• The KIPS Transactions:PartD
• /
• v.9D no.4
• /
• pp.659-666
• /
• 2002

Park, Seo and Kim (12) developed the input domain-based SRGM, which was able to quantitatively assess the reliability of a software system during the testing and operational phases. They assumed perfect debugging during testing and debugging phase. To make this input domain-based SRGM more realistic, this assumption should be relaxed. In this paper we generalize the input domain-based SRGM under imperfect debugging. Then its statistical characteristics are investigated.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.3
• /
• pp.615-621
• /
• 1999

On the aspect of on-time and development resource use, it is very important to predict the software release time during the software development process. In this paper, we present the optimal release problem based on the evaluation indicator and cost evaluation. And also we show the optimal release point considered with both of them. We applied the Exponential Software Reliability Growth Model(E-SRGM) and Testing-effort dependent Software Reliability Growth Model(Te-SRGM) and decided the software release time according to software reliability indicator. As a result of two models comparison, we verify the Te-SRGM is more adopted in our switching system software.

• Proceedings of the Korean Information Science Society Conference
• /
• 2003.10b
• /
• pp.328-330
• /
• 2003

최근 운영시스템. 제어프로그램, 적용프로그램과 같은 여러 가지 소프트웨어 시스템이 더욱 더 복잡화 및 대형화되고 있기 때문에 신뢰도가 높은 소프트웨어 시스템을 개발하는 일이 매우 중요하며, 따라서 소프트웨어 제품 개발에 있어서 소프트웨어의 신뢰도가 핵심사항이라고 할 수 있다. 소프트웨어가 주어진 시간동안 고장이 발생하지 않을 확률 즉, 신뢰도는 소프트웨어의 테스트 과정을 계속하면서 반복해서 결함을 발견 및 수정하면 더욱 더 향상될 것이다. 그러한 검출현상을 설명해주는 소프트웨어 신뢰도 모델을 소프트웨어 신뢰도 성장모델(SRGM)이라 한다.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.18 no.33
• /
• pp.119-122
• /
• 1995

Application of the PWFRM and SRGM for software reliability Prediction offers not only the judging base of model but also themselves with good applicabilty as easy-to-use tool.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.12
• /
• pp.3490-3499
• /
• 1999

The hyper-geometric distribution software reliability growth model (HGDM) was recently developed and successfully applied to the problem of estimating the number of initial faults residual in a software at the beginning of the test-and-debug phase. Though the HGDM is a time-domain software reliability growth model(SRGM), it is not possible to compare the HGDM with other time-domain SRGMs. Furthermore the usual software reliability can not be computed. These drawbacks are derived from fact that the HGDM is not described in terms of the execution time. Thus we develop a continuous-time HGDM with binomial sensitivity factor in order to remove these drawbacks. Statistical characteristics of the suggested model are studied and its applicability is then examined by analyzing real test data sets. It is empirically shown that the continuous-time HGDM with binomial sensitivity factor can be used as an alternative to the current HGDM.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2002.11a
• /
• pp.473-479
• /
• 2002

NHPP에 근거한 SRGM을 구성하는 새로운 안을 제시한다. 본 논문의 주요 초점은 소프트웨어 신뢰도모델링에서 효과적인 파라미터분해기법을 제공하는 것이다. 이는 테스트노력과 결함검출비를 동시에 고려하는 것이다. 일반적으로, 소프트웨어결함검출/제거메카니즘은 이전의 검출/제거결함과 테스트노력을 어떻게 활용하느냐에 달려있다. 실제 현장 연구로부터 우리는 테스트노력소모패턴을 추론하여 FDR의 경향을 예측할 수 있을 것으로 생각된다. 결함검출이 증가, 감소 및 일정한 것 등 광범위에 걸쳐서 나타나는 경향을 잡아내는 고유의 융통성을 가지는 하나의 시변수집합인 FDR모델에 근거한 테스트노력을 개발하였다. 이 스킴은 구조에 융통성이 있어서 여러 가지 테스트노력을 고려하여 광범위한 소프트웨어 개발 환경을 모델화할 수 있다 본 논문에서는 FDR을 기술하고, 관련된 테스트 행위를 이러한 새로운 모델링접근법에 연합시킬 수 있다. 우리의 모델과 그리고 이것과 관련된 파라미터 분해기법을 적용한 것을 여러 가지 소프트웨어 프로젝트에서 도출한 실제 데이터집합을 통하여 시연한다. 분석결과에 의하면 SRGM에 관한 테스트노력과 FDR을 결합하기 위한 제안된 구조가 상당히 정확한 예측능력을 보여주고 있으며, 실제 수명상황을 좀더 정대하게 설명해 준다. 이 기법은 광범위한 소프트웨어시스템에 쓰일 수 있다.

• International Journal of Reliability and Applications
• /
• v.4 no.1
• /
• pp.1-12
• /
• 2003

Many software reliability growth models (SRGM's) based on a nonhomogeneous Poisson process (NHPP) have been proposed by many researchers. Most of the SRGM's which have been proposed up to the present treat the event of software fault-detection in the testing and operational phases as a counting process. However, if the size of the software system is large, the number of software faults detected during the testing phase becomes large, and the change of the number of faults which are detected and removed through debugging activities becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. Therefore, in such a situation, we can model the software fault-detection process as a stochastic process with a continuous state space. In this paper, we propose a new software reliability growth model describing the fault-detection process by applying a mathematical technique of stochastic differential equations of an Ito type. We also compare our model with the existing SRGM's in terms of goodness-of-fit for actual data sets.

• KIPS Transactions on Software and Data Engineering
• /
• v.4 no.1
• /
• pp.9-18
• /
• 2015

Software Reliability Growth Models (SRGMs) are useful for determining the software release date or additional testing efforts by using software failure data. It is not appropriate for a SRGM to apply to all software. And besides a large number of SRGMs have already been proposed to estimate software reliability measures. Therefore selection of an optimal SRGM for use in a particular case has been an important issue. The existing methods for selecting a SRGM use the entire collected failure data. However, initial failure data may not affect the future failure occurrence and, in some cases, it results in the distorted result when evaluating the future failure. In this paper, we suggest a method for selecting a SRGM based on the evaluation goodness-of-fit using partial data. Our approach uses partial data except for inordinately unstable failure data in the entire failure data. We will find a portion of data used to select a SRGM through the comparison between the entire failure data and the partial failure data excluded the initial failure data with respect to the predictive ability of future failures. To justify our approach this paper shows that the predictive ability of future failures using partial data is more accurate than using the entire failure data with the real collected failure data.

• Proceedings of the Korea Society of Information Technology Applications Conference
• /
• 2003.06a
• /
• pp.21-26
• /
• 2003

• International Journal of Reliability and Applications
• /
• v.6 no.2
• /
• pp.87-99
• /
• 2005

Coverage-based software reliability growth models (SRGMs) have been developed and successfully applied in practice. Performance of a coverage-based SRG M depends on the coverage function employed by the SRGM. When the coverage function represents the coverage growth behavior well irrespective of type of the testing profile the corresponding coverage-based SRGM is expected to be widely applicable. This paper first conducts a study of selecting the most representative coverage functions among the available coverage functions. Then their performances are empirically evaluated and compared. The result provides a foundation for developing widely applicable coverage-based SRGMs and monitoring the progress of a testing process.