SoftwareXPub Date : 2025-04-15DOI: 10.1016/j.softx.2025.102157
David Chapela-Campa, Orlenys López-Pintado, Ihar Suvorau, Marlon Dumas
{"title":"SIMOD: Automated discovery of business process simulation models","authors":"David Chapela-Campa, Orlenys López-Pintado, Ihar Suvorau, Marlon Dumas","doi":"10.1016/j.softx.2025.102157","DOIUrl":"10.1016/j.softx.2025.102157","url":null,"abstract":"<div><div>Business process simulation is a technique that enables analysts to estimate the impact of changes to a business process with respect to time and cost-related performance measures. Specifically, business process simulation allows analysts to answer questions such as “what would be the cycle time of a process if 10% of the resources become unavailable for an extended period of time, or if we automate one of the activities in the process?” The starting point of business process simulation is a model capturing the possible sequences of activities of a process, the distribution of processing times of each activity, the resources available to perform each activity in a process, and other parameters that capture the workload and behavior of resources. Designing simulation models by hand is overly time-consuming and error-prone. To address this shortcoming, several methods have been proposed to automatically discover business process simulation models from event logs extracted from enterprise information systems. This paper introduces SIMOD, a Python package to automatically discover business process simulation models from event logs. SIMOD applies a range of statistical and process mining techniques to discover a process model from an event log and to enhance it with simulation parameters.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102157"},"PeriodicalIF":2.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SoftwareXPub Date : 2025-04-15DOI: 10.1016/j.softx.2025.102148
Antonio Manjavacas , Juan Gómez-Romero , Damien Ernst , Manuel A. Vázquez-Barroso , Francisco Martín-Fuertes
{"title":"MELGYM: A dynamic control interface for MELCOR simulations","authors":"Antonio Manjavacas , Juan Gómez-Romero , Damien Ernst , Manuel A. Vázquez-Barroso , Francisco Martín-Fuertes","doi":"10.1016/j.softx.2025.102148","DOIUrl":"10.1016/j.softx.2025.102148","url":null,"abstract":"<div><div>MELCOR is a computer code for the simulation and safety analysis of nuclear facilities, comprising several modules for the calculation of thermal–hydraulic phenomena and aerosol physics. However, MELCOR offers limited support for interactive control, such as valve opening or flow rates redefinition, as its batch execution mode does not allow real-time modification of model parameters. To overcome this limitation, we present MELGYM, a Gymnasium-based interface that facilitates interactive control in MELCOR. MELGYM enables the training of reinforcement learning agents on MELCOR simulations, introducing real-time interaction and enabling the definition of reward functions based on simulation outcomes.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102148"},"PeriodicalIF":2.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SoftwareXPub Date : 2025-04-14DOI: 10.1016/j.softx.2025.102156
Pablo Rodríguez , Sergio Laso , Javier Berrocal , Pablo Fernández , Antonio Ruiz-Cortés , Juan Manuel Murillo
{"title":"Computing Continuum Simulator: A comprehensive framework for continuum architecture evaluation","authors":"Pablo Rodríguez , Sergio Laso , Javier Berrocal , Pablo Fernández , Antonio Ruiz-Cortés , Juan Manuel Murillo","doi":"10.1016/j.softx.2025.102156","DOIUrl":"10.1016/j.softx.2025.102156","url":null,"abstract":"<div><div>The Computing Continuum paradigm is essential for meeting the needs of IoT applications that demand real-time processing, reliable connectivity, and low-latency response. Unlike traditional cloud models, Computing Continuum integrates resources across edge, fog, and cloud layers, bringing data processing closer to its source. It is crucial in fields like healthcare, industry, and agriculture, where strict quality requirements have significant economic and social impacts. However, evaluating the performance and reliability of continuum architectures is challenging due to the complexity and high costs of setting up customizable and scalable near-realistic multi-layered environments. To address these challenges, we introduce the Computing Continuum Simulator framework, specifically designed to evaluate the deployment architecture – both physical and logical – of continuum environments. It enables the deployment of large Computing Continuum scenarios, customizing device types, network infrastructure, and custom application setups to accurately simulate and evaluate near real-world conditions. Implemented as a Software as a Service, it minimizes required computational demands on the user-side and integrates seamlessly into DevOps workflows, simplifying deployment, testing, and adoption by software companies, offering a pricing plan to ensure accessibility for various needs. Scalability tests showed the framework maintains stable run times, with different simulation sizes depending on the pricing plan. This consistency underscores the robustness and its suitability for customizable and scalable continuum architecture evaluations.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102156"},"PeriodicalIF":2.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SoftwareXPub Date : 2025-04-11DOI: 10.1016/j.softx.2025.102137
