The recent advancement of artificial intelligence, especially machine learning (ML), has significantly impacted software engineering research, including bug report analysis. ML aims to automate the understanding, extraction, and correlation of information from bug reports. Despite its growing importance, there has been no comprehensive review in this area. In this paper, we present a systematic literature review covering 1,825 papers, selecting 204 for detailed analysis. We derive seven key findings: 1) Extensive use of CNN, LSTM, and $k$NN for bug report analysis, with advanced models like BERT underutilized due to their complexity. 2) Word2Vec and TF-IDF are popular for feature representation, with a rise in deep learning approaches. 3) Stop word removal is the most common preprocessing, with structural methods rising after 2020. 4) Eclipse and Mozilla are the most frequently evaluated software projects. 5) Bug categorization is the most common task, followed by bug localization and severity prediction. 6) There is increasing attention on specific bugs like non-functional and performance bugs. 7) Common evaluation metrics are F1-score, Recall, Precision, and Accuracy, with $k$-fold cross-validation preferred for model evaluation. 8) Many studies lack robust statistical tests. We also identify six promising future research directions to provide useful insights for practitioners.
Santiago Matalonga, Domenico Amalfitano, Jean Carlo Rossa Hauck
et al.
Background: Conducting Multi Vocal Literature Reviews (MVLRs) is often time and effort-intensive. Researchers must review and filter a large number of unstructured sources, which frequently contain sparse information and are unlikely to be included in the final study. Our experience conducting an MVLR on Context-Aware Software Systems (CASS) Testing in the avionics domain exemplified this challenge, with over 8,000 highly heterogeneous documents requiring review. Therefore, we developed a Large Language Model (LLM) assistant to support the search and filtering of documents. Aims: To develop and validate an LLM based tool that can support researchers in performing the search and filtering of documents for an MVLR without compromising the rigor of the research protocol. Method: We applied sound engineering practices to develop an on-premises LLM-based tool incorporating Retrieval Augmented Generation (RAG) to process candidate sources. Progress towards the aim was quantified using the Positive Percent Agreement (PPA) as the primary metric to ensure the performance of the LLM based tool. Convenience sampling, supported by human judgment and statistical sampling, were used to verify and validate the tool's quality-in-use. Results: The tool currently demonstrates a PPA agreement with human researchers of 90% for sources that are not relevant to the study. Development details are shared to support domain-specific adaptation of the tool. Conclusions: Using LLM-based tools to support academic researchers in rigorous MVLR is feasible. These tools can free valuable time for higher-level, abstract tasks. However, researcher participation remains essential to ensure that the tool supports thorough research.
The transition from 5G to 6G requires frequency-dependent, physically consistent radio channel models across the FR1--FR3 span, particularly in the under-explored $4$--$8$~GHz region targeted in the current WRC-$27$ studies, where outdoor urban channel measurements and parameterizations remain scarce. This paper presents a $4.85$~GHz measurement-anchored study of urban channels and a literature-referenced cross-band analysis. Double-directional measurements were conducted at $4.85$~GHz in urban macrocell (UMa) and urban microcell (UMi) routes in Yokohama, Japan, from which path loss, delay spread (DS), azimuth spread of arrival/departure (ASA/ASD), $K$-factor, and route-dependent spatial-consistency statistics were extracted. To align these results in a broader cross-band context, the measured $4.85$~GHz large-scale parameter (LSP) means were combined with scenario-matched literature anchors to derive log-log trends for DS, ASA, and ASD over an approximately $4$--$28$~GHz range that spans the $7.125$~GHz FR1--FR3 boundary. The resulting trends were compared with 3GPP UMa/UMi reference parameterizations over the same interval. Because the cross-band analysis relies on a single in-house measurement band and a limited number of heterogeneous literature anchors, it is presented as measurement-informed and indicative, rather than as a definitive multi-band model. The paper therefore contributes both a detailed, parameterized $4.85$~GHz urban measurement reference and a bounded literature-referenced cross-band view of channel behavior near the FR1--FR3 transition.
Trent D. Buskirk, Florian Keusch, Leah von der Heyde
et al.
