Pub Date : 2025-05-19DOI: 10.1016/j.simpa.2025.100767
Azeem Ahmad , Muhammad Rashid Naeem , Yasir Javed , Mohammad Akour
This paper presents Eiffel-Store, an open-source tool for real-time traceability in Continuous Integration (CI) pipelines. Unlike traditional batch visualization tools, Eiffel-Store dynamically visualizes live Eiffel events from CI tools (e.g., Jenkins) using MongoDB and Meteor.js. It supports incremental updates, enabling users to trace faults back to specific commits across the pipeline. Events can be streamed from RabbitMQ or added manually, offering flexibility for diverse workflows. By connecting code changes to final product faults, Eiffel-Store improves transparency, debugging, and quality assurance. The tool has been tested with industry partners and is available publicly to promote adoption and further development.
{"title":"Reconstructing software evolution: Traceability from code commits to fault manifestation in CI","authors":"Azeem Ahmad , Muhammad Rashid Naeem , Yasir Javed , Mohammad Akour","doi":"10.1016/j.simpa.2025.100767","DOIUrl":"10.1016/j.simpa.2025.100767","url":null,"abstract":"<div><div>This paper presents <em>Eiffel-Store</em>, an open-source tool for real-time traceability in Continuous Integration (CI) pipelines. Unlike traditional batch visualization tools, Eiffel-Store dynamically visualizes live Eiffel events from CI tools (e.g., Jenkins) using MongoDB and Meteor.js. It supports incremental updates, enabling users to trace faults back to specific commits across the pipeline. Events can be streamed from RabbitMQ or added manually, offering flexibility for diverse workflows. By connecting code changes to final product faults, Eiffel-Store improves transparency, debugging, and quality assurance. The tool has been tested with industry partners and is available publicly to promote adoption and further development.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100767"},"PeriodicalIF":1.3,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-15DOI: 10.1016/j.simpa.2025.100768
Muhamad Keenan Ario , Muhammad Fikri Hasani , Khairatul Balqis , Messya Carment
Extended reality in education has advanced, offering safe, immersive simulations. Agriculture, a key area, lacks urban exposure. HoloFarm, a VR-based farming simulation, addresses this gap using Unity and C#. It integrates physical movement, joystick navigation, and spatial audio for crop cultivation. Evaluated with 27 urban users via the Igroup Presence Questionnaire, it showed strong spatial (M=5.59) and general presence (M=5.81), though realism (M=4.10) and involvement (M=4.77). Future updates will enhance realism and enable collaborative learning, bridging theoretical and practical agricultural knowledge.
{"title":"HoloFarm: Enhancing agricultural learning through immersive technology","authors":"Muhamad Keenan Ario , Muhammad Fikri Hasani , Khairatul Balqis , Messya Carment","doi":"10.1016/j.simpa.2025.100768","DOIUrl":"10.1016/j.simpa.2025.100768","url":null,"abstract":"<div><div>Extended reality in education has advanced, offering safe, immersive simulations. Agriculture, a key area, lacks urban exposure. HoloFarm, a VR-based farming simulation, addresses this gap using Unity and C#. It integrates physical movement, joystick navigation, and spatial audio for crop cultivation. Evaluated with 27 urban users via the Igroup Presence Questionnaire, it showed strong spatial (M=5.59) and general presence (M=5.81), though realism (M=4.10) and involvement (M=4.77). Future updates will enhance realism and enable collaborative learning, bridging theoretical and practical agricultural knowledge.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100768"},"PeriodicalIF":1.3,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144105934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-05DOI: 10.1016/j.simpa.2025.100761
Achour Khaoula , Lachgar Mohamed , Elloubab Aya , Ait Ouahda Younes , Laanaoui My Driss , Ourahay Mustapha
EduXgame is a gamified mobile application designed to enhance the learning experience of secondary education students. The application integrates AI-driven content generation, gamification features, and interactive learning tools such as quizzes, flipcards, and matching games. It provides educators with a web interface to upload chapters, which are processed by an AI model to generate learning material dynamically. eduXgame transforms traditional learning methods into engaging, competitive, and interactive experiences, making education more accessible and enjoyable for students.
{"title":"EduXgame: Gamified learning for secondary education","authors":"Achour Khaoula , Lachgar Mohamed , Elloubab Aya , Ait Ouahda Younes , Laanaoui My Driss , Ourahay Mustapha","doi":"10.1016/j.simpa.2025.100761","DOIUrl":"10.1016/j.simpa.2025.100761","url":null,"abstract":"<div><div>EduXgame is a gamified mobile application designed to enhance the learning experience of secondary education students. The application integrates AI-driven content generation, gamification features, and interactive learning tools such as quizzes, flipcards, and matching games. It provides educators with a web interface to upload chapters, which are processed by an AI model to generate learning material dynamically. eduXgame transforms traditional learning methods into engaging, competitive, and interactive experiences, making education more accessible and enjoyable for students.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100761"},"PeriodicalIF":1.3,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-05DOI: 10.1016/j.simpa.2025.100760
Jinhang Jiang , Ben Liu , Weiyao Peng , Karthik Srinivasan
TextRegress is an open-source Python package that leverages state-of-the-art deep learning techniques to perform regression analysis on long-form text data. Departing from conventional text mining tools that are confined to classification, sentiment, or readability metrics, TextRegress provides a unified framework for conducting predictive modeling of continuous outcomes. By integrating advanced encoding methods – including transformer-based embeddings, TF-IDF, and pre-trained Hugging Face models – with a robust PyTorch Lightning backend, TextRegress efficiently processes long texts through automatic chunking and dynamic feature integration. Its flexible architecture and customizable training paradigms empower researchers and practitioners across diverse domains to deploy sophisticated regression models, fostering reproducibility and accelerating innovation in text analytics.