Jinfeng Ma , Hua Zheng , Ruonan Li , Kaifeng Rao , Yanzheng Yang , Weifeng Li
{"title":"Version [2.0] - [VIC-Borg: Multiobjective automatic calibration toolkit for VIC model]","authors":"Jinfeng Ma , Hua Zheng , Ruonan Li , Kaifeng Rao , Yanzheng Yang , Weifeng Li","doi":"10.1016/j.softx.2025.102137","DOIUrl":"10.1016/j.softx.2025.102137","url":null,"abstract":"<div><div>The VIC-Borg tool facilitates multi-objective automatic calibration for the variable infiltration capacity (VIC) model, but its efficiency was constrained by single-machine performance. To enhance calibration efficiency, we developed a parallelized, distributed optimization tool using MPI. It orchestrates computing tasks, enabling multiple VIC simulations per node while preventing result overwrites. Case studies demonstrate that the tool achieves a speedup of 31 using 50 computing cores, significantly enhancing performance. This tool simplifies computational setup, making it ideal for advanced users in distributed environments.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102137"},"PeriodicalIF":2.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SoftwareXPub Date : 2025-04-11DOI: 10.1016/j.softx.2025.102151
Lucia Hradecká, Filip Lux, Samuel Šul’an, Petr Matula
{"title":"Bio-Volumentations: A Python library for augmentation of volumetric image sequences","authors":"Lucia Hradecká, Filip Lux, Samuel Šul’an, Petr Matula","doi":"10.1016/j.softx.2025.102151","DOIUrl":"10.1016/j.softx.2025.102151","url":null,"abstract":"<div><div>Data augmentation is a widely used technique to increase generalization ability of deep learning models, especially when dealing with sparse training data. It is also crucial in biomedical applications, where annotated images are extremely rare due to high image dimensionality and expensive data acquisition processes. However, existing image augmentation toolboxes are not suitable for biomedical applications: they usually only support low-dimensional images or very few annotation types. To address this issue, we developed <em>Bio-Volumentations</em>—a Python library for transforming multidimensional biomedical images with image- and point-based annotations. Thanks to its universality, user-friendly interface, and independence of deep learning toolboxes, it facilitates efficient data preprocessing and augmentation in various computer vision tasks.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102151"},"PeriodicalIF":2.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SoftwareXPub Date : 2025-04-11DOI: 10.1016/j.softx.2025.102147
Neil He , Ming-Cheng Cheng , Yu Liu
{"title":"PyPOD-GP: Using PyTorch for accelerated chip-level thermal simulation of the GPU","authors":"Neil He , Ming-Cheng Cheng , Yu Liu","doi":"10.1016/j.softx.2025.102147","DOIUrl":"10.1016/j.softx.2025.102147","url":null,"abstract":"<div><div>The rising demand for high-performance computing (HPC) has made full-chip dynamic thermal simulation in many-core GPUs critical for optimizing performance and extending device lifespans. Proper orthogonal decomposition (POD) with Galerkin projection (GP) has shown to offer high accuracy and massive runtime improvements over direct numerical simulation (DNS). However, previous implementations of POD-GP use MPI-based libraries like PETSc and FEniCS and face significant runtime bottlenecks. We propose a <strong>Py</strong>Torch-based <strong>POD-GP</strong> library (PyPOD-GP), a GPU-optimized library for chip-level thermal simulation. PyPOD-GP achieves over <span><math><mrow><mn>23</mn><mo>.</mo><mn>4</mn><mo>×</mo></mrow></math></span> speedup in training and over <span><math><mrow><mn>10</mn><mo>×</mo></mrow></math></span> speedup in inference on a GPU with over 13,000 cores, with just 1.2% error over the device layer.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102147"},"PeriodicalIF":2.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SoftwareXPub Date : 2025-04-09DOI: 10.1016/j.softx.2025.102146
Amir Arslan Haghrah, Sehraneh Ghaemi, Mohammad Ali Badamchizadeh