Survey research has a long-standing history of being a human-powered field, but one that embraces various technologies for the collection, processing, and analysis of various behavioral, political, and social outcomes of interest, among others. At the same time, Large Language Models (LLMs) bring new technological challenges and prerequisites in order to fully harness their potential. In this paper, we report work-in-progress on a systematic literature review based on keyword searches from multiple large-scale databases as well as citation networks that assesses how LLMs are currently being applied within the survey research process. We synthesize and organize our findings according to the survey research process to include examples of LLM usage across three broad phases: pre-data collection, data collection, and post-data collection. We discuss selected examples of potential use cases for LLMs as well as its pitfalls based on examples from existing literature. Considering survey research has rich experience and history regarding data quality, we discuss some opportunities and describe future outlooks for survey research to contribute to the continued development and refinement of LLMs.
The rapid growth of research literature, particularly in large language models (LLMs), has made producing comprehensive and current survey papers increasingly difficult. This paper introduces autosurvey2, a multi-stage pipeline that automates survey generation through retrieval-augmented synthesis and structured evaluation. The system integrates parallel section generation, iterative refinement, and real-time retrieval of recent publications to ensure both topical completeness and factual accuracy. Quality is assessed using a multi-LLM evaluation framework that measures coverage, structure, and relevance in alignment with expert review standards. Experimental results demonstrate that autosurvey2 consistently outperforms existing retrieval-based and automated baselines, achieving higher scores in structural coherence and topical relevance while maintaining strong citation fidelity. By combining retrieval, reasoning, and automated evaluation into a unified framework, autosurvey2 provides a scalable and reproducible solution for generating long-form academic surveys and contributes a solid foundation for future research on automated scholarly writing. All code and resources are available at https://github.com/annihi1ation/auto_research.
In this paper, we present a comprehensive corpus-driven analysis of Bangla literary and newspaper texts to investigate their lexical diversity, structural complexity and readability. We undertook Vacaspati and IndicCorp, which are the most extensive literature and newspaper-only corpora for Bangla. We examine key linguistic properties, including the type-token ratio (TTR), hapax legomena ratio (HLR), Bigram diversity, average syllable and word lengths, and adherence to Zipfs Law, for both newspaper (IndicCorp) and literary corpora (Vacaspati).For all the features, such as Bigram Diversity and HLR, despite its smaller size, the literary corpus exhibits significantly higher lexical richness and structural variation. Additionally, we tried to understand the diversity of corpora by building n-gram models and measuring perplexity. Our findings reveal that literary corpora have higher perplexity than newspaper corpora, even for similar sentence sizes. This trend can also be observed for the English newspaper and literature corpus, indicating its generalizability. We also examined how the performance of models on downstream tasks is influenced by the inclusion of literary data alongside newspaper data. Our findings suggest that integrating literary data with newspapers improves the performance of models on various downstream tasks. We have also demonstrated that a literary corpus adheres more closely to global word distribution properties, such as Zipfs law, than a newspaper corpus or a merged corpus of both literary and newspaper texts. Literature corpora also have higher entropy and lower redundancy values compared to a newspaper corpus. We also further assess the readability using Flesch and Coleman-Liau indices, showing that literary texts are more complex.
Clinical trial eligibility matching is a critical yet often labor-intensive and error-prone step in medical research, as it ensures that participants meet precise criteria for safe and reliable study outcomes. Recent advances in Natural Language Processing (NLP) have shown promise in automating and improving this process by rapidly analyzing large volumes of unstructured clinical text and structured electronic health record (EHR) data. In this paper, we present a systematic overview of current NLP methodologies applied to clinical trial eligibility screening, focusing on data sources, annotation practices, machine learning approaches, and real-world implementation challenges. A comprehensive literature search (spanning Google Scholar, Mendeley, and PubMed from 2015 to 2024) yielded high-quality studies, each demonstrating the potential of techniques such as rule-based systems, named entity recognition, contextual embeddings, and ontology-based normalization to enhance patient matching accuracy. While results indicate substantial improvements in screening efficiency and precision, limitations persist regarding data completeness, annotation consistency, and model scalability across diverse clinical domains. The review highlights how explainable AI and standardized ontologies can bolster clinician trust and broaden adoption. Looking ahead, further research into advanced semantic and temporal representations, expanded data integration, and rigorous prospective evaluations is necessary to fully realize the transformative potential of NLP in clinical trial recruitment.
