• Journal of Information Technology Applications and Management
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• v.12 no.2
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• pp.1-13
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• 2005

This paper proposes a software-reliability growth model incoporating the amount of testing effort expended during the software testing phase after developing it. The time-dependent behavior of testing effort expenditures is described by a Logistic curve. Assuming that the error detection rate to the amount of testing effort spent during the testing phase is proportional to the current error content, a software-reliability growth model is formulated by a nonhomogeneous Poisson process. Using this model the method of data analysis for software reliability measurement is developed. After defining a software reliability, This paper discusses the relations between testing time and reliability and between duration following failure fixing and reliability are studied. SRGM in several literatures has used the exponential curve, Railleigh curve or Weibull curve as an amount of testing effort during software testing phase. However, it might not be appropriate to represent the consumption curve for testing effort by one of already proposed curves in some software development environments. Therefore, this paper shows that a logistic testing-effort function can be adequately expressed as a software development/testing effort curve and that it gives a good predictive capability based on real failure data.

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

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.1-9
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• 2008

The Logistic cone is studied as a most desirable for the software testing effort. Assuming that the error detection rate to the amount of testing effort spent during the testing phase is proportional to the current error content, a software-reliability growth model is formulated by a nonhomogeneous Poisson process. Using this model the method of data analysis for software reliability measurement is developed. After defining a software reliability, This paper discusses the relations between testing time and reliability and between duration following failure fixing and reliability are studied SRGM in several literatures has used the exponential curve, Railleigh curve or Weibull cure as an amount of testing effort during software testing phase. However, it might not be appropriate to represent the consumption curve for testing effort by one of already proposed curves in some software development environments. Therefore, this paper shows that a logistic testing- effort function can be adequately expressed as a software development/testing effort curve and that it gives a good predictive capability based on real failure data.

• Journal of Information Technology Applications and Management
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• v.11 no.2
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• pp.191-204
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• 2004

Many software reliability growth model(SRGM) have been proposed for past several decades. 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. We consider the methology to evaluate the SRGN using least square estimator(LSE) and maximum likelihood estimator(MLE) for those 4 functions, and then examine parameters applying actual data adopted from real field test of developing S/W.

• Journal of Information Technology Application
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• v.3 no.3
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• pp.91-106
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• 2001

We propose a software-reliability growth model incoporating the amount of uniform and Weibull testing efforts during the software testing phase in this paper. The time-dependent behavior of testing effort is described by uniform and Weibull curves. Assuming that the error detection rate to the amount of testing effort spent during the testing phase is proportional to the current error content, the model is formulated by a nonhomogeneous Poisson process. Using this model the method the data analysis for software reliability measurement is developed. The optimum release time is determined by considering how the initial reliability R($\chi$ 0) would be. The conditions are ($R\chi$ 0)＞$R_{o}$ , $P_{o}$ ＞R($\chi$ 0)＞ $R_{o}$ $^{d}$ and R($\chi$ 0)＜$R_{o}$ $^{d}$ for uniform testing efforts. deal case is $P_{o}$ ＞($R\chi$ 0)＞ $R_{o}$ $^{d}$ Likewise, it is ($R\chi$ 0)$\geq$$R_{o}$ , $R_{o}$ ＞($R\chi$ 0)＞R(eqation omitted) and ($R\chi$ 0)＜R(eqation omitted)for Weibull testing efforts. Ideal case is $R_{o}$ ＞ R($\chi$ 0)＞ R(eqation omitted).

• Journal of Advanced Navigation Technology
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• v.14 no.1
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• pp.119-126
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• 2010

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 eventually 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, We want to study imperfect software testing effort for the logistic testing effort which is thought to be the most adequate in this paper.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.173-182
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• 2004

The research to estimate development effort of software has been progress. But, it is not easy gain that testing data for estimating of development effort. Also, if we get the testing data, it is important that analysis testing data. In this paper, we study the data analysis of software development effort using the 789 software development projects which developed in the 1990's. Software development scale and software development team site are various. Using the characteristic of factor, we have to study characteristic of data and we estimate the development effort step by step. First, we prove the difference of development effort with the 789 project data according to development type, development environment, the development language etc. Also, we execute the crosstabs analysis that team site and function point.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.251-262
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• 2012

Unit testing is one of the test levels, which tests an individual unit or a group of related units. Recently, in Agile Development or Safety-critical System Development, the unit testing plays an important role for the qualities. According to the definition of unit testing, it is supposed to be done by the developers of units. That is because test models for the unit testing refers to the structure of units, and others but its original developers hardly can understand the structures. However, in practice, unit testing is often asked to be done without the original developers. For example, it is when faults are revealed in customer sites and the development team does not exit any more. In this case, instead of original developers, other developers or test engineers take a product and test it. The unit testing done by a non-developer, who is not the original developer, would cause some difficulties or cause more cost. In this paper, we tests an open source, JTopas, as a non-developer, with building test models, implementing test codes, and executing test cases. To fit this experiment to practical testing situations, we designed it based on the practices of unit testing, which were surveyed through SPIN(Software Process Improvement Network). This paper analyzes which part of unit testing done by non-developers needs more effort compared to the unit testing done by original developers. And it concludes that Agile Development contributes on reducing the extra effort caused by non-developers, since it implements test codes first before developing source code. That means all the units have already included their own tests code when they are released.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.444-447
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• 2006

We propose software reliability growth model, considering testing effort resource during testing stage of S/W, and compare the time dependent testing effort resource behavior in this paper. We develop the data technology method for the S/W reliability measure. We study in detail between the time elapse and reliability. Also, we determine the optimum release time which meets the target reliability. We decide optimum release time for each condition how the reliability is good before testing after development.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.5
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• pp.61-73
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• 2009

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 estimater and maximum likelihood estimater for those 4 functions, and then examine parameters applying actual data adopted from real field test of developing S/W.