{"title":"PyIT2FLS: An open-source Python framework for flexible and scalable development of type 1 and interval type 2 fuzzy logic models","authors":"Amir Arslan Haghrah, Sehraneh Ghaemi, Mohammad Ali Badamchizadeh","doi":"10.1016/j.softx.2025.102146","DOIUrl":"10.1016/j.softx.2025.102146","url":null,"abstract":"<div><div>Fuzzy set theory and fuzzy logic have become essential tools for converting expert knowledge into mathematical models and extracting meaningful insights from numerical data. Despite their wide application, a comprehensive and integrated tool for fuzzy logic development in Python has been lacking. To address this gap, we developed PyIT2FLS, an open-source framework for creating both Type-1 and Interval Type-2 fuzzy logic models. In addition to supporting a broad range of membership functions, t-norms, s-norms, and fuzzy operators, and facilitating the development of TSK and Mamdani systems, PyIT2FLS distinguishes itself from other toolkits by offering an easy integration of optimization algorithms, such as meta-heuristic techniques, for efficiently tuning fuzzy system parameters. This comprehensive toolkit bridges the divide between fuzzy logic theory and practical applications, enabling the rapid development of novel intelligent methods and schemes.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102146"},"PeriodicalIF":2.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SoftwareXPub Date : 2025-04-08DOI: 10.1016/j.softx.2025.102144
Hua He , Xiaofan Liu , Lei Xu , Guanghui Mei , Jifeng Xuan
{"title":"MergeBot: A platform of semi-structured merge conflict resolution for C/C++ code","authors":"Hua He , Xiaofan Liu , Lei Xu , Guanghui Mei , Jifeng Xuan","doi":"10.1016/j.softx.2025.102144","DOIUrl":"10.1016/j.softx.2025.102144","url":null,"abstract":"<div><div>In software version control systems, like Git, merge conflicts typically arise when multiple developers edit the same segment of a source file from different branches. Detecting merge conflicts can be automated; however, resolving merge conflicts is tedious and demanding. Developers have to manually read and trace the conflicts to reconcile the changes. In this paper, we introduce MergeBot, a platform designed for resolving merge conflicts in C/C++ codebases. MergeBot utilizes the techniques of static program analysis to recommend the resolution of merge conflicts via a user-friendly graphical user interface. In the resolution of merge conflicts, MergeBot can help reduce introducing potential errors via real-time visualization of code differences between pre-resolution and post-resolution versions; meanwhile, MergeBot can avoid error propagation via the prevention of staging or committing unresolved conflicts. We demonstrate the extensibility of semi-structured merge through an implementation in C/C++. The semi-structured merge combines structural merge of program entities (e.g., functions, classes) with unstructured merge of code lines. A preliminary evaluation on 10 widely-used open-source C/C++ projects demonstrates the effectiveness of MergeBot with the precision of 62.9% and the accuracy of 42.4% in resolving merge conflicts.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102144"},"PeriodicalIF":2.4,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143790824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SoftwareXPub Date : 2025-04-07DOI: 10.1016/j.softx.2025.102154
Vartul Shrivastava , Shekhar Shukla
{"title":"FARM-VSS: A web-based visualizer and summarizer suite with Gen-AI-enabled interpretation for Fuzzy association rule mining","authors":"Vartul Shrivastava , Shekhar Shukla","doi":"10.1016/j.softx.2025.102154","DOIUrl":"10.1016/j.softx.2025.102154","url":null,"abstract":"<div><div>In the current landscape of data analytics, Fuzzy Association Rule Mining (FARM) is being extensively employed to produce interpretable fuzzy rules. In the literature, various toolkits exist that assist practitioners in performing FARM-based inference on their dataset, but a comprehensive open-source GUI-enabled toolkit that facilitates Generative AI-based inference, customizable fuzzy partitioning and brute-force FARM Rules Explorer is scarce in existing toolkits. This research aims to bridge this gap by offering a comprehensive technical suite for FARM and Weighted FARM (WFARM) using the Fuzzy Apriori algorithm to aid researchers and practitioners.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102154"},"PeriodicalIF":2.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SoftwareXPub Date : 2025-04-05DOI: 10.1016/j.softx.2025.102142
Daniel D. Yanyachi, Yamir H. Anco-Agüero, German A. Echaiz, Miguel A. Esquivel, Alfredo Mamani-Saico, Pablo R. Yanyachi
{"title":"Laser_RobMap: An open source ROS2 compatible tool for 3D mapping using a Mobile Robot and 2D LiDAR","authors":"Daniel D. Yanyachi, Yamir H. Anco-Agüero, German A. Echaiz, Miguel A. Esquivel, Alfredo Mamani-Saico, Pablo R. Yanyachi","doi":"10.1016/j.softx.2025.102142","DOIUrl":"10.1016/j.softx.2025.102142","url":null,"abstract":"<div><div>We propose a tool for implementing a low-cost 3D mapping system using a vertically mounted 2D LiDAR sensor on a mobile robot, compatible with ROS2. The tool adapts to any ROS2-enabled robot that provides inertial and odometry data, with configurable parameters to optimize performance on systems with limited computational resources. Its main objective is to deliver a cost-effective 3D mapping solution for autonomous navigation using 3D SLAM. The results can be exported as 3D point clouds in PCD, LAS, and PLY formats, with an optional voxelization feature for efficient data management.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102142"},"PeriodicalIF":2.4,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}