Research on second-language (L2) acquisition of tense–aspect morphology has grown markedly over the past four decades, yet no panoramic scientometric synthesis exists. Drawing on 2,398 unique publications indexed in Scopus (2,153) and Web of Science Core Collection (269) from 1984–2025, we chart publication trajectories, theoretical emphases, target languages, and thematic shifts. CiteSpace, VOSviewer, and WordStat reveal that annual output surged after 2000 and peaked during 2020–2023, driven primarily by U.S. institutions and prolific authors such as Montrul (53 papers) and Shirai (50). The Aspect Hypothesis remains the most frequently tested framework (18 explicit mentions), while English (2,082 studies) and Spanish (1,415) dominate the language sample, underscoring a continued geographic–linguistic skew. Thirteen cohesive research clusters (silhouette = 0.80–0.997) trace an evolution from form-focused to meaning-oriented and usage-based perspectives. Influential contributions increasingly integrate cognitive, developmental, and instructional lenses. Despite this maturation, under-represented languages and regions persist, signalling the need for cross-linguistic replication and pedagogically oriented, usage-based research.
Gjennom en etnografisk studie bidrar artikkelen til samtalen om dekolonial filosofi ved å fremme en dialog mellom urfolks erfaringsbaserte kunnskapsformer og vestlig filosofi som likeverdige tilnærminger til verden. Analysen tar utgangspunkt i mapuche urfolk i Patagonia (Argentina og Chile) og deres begreper rakizuam (sansende tenkning) og kimün (visdom). Det argumenteres for at urfolks onto-epistemologier ikke bør betraktes som kuriositeter, men som verdifulle bidrag til vestlig tenkning. Den dominerende filosofiske kanon kunne utvilsomt sett annerledes ut dersom den hadde omfavnet flere av de alternative perspektiver som, innenfor vestlig filosofi selv, har utviklet forståelser av verden nært beslektet med urfolks helhetlige perspektiver.
In recent years, Norwegian cultural production has increasingly foregrounded the experiences of sailors serving aboard merchant vessels allied with the British during the Second World War. These men endured not only physical injuries from submarine and aerial attacks, but also profound psychic trauma, often manifesting in post-war alcoholism and depression. However, the war at sea also left indelible marks on women’s bodies. This article examines Vigdis Stokkelien’s trilogy on Gro—<i>Lille-Gibraltar</i> (<i>Little Gibraltar</i>, 1972), <i>Båten under solseilet</i> (<i>The boat under the sun sail</i>, 1982), and <i>Stjerneleden</i> (<i>The star joint</i>, 1984)—to explore how emotions as fear, shame and pain circulate between different individuals and groups during the war and in war memories. Drawing on affect theory, this reading of Stokkelien’s novels demonstrates how what happened at sea marked Norwegian bodies and national identity for a long time after the war.
History of scholarship and learning. The humanities
In recent years, visual sensors have been quickly improving towards mimicking the visual information acquisition process of human brain by responding to illumination changes as they occur in time rather than at fixed time intervals. In this context, the so-called neuromorphic vision sensors depart from the conventional frame-based image sensors by adopting a paradigm shift in the way visual information is acquired. This new way of visual information acquisition enables faster and asynchronous per-pixel responses/recordings driven by the scene dynamics with a very high dynamic range and low power consumption. However, depending on the application scenario, the emerging neuromorphic vision sensors may generate a large volume of data, thus critically demanding highly efficient coding solutions in order applications may take full advantage of these new, attractive sensors' capabilities. For this reason, considerable research efforts have been invested in recent years towards developing increasingly efficient neuromorphic vision data coding (NVDC) solutions. In this context, the main objective of this paper is to provide a comprehensive overview of NVDC solutions in the literature, guided by a novel classification taxonomy, which allows better organizing this emerging field. In this way, more solid conclusions can be drawn about the current NVDC status quo, thus allowing to better drive future research and standardization developments in this emerging technical area.
