM은 독립 신뢰도 모델의 단점을 극복하는데 쓰일 수 있다. 이는 독립적인 모델보다 더욱 더 정확하게 시스템 신뢰도를 예측할 수 있으며, 언제 테스트를 중단해야 하는가를 결정하는 데에도 쓰일 수 있으며, 이는 NVP 시스템 개발 수명주기 단계를 테스트 및 디버깅함에 있어서 핵심 질문사항이다.

• Proceedings of the Korean Institute of Communication Sciences Conference
• /
• 2004.07a
• /
• pp.357-357
• /
• 2004

• Journal of Information Technology Applications and Management
• /
• v.11 no.4
• /
• pp.87-94
• /
• 2004

The software reliability growth model(SRGM) has been developed in order to estimate such reliability measures as remaining fault number, failure rate and reliability for the developing stage software. Almost of them assumed that the faults detected during testing were evetually removed. Namely, they have studied SRGM based on the assumption that the faults detected during testing were perfectly removed. The fault removing efficiency. however. IS imperfect and it is widely known as so in general. It is very difficult to remove detected fault perfectly because the fault detecting is not easy and new error may be introduced during debugging and correcting. Therefore, the fault detecting efficiency may influence the SRGM or cost of developing software. It is a very useful measure for the developing software. much helpful for the developer to evaluate the debugging efficiency, and, moreover, help to additional workloads necessary. Therefore. it is very important to evaluate the effect of imperfect dubugging in point of SRGM and cost. and may influence the optimal release time and operational budget. I extent and study the generally used reliability and cost models to the imperfect debugging range in this paper.

• Journal of Korean Society for Quality Management
• /
• v.37 no.4
• /
• pp.16-22
• /
• 2009

The inflection S-shaped software reliability growth model (SRGM) proposed by Ohba(1984) is one of the most commonly used models and has been discussed by many authors. The main purpose of this paper is to estimate the parameters of Ohba's SRGM within the Bayesian framework by applying the Markov chain Monte Carlo techniques. While the maximum likelihood estimates for these parameters are well known, the Bayesian method for the inflection S-shaped SRGM have not been discussed in the literature. The proposed methods can be quite flexible depending on the choice of prior distributions for the parameters of interests. We also compare the Bayesian methods with the maximum likelihood method numerically based on the real data.

• Journal of KIISE
• /
• v.42 no.12
• /
• pp.1551-1560
• /
• 2015

Software Reliability Growth Models (SRGMs) are used to quantitatively evaluate software reliability and to determine the software release date or additional testing efforts using software failure data. Because a single SRGM is not universally applicable to all kinds of software, the selection of an optimal SRGM suitable to a specific case has been an important issue. The existing methods for SRGM selection assess the goodness-of-fit of the SRGM in terms of the collected failure data but do not consider the accuracy of future failure predictions. In this paper, we propose a method for selecting SRGMs using the trend of failure data and failure prediction ability. To justify our approach, we identify problems associated with the existing SRGM selection methods through experiments and show that our method for selecting SRGMs is superior to the existing methods with respect to the accuracy of future failure prediction.

• Communications for Statistical Applications and Methods
• /
• v.15 no.6
• /
• pp.875-881
• /
• 2008

This paper first discusses the existing non-homogeneous Poisson process(NHPP) software reliability growth model(SRGM) frameworks with respect to capability of representing software reliability growth phenomenon. As an enhancement of representational capability a new general coverage-based NHPP SRGM framework is developed. Issues associated with application of the new framework are then considered.

• Journal of Korean Society for Quality Management
• /
• v.26 no.3
• /
• pp.130-140
• /
• 1998

Studies present a guide to parameter estimation of software reliability models using SAS JMP. In this paper, we consider only software reliability growth model(SRGM), where mean value function has a S-shaped growth curve, such as Yamada et al. model, and ohba inflection model. Besides these stochastic SRGM, deterministic SRGM's, by fitting Logistic and Gompertz growth curve, have been widely used to estimate the error content of software systems. Introductions or guide lines of JMP are concerned. Estimation of parameters of Yamada et al. model and Logistic model is accomplished by using JMP. The differences between Yamada et al. model and Logistic model is accomplished by using JMP. The differences between Yamada et al. model and Logistic model is discussed, along with the variability in the estimates or error sum of squares. This paper have shown that JMP can be an effective tool I these research.

• Journal of Information Technology Applications and Management
• /
• v.13 no.4
• /
• pp.1-12
• /
• 2006

Many software reliability growth models(SRGM) have been proposed since the software reliability issue was raised in 1972. The technology to estimate and grow the reliability of developing S/W to target value during testing phase were developed using them. Most of these propositions assumed the S/W debugging testing efforts be constant or even did not consider them. A few papers were presented as the software reliability evaluation considering the testing effort was important afterwards. The testing effort forms which have been presented by this kind of papers were exponential, Rayleigh, Weibull, or logistic functions, and one of these 4 types was used as a testing effort function depending on the S/W developing circumstances. I propose the methology to evaluate the SRGM using least square estimator and maximum likelihood estimator for those 4 functions, and then examine parameters applying actual data adopted from real field test of developing S/W.