The KIPS Transactions:PartD (정보처리학회논문지D)

Korea Information Processing Society (한국정보처리학회)
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Sigmoid Curve Model for Software Test-Effort Estimation

소프트웨어 시험 노력 추정 시그모이드 모델

  • 이상운 (국립원주대학 여성교양과)
  • Published : 2004.08.01
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Abstract

Weibull distribution Iincluding Rayleigh and Exponential distribution is a typical model to estimate the effort distribution which is committed to the software testing phase. This model does not represent standpoint that many efforts are committed actually at the test beginning point. Moreover, it does not properly represent the various distribution form of actual test effort. To solve these problems, this paper proposes the Sigmoid model. The sigmoid function to be applicable in neural network transformed into the function which properly represents the test effort of software in the model. The model was verified to the six test effort data which were got from actual software projects which have various distribution form and verified the suitability. The Sigmoid model nay be selected by the alternative of Weibull model to estimate software test effort because it is superior than the Weibull model.

소프트웨어 시험단계에 투입되는 노력의 분포를 추정하는 대표적인 모델로 Weibull 분포(Rayleigh와 지수분포 포함)가 있다. 이 모델은 시험 시작시점에서 실제로 많은 노력이 투입되는 점을 표현하지 못한다. 또한 다양한 형태를 갖고 있는 실제 시험 노력의 분포를 적절히 표현하지 못하고 있다. 이러한 문제점을 해결하기 위해 본 논문은 시그모이드 모델을 제안하였다. 신경망 분야에서 적용되고 있는 시그모이드 함수로부터 소프트웨어 시험 노력을 적절히 표현할 수 있도록 함수 형태를 변형시켰다 제안된 모델은 다양한 분포 형태를 보이고 있는 실제 수행된 소프트웨어 프로젝트로부터 얻어진 6개의 시험 노력 데이터에 적용하여 적합성을 검증하였다. 제안된 시그모이드 모델은 기존의 Weibull 모델보다 성능이 우수하여 소프트웨어 시험노력을 추정하는데 있어 와이블 모델의 대안으로 채택될 수 있을 것이다.

Keywords

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