Most of the models for software reliability analysis are based on reliability growth models which deal with the fault detection process. This is done either by assuming that faults are corrected immediately after being detected or the time to correct a fault is not counted. Some models have been developed to relax this assumption. However, unlike the fault-detection process, few published data sets are available to support the modeling and analysis of both the fault detection and removal processes. In this paper, some useful approaches to the modeling of both software fault-detection and faultcorrection processes are discussed. Further analysis on the software release time decision that incorporates both a fault-detection model and fault-correction model is also presented. This procedure is easy to use and useful for practical applications. The approach is illustrated with an actual set of data from a software development project.
Software defect prediction (SDP) is an effective technique to lower software module testing costs. However, the imbalanced distribution almost exists in all SDP datasets and restricts the accuracy of defect prediction. In order to balance the data distribution reasonably, we propose a novel resampling method LIMCR on the basis of Naïve Bayes to optimize and improve the SDP performance. The main idea of LIMCR is to remove less-informative majorities for rebalancing the data distribution after evaluating the degree of being informative for every sample from the majority class. We employ 29 SDP datasets from the PROMISE and NASA dataset and divide them into two parts, the small sample size (the amount of data is smaller than 1100) and the large sample size (larger than 1100). Then we conduct experiments by comparing the matching of classifiers and imbalance learning methods on small datasets and large datasets, respectively. The results show the effectiveness of LIMCR, and LIMCR+GNB performs better than other methods on small datasets while not brilliant on large datasets.
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