Nidhika Chauhan, Navneet Kaur, Kamaljit Singh Saini
et al.
As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The review revealed three task allocation research topics and seven performance management methods. Task allocation research areas are resource allocation, load-Balancing, and scheduling. Performance management includes monitoring and control, power and energy management, resource utilization optimization, quality of service management, fault management, virtual machine management, and network management. The study proposes new techniques to enhance cloud computing work allocation and performance management. Short-comings in each approach can guide future research. The research's findings on cloud data center task allocation and performance management can assist academics, practitioners, and cloud service providers in optimizing their systems for dependability, cost-effectiveness, and scalability. Innovative methodologies can steer future research to fill gaps in the literature.
In software applications, user models can be used to specify the profile of the typical users of the application, including personality traits, preferences, skills, etc. In theory, this would enable an adaptive application behavior that could lead to a better user experience. Nevertheless, user models do not seem to be part of standard modeling languages nor common in current model-driven engineering (MDE) approaches. In this paper, we conduct a systematic literature review to analyze existing proposals for user modeling in MDE and identify their limitations. The results showcase that there is a lack of a unified and complete user modeling perspective. Instead, we observe a lot of fragmented and partial proposals considering only simple user dimensions and with lack of proper tool support. This limits the implementation of richer user interfaces able to better support the user-specific needs. Therefore, we hope this analysis triggers a discussion on the importance of user models and their inclusion in MDE pipelines. Especially in a context where, thanks to the rise of AI techniques, personalization, based on a rich number of user dimensions, is becoming more and more of a possibility.
During a diving expedition through northern Norway, a total of 25 heterobranchiate taxa were collected, and additional data on distribution and ecology were given. Cumanotus beaumonti and Flabellina borealis are recorded for the first time since Odhner (1907), (1922) on the Norwegian coast, and the distribution of eight heterobranch taxa were re-evaluated. By using SCUBA diving as a sampling method, it is proved that the diversity and distribution of heterobranchs is poorly known in northern Norwegian waters, as the results show that large quantities of specimens, as well as a significant number of taxa, are found at each locality. The significance of diving for studying heterobranchs is emphasized. Apart from new records of heterobranchs and enhanced knowledge of biodiversity and biogeography of this group in northern Norway, this paper presents previous published literature where heterobranch taxa are mentioned for northern Norway.
Jill P. Naiman, Peter K. G. Williams, Alyssa Goodman
Scientific articles published prior to the "age of digitization" in the late 1990s contain figures which are "trapped" within their scanned pages. While progress to extract figures and their captions has been made, there is currently no robust method for this process. We present a YOLO-based method for use on scanned pages, after they have been processed with Optical Character Recognition (OCR), which uses both grayscale and OCR-features. We focus our efforts on translating the intersection-over-union (IOU) metric from the field of object detection to document layout analysis and quantify "high localization" levels as an IOU of 0.9. When applied to the astrophysics literature holdings of the NASA Astrophysics Data System (ADS), we find F1 scores of 90.9% (92.2%) for figures (figure captions) with the IOU cut-off of 0.9 which is a significant improvement over other state-of-the-art methods.
Generative audio models typically focus their applications in music and speech generation, with recent models having human-like quality in their audio output. This paper conducts a systematic literature review of 884 papers in the area of generative audio models in order to both quantify the degree to which researchers in the field are considering potential negative impacts and identify the types of ethical implications researchers in this area need to consider. Though 65% of generative audio research papers note positive potential impacts of their work, less than 10% discuss any negative impacts. This jarringly small percentage of papers considering negative impact is particularly worrying because the issues brought to light by the few papers doing so are raising serious ethical implications and concerns relevant to the broader field such as the potential for fraud, deep-fakes, and copyright infringement. By quantifying this lack of ethical consideration in generative audio research and identifying key areas of potential harm, this paper lays the groundwork for future work in the field at a critical point in time in order to guide more conscientious research as this field progresses.
