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A Case Study on Function Point Method applying on Monte Carlo Simulation in Automotive Software Development

  • Do, Sung Ryong (Industry Academy Cooperation Foundation, SangMyung University)
  • Received : 2020.05.06
  • Accepted : 2020.06.19
  • Published : 2020.06.30

Abstract

Software development activities are influenced by stochastic theory rather than deterministic one due to having process variability. Stochastic methods factor in the uncertainties associated with project activities and provides insight into the expected project outputs as probability distributions rather than as deterministic approximations. Thus, successful software projects systematically manage and balance five objectives based on historical probability: scope, size, cost, effort, schedule, and quality. Although software size estimation having much uncertainty in initial development has traditionally performed using deterministic methods: LOC(Lines Of Code), COCOMO(COnsructive COst MOdel), FP(Function Point), SLIM(Software LIfecycle Management). This research aims to present a function point method based on stochastic distribution and a case study based on Monte Carlo Simulation applying on an automotive electrical and electronics system software development. It is expected that the result of this paper is used as guidance for establishing of function point method in organizations and tools for helping project managers make decisions correctly.

소프트웨어 개발은 다양한 프로세스 변동을 포함하기 때문에, 결정론적 이론 보다는 확률론적 이론에 더 영향을 많이 받는다. 확률론적 방식은 결정론적 방식보다 프로젝트 활동과 관련된 불확실을 고려하고, 예상되는 결과에 대해서 확률 분포로 접근하는 장점이 있다. 그러므로 소프트웨어 프로젝트를 성공하기 위해서는 확률 분포에 기반하여 범위, 규모, 비용, 공수, 일정 그리고 품질 목표를 체계적으로 관리해야 한다. 소프트웨어 규모 산정은 불확실성이 큰 개발 초기의 활동임에도 불구하고, LOC, COCOMO, FP, SLIM과 같은 결정론적 산정 방식으로 수행되고 있다. 본 연구에서는 확률적 분포 기반의 기능 점수 프로세스를 수립하고, 효과를 검증하기 위해 몬테카를로 시뮬레이션 기반의 자동차 전기전자 제어시스템 소프트웨어 개발에 적용한 사례를 제시한다. 본 연구 결과가 조직 내 기능 점수 프로세스를 수립하기 위한 가이드 및 관리자들의 정확한 의사결정 도구로 활용될 것으로 기대한다.

Keywords

References

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