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Maintaining quality in software development projects is becoming very difficult because the complexity of modules in the software is growing exponentially. Software defects are the primary concern, and software defect prediction (SDP) plays a crucial role in detecting faulty modules early and planning effective testing to reduce maintenance costs. However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. The SPAM-XAI model exhibited improved performance after experimenting with the NASA PROMISE repository's datasets. It achieved an accuracy of 98.13% on CM1, 96.00% on PC1, and 98.65% on PC2, surpassing previous state-of-the-art and baseline models with other evaluation matrices enhancement compared to existing methods. The SPAM-XAI model increases transparency and facilitates understanding of the interaction between features and error status, enabling coherent and comprehensible predictions. This enhancement optimizes the decision-making process and enhances the model's trustworthiness in the SDLC. [ABSTRACT FROM AUTHOR]

Moments of order statistics (OSs) characterize the Weibull–geometric and half-logistic families of distributions, of which the extended exponential–geometric (EEG) distribution is a particular case. The EEG distribution is used to create the log-extended exponential–geometric (LEEG) distribution, which is bounded in the unit interval (0, 1). In addition to the generalized Stirling numbers of the first kind, a few years ago, the polylogarithm function and the Lerch transcendent function were used to determine the moments of order statistics of the LEEG distributions. As an application based on the L-moments, we expand the features of the LEEG distribution in this work. In terms of the Gauss hypergeometric function, this work presents the precise equations and recurrence relations for the single moments of OSs from the LEEG distribution. Along with recurrence relations between the expectations of function of two OSs from the LEEG distribution, it also displays the truncated and conditional distribution of the OSs. Additionally, we use the L-moments to estimate the parameters of the LEEG distribution. We further fit the LEEG distribution on three practical data sets from medical and environmental sciences areas. It is seen that the estimated parameters through L-moments of the OSs give a superior fit. We finally determine the correspondence between the entropies and the OSs. [ABSTRACT FROM AUTHOR]

New 2D layered materials WX2N4(X≐Si, Ge)1 are suitable for thermoelectric applications for a pretty good value of the figure of merit (ZT). Here, the thermoelectric properties of the 2D monolayer of WX2N4(X≐Si, Ge) using Density Functional Theory (DFT) is investigated combined with Boltzmann Transport Equation (BTE) along with spin‐orbit coupling (SOC). An excellent thermoelectric ZT$ZT$ of 0.91 (0.92 with SOC) is obtained at 900 K for p‐type WGe2N4, and a ZT$ZT$ of 0.81 (0.86 with SOC) is observed for n‐type at the same temperature. Furthermore, the WGe2N4 showed a ZT$ZT$ of more than 0.7 (0.79 with SOC) at room temperature for p‐type. On the other hand, the WSi2N4 showed a comparatively lower ZT$ZT$ at room temperature. However, the ZT$ZT$ value increases significantly at higher temperatures, reaching 0.72 (0.79 with SOC) and 0.71 (0.62 with SOC) for p and n‐type at 900 K, respectively. The electronic band structure is examined and discovered that WSi2N4 and WGe2N4 possess indirect bandgaps (BG) of 2.68 eV (2.57 eV with SOC) and 1.53 eV (1.46 eV with SOC), respectively, according to Heyd‐Scuseria‐Ernzerhof (HSE) approximation. These materials may also be useful in UV and visible range optoelectronic devices because of their strong absorption in the respective regions. [ABSTRACT FROM AUTHOR]

In this study, we introduce a novel class of distributions called the length biased Uma distribution. This distribution is a specific instance of the broader weighted distribution family, known for its versatility in various applications. We explore the structural properties of the length biased Uma distribution and propose a robust parameter estimation technique based on maximum likelihood estimation. To assess its efficacy, we apply the newly introduced distribution to two real-world datasets, evaluating its flexibility and performance in comparison to existing models. The results obtained demonstrate the potential of the length biased Uma distribution as a valuable addition to the repertoire of statistical distributions, offering valuable insights for a wide range of practical applications. [ABSTRACT FROM AUTHOR]