With the current advancement of technology, real-time software has been extensively utilized in several complex systems. The eternally rising complexity of software formulates it exceptionally hard to maintain the reliability of software and has strained extraordinary awareness in software industries. Nearly all reliability-based software reliability growth models (SRGMs) are formulated using a general consideration that during the testing phase, all detected faults are instantly corrected without introducing any new fault. Therefore, both detection and correction of faults are considered as similar processes. In this paper, inclusive modeling is done for investigating the detection and correction of faults under an imperfect debugging scenario. This formulation is modeled by considering the postulation that new faults are involved throughout the correction of a hard type of fault. Numerous qualitative measures for assessment of reliability are considered and least square estimations of unidentified model parameters are assessed. The validation of the derived proposed models is verified through actual datasets. The measures of accuracy are the mean square error (MSE), root mean square error (RMSE), bias, variance and root mean square prediction error (RMSPE) are calculated employing valid datasets. The goodness of fit criteria is verified based on such qualitative measures.

中文翻译：

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使用统计方法对故障检测和纠正进行基于不完美调试的建模

随着当前技术的进步，实时软件已在多个复杂系统中得到广泛应用。软件的不断增加的复杂性使维护软件的可靠性变得异常困难，并且使软件行业的意识变得异常紧张。几乎所有基于可靠性的软件可靠性增长模型 (SRGM) 都是基于在测试阶段所有检测到的故障都会立即纠正而不会引入任何新故障的一般考虑来制定的。因此，故障的检测和纠正都被认为是相似的过程。在本文中，包容性建模用于调查在不完善的调试场景下的故障检测和纠正。这个公式是通过考虑在整个硬故障的纠正过程中涉及新故障的假设来建模的。考虑了许多用于评估可靠性的定性措施，并评估了未识别模型参数的最小二乘估计。派生提出的模型的验证通过实际数据集进行验证。准确度的度量是均方误差 (MSE)、均方根误差 (RMSE)、偏差、方差和均方根预测误差 (RMSPE)，均使用有效数据集计算。基于这种定性测量验证拟合优度标准。派生提出的模型的验证通过实际数据集进行验证。准确度的度量是均方误差 (MSE)、均方根误差 (RMSE)、偏差、方差和均方根预测误差 (RMSPE)，均使用有效数据集计算。基于这种定性测量验证拟合优度标准。派生提出的模型的验证通过实际数据集进行验证。准确度的度量是均方误差 (MSE)、均方根误差 (RMSE)、偏差、方差和均方根预测误差 (RMSPE)，均使用有效数据集计算。基于这种定性测量验证拟合优度标准。