In this work, we propose a time-variant software reliability model (SRM)which considers the fault detection and the highest number of faults in software. The time-variant genetic algorithm process is implemented for the assessment of the SRM parameters. The proposed model works upon a non-homogeneous Poisson process (NHPP) and incorporates fault dependent detection and software failure intensity and the un-removed error in the software. We had considered programmers proficiency, software complexity, organization hierarchy, and perfect debugging as the determining factors for SRM. The dataset collected from 74 software projects was experimented with to establish and validate the proposed software reliability model's better fit. Data is collected over a period, which is initiated with the start of the project and is continuously monitored until its completion. Several parameters are analyzed, and a collection of 115 attributes are given with 11 different time frames in terms of product and process characteristics. A total of 383 persons were involved in software design, where the issue count total is 255. The proposed time-variant fault detection SRM is implemented in Jira and is also compared with the existing reliability model presented in the literature. It is observed that the proposed fault detection SRM works better in terms of different parameters like mean square error (MSE), root mean square error (RMSE), and r-squared (R²). The work is carried out, ensuring time-varying fault detection, which is measured by considering response count, coding and non-coding deliverables, and the number of bugs in the software. We considered the programmer's proficiency, software complexity, organization hierarchy, and perfect debugging as the determining factors for presenting the software reliability model. The proposed Software reliability model shows improvement over existing algorithms as the residual errors are reduced, and prediction accuracy is high in terms of cumulative fault detection.

Most modern cryptographic mechanisms are often based on the key distribution schemes, the security of which depends on the computational complexity and the power used to break the encryption. Quantum Key Distribution (QKD) is gaining popularity as a panacea to the issue of secure key distribution due to its ability to show secret keys' information-theoretic protection that is already suitable for commercialization. The goal is to generate a secret key between trusted parties connected through a quantum channel and an authenticated classical channel. The technology promises unconditional secure communication based on the principles of quantum mechanics without limiting the power of an eavesdropper. The first three sections provide a contemporary review of the Quantum Key Distribution in a nutshell. The remaining part of the paper deals with the key parameters and implementations that have been developed to assess the security of the leading experimental platforms and the challenges with respect to the development of this technology such as optimally overlaying the logic gates via quantum-dot cellular automata, which also creates possible avenues for research in the cryptographic domain.

This paper explores the potential of automating bug triage using machine learning and natural language processing. In the context of growing software complexity, manual bug handling faces challenges leading to delays. The study critically examines traditional bug triage methods, emphasizing their limitations and the need for automated solutions. By reviewing various machine learning techniques applied in bug triage, the paper provides insights for practitioners. The proposed Bug Triage model, integrating Word2Vec embeddings and XGBoost, shows promising results in categorizing and prioritizing bug reports. The paper concludes by highlighting future research opportunities to advance bug triage methodologies and improve software development practices.