• Proceedings of the Korean Statistical Society Conference
• /
• 2000.11a
• /
• pp.57-64
• /
• 2000

In this paper we consider a Markovian perfect debugging model for which the software failure is caused by two types of faults, one which is easily detected and the other which is difficult to detect. When a failure occurs, a perfect debugging is immediately performed and consequently one fault is reduced from fault contents. We also treat the debugging time as a variable to develop a new debugging model. Several measures, including the distribution of first passage time to the specified number of removed faults, are also obtained using the proposed debugging model, Numerical examples are provided for illustrative purposes.

• Communications for Statistical Applications and Methods
• /
• v.18 no.6
• /
• pp.741-750
• /
• 2011

In this paper, we derive an software mixed debugging model based on a Markov process, assuming that the length of time to perform the debugging is random and its distribution may depend on the fault type causing the failure. We assume that the debugging process starts as soon as a software failure occurs, and either a perfect debugging or an imperfect debugging is performed upon each fault type. One type is caused by a fault that is easily corrected and in this case, the perfect debugging process is performed. An Imperfect debugging process is performed to fix the failure caused by a fault that is difficult to correct. Distribution of the first passage time and working probability of the software system are obtained; in addition, an availability function of a software system which is the probability that the software is in working at a given time, is derived. Numerical examples are provided for illustrative purposes.

• The Korean Journal of Applied Statistics
• /
• v.21 no.6
• /
• pp.997-1006
• /
• 2008

This paper proposes a new model by combining an infinite-server queueing model for multi-task processing software system with a perfect debugging model based on Markov process with two types of faults suggested by Lee et al. (2001). We apply this model for module and integration testing in the testing process. Also, we compute several measure, such as the expected number of tasks whose processes can be completed and the task completion probability are investigated under the proposed model.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.30 no.11A
• /
• pp.987-994
• /
• 2005

Most of the proposed SRGMs are required to perfect debugging based on removal of defect as soon as the detection of defects in system tests. But the detected defects are corrected after few days as a fixed time or induced new fault in software under the imperfect debugging environments. Solving these problems, we discussed that the formal software reliability model considered testing-effort for the fault detection and correction of software defects, and then using this model we have estimated of the software reliability closed to practical conditions.

• 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.7 no.4
• /
• pp.1103-1111
• /
• 2000

The hyper-geometric distribution software reliability growth model (HGDM) usually assumes that all the software faults detected are perfectly removed without introducing new faults. However, since new faults can be introduced during the test-and-debug phase, the perfect debugging assumption should be relaxed. In this context, Hou, Kuo and Chang [7] developed a modified HGDM for imperfect debugging environment, assuming tat the learning factor is constant. In this paper we extend the existing imperfect debugging HGDM for tow respects: introduction of random sensitivity factor and allowance of variable learning factor. Then the statistical characteristics of he suggested model are studied and its applications to two real data sets are demonstrated.

• Journal of Korean Institute of Industrial Engineers
• /
• v.18 no.1
• /
• pp.37-45
• /
• 1992

Common assumption we frequently encounter in early models of software reliability is that no new faults are introduced during the fault removal process. In real life, however, there are situations in which new faults are introducted as a result of imperfect debugging. This study alleviating this assumption by introducting the probability of perfect error-correction is an extension of Littlewood's work. In this model, the system reliability, failure rates, mean time to failure and average failure frequency are obtained. Here, when the probability of perfect error-correction is one, the results appear identical with those of the previous studies. In the respect that the results of previous studies are special cases of this model, the model developed can be considered as a generalized one.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.54 no.6
• /
• pp.396-402
• /
• 2005

The software reliability growth model(SRGM) has been developed in order to evaluate such measures as remaining fault number, fault rate, and reliability for the developing stage software. Most of the study literatures assumed that this detecting efficiency was perfect. However the actual fault detecting is generally imperfect, and widely known to many persons. It is not easy to develop and remove the fault existing in the software because the fault finding is difficult, and the exact solving method also not easy, and new fault may be introduced depending on the tester's capability. There, the fault removing efficiency influences the software reliability growth or developing cost of software. It is a very useful measure throughout the developing stage, much helpful for the developer to evaluate the debugging efficiency, and evaluate additional workload. Hence, the study for the imperfect debugging is important in point of software reliability and cost. This paper proposes that the fault debugging is imperfect and new fault may be introduced for the developing software during the developing stage.

• Communications for Statistical Applications and Methods
• /
• v.17 no.6
• /
• pp.891-898
• /
• 2010

In real software development fields, software is unified by several modules that are developed before the software testing period. For the evaluation of software task processing performance, this paper considers the software imperfect debugging model that is proposed by Lee and Park (2003) and presents the measures of a unified software, such as the completion probability of a task which is completed in a time interval and the expected number of the completed tasks. In addition, we suggest a software release policy that satisfies the required level of the expected perfect debugging, completion probability, and availability.