로그선형 학습효과에 근거한 소프트웨어 신뢰모형에 관한 통계적 공정관리 접근 방법 비교 = The Assessing Comparative Study for Statistical Process Control of Software Reliability Model Based on Log Linear Learning Effects

다국어 초록 (Multilingual Abstract)

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem.
It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on finite failure model and non-homogeneous Poisson Processes (NHPP). For someone making a decision about when to market software, the conditional failure rate is an important variables. The finite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outlier, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. Statistical process control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, proposed a control mechanism based on NHPP using mean value function of log linear learning effects property.

Key Words : Learning Effects, Non-Homogeneous Poisson Process, Statistical Process Control.