Effective water management is crucial in urban and rural settings, requiring efficient usage and timely detection of issues like leakages for sustainability. This paper introduces an integrated framework that combines Digital Twin technology with a multimodal transformer-based model for accurate water usage prediction and leakage detection. The system synchronizes real-time data from various sensors including flow meters, pressure sensors, and thermal imaging devices with a Digital Twin of the water network. Advanced transformer models, specifically the Informer model for long-term time-series prediction and a Water Multimodal Transformer for anomaly detection, process these data to capture complex patterns and dependencies. Experimental results demonstrate the framework’s effectiveness: the Informer model achieved an R² score of 0.9995 and a Mean Squared Error (MSE) of 2.2, outperforming traditional models. For leakage detection, the model attained 98.4% accuracy and precision, an F1 score of 97.6%, a low False Positive Rate of 0.0019, and an Area Under the Curve (AUC) of 0.984. By fusing diverse sensor data and utilizing advanced transformer architectures, the framework provides a comprehensive view of the water network, enabling real-time decision-making, enhancing forecasting accuracy, and reducing water waste. This scalable solution supports sustainable water management practices in both urban and industrial contexts.

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

Software developers frequently reuse source code from repositories as it saves development time and effort. Code clones (similar code fragments) accumulated in these repositories represent often repeated functionalities and are candidates for reuse in an exploratory or rapid development. To facilitate code clone reuse, we previously presented DeepClone, a novel deep learning approach for modeling code clones along with non-cloned code to predict the next set of tokens (possibly a complete clone method body) based on the code written so far. The probabilistic nature of language modeling, however, can lead to code output with minor syntax or logic errors. To resolve this, we propose a novel approach called Clone-Advisor. We apply an information retrieval technique on top of DeepClone output to recommend real clone methods closely matching the predicted clone method, thus improving the original output by DeepClone. In this paper we have discussed and refined our previous work on DeepClone in much more detail. Moreover, we have quantitatively evaluated the performance and effectiveness of Clone-Advisor in clone method recommendation.

The emergence of large language models (LLMs) has opened up new possibilities for geoanalysis, spanning geospatial and geospatio-temporal data analysis, making it easily accessible to non-GIS experts via their natural language interface. In this study, we present the first systematic literature review (SLR) of LLM-powered geoanalysis tools.We analyzed 17 peer-reviewed studies published between 2022 and 2025. Our review synthesizes current developments along several key dimensions. Based on our review, we propose a taxonomy of geoanalysis data categories, classify the types of geoanalysis supported, and identify application domains where these techniques are being applied. Additionally, we examine the LLM models, programming languages, and libraries employed, as well as the extent to which systems offer open data and source code access.We further analyze evaluation strategies by comparing performance metrics and methodological approaches across studies. Finally, we discuss common challenges, such as LLM hallucination, tool chaining limitations, and data compatibility issues, in the context of geoanalysis and summarize the solutions proposed in the literature. This paper provides a valuable and comprehensive overview of the landscape of LLM-powered geoanalysis tools and frameworks to aid our research and development community in adopting LLMs for geoanalysis.