Tahereh Nayerifard, Haleh Amintoosi, Abbas Ghaemi Bafghi
et al.
Development and exploitation of technology have led to the further expansion and complexity of digital crimes. On the other hand, the growing volume of data and, subsequently, evidence is a severe challenge in digital forensics. In recent years, the application of machine learning techniques to identify and analyze evidence has been on the rise in different digital forensics domains. This paper offers a systematic literature review of the research published in major academic databases from January 2010 to December 2021 on the application of machine learning in digital forensics, which was not presented yet to the best of our knowledge as comprehensive as this. The review also identifies the domains of digital forensics and machine learning methods that have received the most attention in the previous papers and finally introduces remaining research gaps. Our findings demonstrate that image forensics has obtained the greatest benefit from using machine learning methods, compared to other forensic domains. Moreover, CNN-based models are the most important machine learning methods that are increasingly being used in digital forensics. We present a comprehensive mind map to provide a proper perspective for valuable analytical results. Furthermore, visual analysis has been conducted based on the keywords of the papers, providing different thematic relevance topics. This research will give digital forensics investigators, machine learning developers, security researchers, and enthusiasts a broad view of the application of machine learning in digital forensics.
This review examines the scientific articles of the last decade, approaching the subject through the methodology of the scoping literature review. Starting with the Boolean search global citizens AND education AND (international business OR international business school) in the ScienceDirect, Emerald, and Scopus databases, the review resulted in only scientific journal articles, strictly targeted at tertiary education ONLY of international business schools and ONLY in those articles that study global citizenship. For reasons of up-to-date knowledge, the present literature was content with the final decade. A total of 13 articles are recorded as a result of the aforementioned Boolean search from a total of 216 articles identified in the first phase of the search. The results will help the researchers to acquire the required knowledge base for their research, the academics to incorporate new methods in their teaching and the approach of their students, and the policymakers to adapt the schools curricula according to the data from the articles present in the literature review.
The purpose of this article is to analyse the autobiographical novel Eternal Sunday by Linnéa Myhre in order to present the way in which the language reflects the valuation and categorization of the human body. The novel conveys the perspective of a person who suffers from an eating disorder. The way she is describing the human body is affected by the illness, and subconsciously associated with a particular distorted body image, beauty standards and expectations. The theoretical basis is rooted in cognitive linguistics and linguistic categories as proposed by George Lakoff. Cultural and psychological concepts are also taken into the account.
Anne Fasting, Irene Hetlevik, Bente Prytz Mjølstad
Abstract Background Modern palliative care focuses on enabling patients to spend their remaining time at home, and dying comfortably at home, for those patients who want it. Compared to many European countries, few die at home in Norway. General practitioners’ (GPs’) involvement in palliative care may increase patients’ time at home and achievements of home death. Norwegian GPs are perceived as missing in this work. The aim of this study is to explore GPs’ experiences in palliative care regarding their involvement in this work, how they define their role, and what they think they realistically can contribute towards palliative patients. Methods We performed focus group interviews with GPs, following a semi-structured interview guide. We included four focus groups with a total of 25 GPs. Interviews were recorded and transcribed verbatim. We performed qualitative analysis on these interviews, inspired by interpretative phenomenological analysis. Results Strengths of the GP in the provision of palliative care consisted of characteristics of general practice and skills they relied on, such as general medical knowledge, being coordinator of care, and having a personal and longitudinal knowledge of the patient and a family perspective. They generally had positive attitudes but differing views about their formal role, which was described along three positions towards palliative care: the highly involved, the weakly involved, and the uninvolved GP. Conclusion GPs have evident strengths that could be important in the provision of palliative care. They rely on general medical knowledge and need specialist support. They had no consensus about their role in palliative care. Multiple factors interact in complex ways to determine how the GPs perceive their role and how involved they are in palliative care. GPs may possess skills and knowledge complementary to the specialized skills of palliative care team physicians. Specialized teams with extensive outreach activities should be aware of the potential they have for both enabling and deskilling GPs.