The COVID-19 pandemic, widely acknowledged as the most significant global health crisis of this century, has impacted virtually every sector, including software development. This unprecedented situation has led to significant implications for software projects and the professionals involved in software development, presenting substantial challenges. The primary objective of this study is to systematically analyze the evolving research trends within the Global Software Engineering (GSE) field. This study focuses on examining whether the global pandemic has led to an increased emphasis on software design research. Furthermore, we investigate the existing gap in social interaction during the software design phase of development. The research methodology consists of a two-phase systematic analysis of the existing literature. In the first phase involves the mapping of GSE research conducted over the two decades preceding the pandemic (2000-2020). In the second phase, this study employed a forward snowballing approach to review literature related to the software design phase published between 2020 and 2022. The analysis of 592 research studies in these two phases reveals various trends in GSE research. Evaluation research stands out as the most extensively explored research type across methods, processes, and human aspects of development. Despite the considerable impact of the COVID-19 pandemic, which led to an increased reliance on distributed teams, our findings suggest that, while software organizations have been extensively studied across all software engineering phases, the software design phase remains one of the least explored areas. Our contribution envisions a more collaborative and adaptable GSE field, providing guidance for future research endeavors aimed at supporting distributed teams.

With the global adoption of Internet services, service providers are having a difficult time securing their systems, especially against new attacks and intrusions. Various anomalous detection approaches have been developed for protecting WSN from cyber-attacks. However, those systems suffer from the major issues of a high number of false alarms, increased over-fitting, and complexity. Therefore, this paper motivates to develop a novel and intelligent IDS framework for protecting WSN from cyber-attacks. For this purpose, an Intensive Binary Pigeon Optimization (IBiPO) and Bi-directional Long Short-Term Memory (Bi-LSTM) mechanisms are developed for accurate intrusion detection and classification.