The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
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A Mechanism to Determine Method Location among Classes using Neural Network

신경망을 이용한 클래스 간 메소드 위치 결정 메커니즘

  • 정영애 (단국대학교 대학원 전자계산학과) ;
  • 박용범 (단국대학교 전자컴퓨터학부)
  • Published : 2006.10.30
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Abstract

There have been various cohesion measurements studied considering reference relation among attributes and methods in a class. Generally, these cohesion measurement are camed out in one class. If the range of reference relation considered are extended from one class to two classes, we could find out the reference relation between two classes. Tn this paper, we proposed a neural network to determine the method location. Neural network is effective to predict output value from input data not to be included in training and generalize after training input and output pattern repeatedly. Learning vector is generated with 30-dimensional input vector and one target binary values of method location in a constraint that there are two classes which have less than or equal to 5 attributes and methods The result of the proposed neural network is about 95% in cross-validation and 88% in testing.

클래스의 속성과 메소드 참조관계를 고려한 응집도 측정 방법은 다양한 형태로 연구되어 왔다 이러한 응집도의 측정 방법은 일반적으로 하나의 클래스에서 이루어진다. 단일 클래스에서 두 개의 클래스로 참조 범위를 확장하면 두 클래스 사이에 발생할 수 있는 참조관계를 알 수 있다. 본 논문에서는 메소드의 위치 결정을 위하여 신경망 학습모델을 제안하였다. 신경망은 입출력 패턴에 대한 반복학습 후, 학습 패턴에 포함되지 않았던 입력 패턴의 목적 값을 예측하고, 일반화(generalization)하는데 효과적이다. 두 개의 클래스 안의 속성과 메소드를 5개 이하로 제한하였고, 학습 벡터는 30개의 이진 값으로 구성된 입력 벡터와 메소드 위치 결정 값인 이진 목적 값으로 생성되었다. 제안된 신경망은 교차검증에서 약 95%, 테스트 데이터에 대해서는 약88%의 예측율을 보였다.

Keywords

References

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