Estimation of Failure Count Data using Confidence Interval
R. Satya Prasad1, V. Goutham2, N. Pawan Kumar3
1Dr. R.Satya Prasad, Associate Professor, Department of Computer Science & Engineering, Acharya Nagarjuna University, Guntur, (A.P), India.
2V. Goutham, Associate Professor, Department of Computer Science and Engineering, St. Mary Group of Institutions, Hyderabad (Telangana), India.
3N. Pawan Kumar, Assistance Professor, Department of Computer Science and Engineering, St. Mary Group of Institutions, Hyderabad (Telangana), India.
Manuscript received on 10 May 2013 | Revised Manuscript received on 18 May 2013 | Manuscript Published on 30 May 2013 | PP: 188-191 | Volume-2 Issue-6, May 2013 | Retrieval Number: F0834052613/13©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A confidence interval gives an estimated range of values which is likely to include an unknown population parameter, the estimated range being calculated from a given set of sample data. Confidence intervals are usually calculated so that this percentage is 95%, Confidence limits are the lower and upper boundaries / values of a confidence interval, that is, the values which define the range of a confidence interval. The upper and lower bounds of a 95% confidence interval are the 95% confidence limits. The method described here is based on Goel Okomoto (GO) model, Confidence Interval (CI) and parameter estimation is maximum likehood (ML). It uses historical sample test data to predict how many residual defects are there in the software system and the estimated range being calculated from a given set of sample data to achieve at least 95% confidence level.
Keywords: Confidence Interval(CI), Failure Intensity Funtion, Goel-Okumoto Model (GO), Interval Estimation, Maximum Likelihood Estimator (MLE), Paramenter Estimation, Sof Twar Reliability.
Scope of the Article: Data Mining