Unmanned aerial vehicles (UAVs) have emerged as versatile tools with significant potential in various fields, including, but not limited to civil engineering, ecology, networking and precision agriculture. Systematic literature reviews (SLRs) play a crucial role in assessing the quality of research methods and approaches, aiding researchers and practitioners in selecting and optimizing their projects. However, the quality assessment of UAV-related SLRs and the aggregation of UAV technologies across research fields remain limited. This study aims to address these gaps by conducting a tertiary literature review (TLR) that assesses the quality of SLRs, aggregates data across research fields, and provides guidelines for researchers and practitioners in the UAV community. Based on a review of 73 SLRs, it is evident that the quality of UAV-related SLRs is generally low, with a lack of quality assessment and inadequate reporting of detailed information on primary studies. Consequently, this study presents reporting items and example quality assessment ratings sourced from the SLRs to enhance the transparency and comparability of future UAV-related research. Additionally, it highlights common limitations faced by UAV applications, such as regulatory, technical, social, and research-related challenges, which require attention for progress in the field. Overall, this study aims to enhance the quality and knowledge sharing in the UAV research community by providing insights into the current state of UAV-related SLRs and offering practical guidance for researchers and practitioners through the provision of data-extraction templates and quality control questions for future UAV-related reviews and primary research. Key outcomes: • This tertiary review indicates a low quality of UAV-related SLRs. • Lack of quality assessment and inadequate reporting of detailed information on primary studies is identified as a prevalent issue. • Data-extraction templates and quality control questions ratings sourced from SLRs are offered for future UAV-related reviews and primary research to enhance transparency and comparability in future UAV-related research. • Common limitations faced by UAV applications are highlighted, including regulatory, technical, social, and research-related challenges.

Digital twin (DT) technology has become a promising approach for improving the efficiency, quality, and reliability of manufacturing processes. This systematic literature review aims to conduct an in-depth analysis of the current approaches used to develop DTs for Additive Manufacturing (AM) process from January 2010 to May 2024. We analyzed 65 studies to identify the types of printers used, challenges and opportunities, methodologies, implementation approach, quality assurance, process optimization, integration of material behavior, and structural analysis. The results outline the challenges associated with data collection and processing due to real-time requirements or generating relevant data through sensors. Additionally, there is a need for accurate material behavior and structural analysis models to achieve efficient simulation modules, yet there has been limited research on integrating those. Standard engineering tools used for DTs in the literature are not employed in the AM context. Many studies present generic implementations for DTs that do not address the required level of detail to achieve an effective DT solution. Future work should focus on understanding the complex additive manufacturing process to build integrated and enhanced simulation capabilities of the DT and on employing standard engineering methodologies and tools for developing DTs in the context of AM.

Source code search plays an important role in software development, e.g. for exploratory development or opportunistic reuse of existing code from a code base. Often, exploration of different implementations with the same functionality is needed for tasks like automated software transplantation, software diversification, and software repair. Code clones, which are syntactically or semantically similar code fragments, are perfect candidates for such tasks. Searching for code clones involves a given search query to retrieve the relevant code fragments. We propose a novel approach called Clone-Seeker that focuses on utilizing clone class features in retrieving code clones. For this purpose, we generate metadata for each code clone in the form of a natural language document. The metadata includes a pre-processed list of identifiers from the code clones augmented with a list of keywords indicating the semantics of the code clone. This keyword list can be extracted from a manually annotated general description of the clone class, or automatically generated from the source code of the entire clone class. This approach helps developers to perform code clone search based on a search query written either as source code terms, or as natural language. With various experiments, we show that (1) Clone-Seeker is effective in finding clones from BigCloneBench dataset by applying code queries and natural language queries; 2) Clone-Seeker has a higher recall when searching for semantic code clones (i.e., Type-4) in BigCloneBench than the state-of-the-art; 3) Clone-Seeker is a generalized technique as it is effective in finding clones in Project CodeNet dataset by applying code queries and natural language queries. 4) Clone-Seeker with manual annotation outperforms other variants in finding clones on the basis of natural language queries