{"title":"TextRegress: A Python package for advanced regression analysis on long-form text data","authors":"Jinhang Jiang , Ben Liu , Weiyao Peng , Karthik Srinivasan","doi":"10.1016/j.simpa.2025.100760","DOIUrl":"10.1016/j.simpa.2025.100760","url":null,"abstract":"<div><div>TextRegress is an open-source Python package that leverages state-of-the-art deep learning techniques to perform regression analysis on long-form text data. Departing from conventional text mining tools that are confined to classification, sentiment, or readability metrics, TextRegress provides a unified framework for conducting predictive modeling of continuous outcomes. By integrating advanced encoding methods – including transformer-based embeddings, TF-IDF, and pre-trained Hugging Face models – with a robust PyTorch Lightning backend, TextRegress efficiently processes long texts through automatic chunking and dynamic feature integration. Its flexible architecture and customizable training paradigms empower researchers and practitioners across diverse domains to deploy sophisticated regression models, fostering reproducibility and accelerating innovation in text analytics.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100760"},"PeriodicalIF":1.3,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-02DOI: 10.1016/j.simpa.2025.100753
Seifallah Elfetni , Reza Darvishi Kamachali
This paper introduces PINNs-MPF, a novel Machine Learning-based solver designed for Multi-Phase-Field (MPF) and diffuse interface simulations, offering innovative approaches to address complex challenges in addressing microstructure evolution in polycrystalline materials using Machine Learning. The framework not only surpasses current limitations in handling multi-phase problems but also allows for potential upscaling to tackle more intricate scenarios. Developed in Python, the related code leverages optimized libraries like TensorFlow, showcasing efficiency and potential scalability in materials science and engineering simulations. This framework, integrating advanced techniques such as multi-networking and training optimization, setting a new standard in predictive capabilities and understanding complex physical phenomena.
{"title":"PINNs-MPF: An Efficient Physics-Informed Machine Learning-based Solver for Multi-Phase-Field Simulations using Tensorflow","authors":"Seifallah Elfetni , Reza Darvishi Kamachali","doi":"10.1016/j.simpa.2025.100753","DOIUrl":"10.1016/j.simpa.2025.100753","url":null,"abstract":"<div><div>This paper introduces PINNs-MPF, a novel Machine Learning-based solver designed for Multi-Phase-Field (MPF) and diffuse interface simulations, offering innovative approaches to address complex challenges in addressing microstructure evolution in polycrystalline materials using Machine Learning. The framework not only surpasses current limitations in handling multi-phase problems but also allows for potential upscaling to tackle more intricate scenarios. Developed in Python, the related code leverages optimized libraries like TensorFlow, showcasing efficiency and potential scalability in materials science and engineering simulations. This framework, integrating advanced techniques such as multi-networking and training optimization, setting a new standard in predictive capabilities and understanding complex physical phenomena.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100753"},"PeriodicalIF":1.3,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-30DOI: 10.1016/j.simpa.2025.100763
Rudy Prietno , Santana Yuda Pradata , Raka Satya Prasasta , Gemilang Santiyuda , Muhammad Alfian Amrizal , Tri Kuntoro Priyambodo , Vincent F. Yu
Distributing medical supplies involves complex logistical challenges, including the need for optimized delivery routes and efficient packing. Medicines, whether ordered in small quantities or in bulk, are packed into cardboard boxes, which affect cargo dimensions, loading plans, and available delivery routes. Additionally, some medicines require refrigeration, making it necessary to coordinate both reefer and standard trucks. This study introduces MedRoPax, a comprehensive software solution designed to address these challenges. MedRoPax solves the 3D Loading Heterogeneous Vehicle Routing Problem (3LHVRP) and provides user-friendly tools for packing, loading visualization, and route planning. While tailored for medical supply distribution, MedRoPax is also well-suited for other logistics operations that demand both efficiency and safety.
{"title":"MedRoPax: A comprehensive software for solving heterogeneous vehicle routing problem with 3D loading constraints and cardboard box packing for medical supply distribution","authors":"Rudy Prietno , Santana Yuda Pradata , Raka Satya Prasasta , Gemilang Santiyuda , Muhammad Alfian Amrizal , Tri Kuntoro Priyambodo , Vincent F. Yu","doi":"10.1016/j.simpa.2025.100763","DOIUrl":"10.1016/j.simpa.2025.100763","url":null,"abstract":"<div><div>Distributing medical supplies involves complex logistical challenges, including the need for optimized delivery routes and efficient packing. Medicines, whether ordered in small quantities or in bulk, are packed into cardboard boxes, which affect cargo dimensions, loading plans, and available delivery routes. Additionally, some medicines require refrigeration, making it necessary to coordinate both reefer and standard trucks. This study introduces MedRoPax, a comprehensive software solution designed to address these challenges. MedRoPax solves the 3D Loading Heterogeneous Vehicle Routing Problem (3LHVRP) and provides user-friendly tools for packing, loading visualization, and route planning. While tailored for medical supply distribution, MedRoPax is also well-suited for other logistics operations that demand both efficiency and safety.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100763"},"PeriodicalIF":1.3,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-29DOI: 10.1016/j.simpa.2025.100766
Leandro Rodrigues da Silva Souza , Daniel Hilário da Silva , Caio Tonus Ribeiro , Daiane Alves da Silva , Slawomir J. Nasuto , Catherine M. Sweeney-Reed , Adriano de Oliveira Andrade , Adriano Alves Pereira
Bibliometric analyses often depend on extracting metadata from large scientific databases, a process that is still largely manual, repetitive, and error prone. This paper presents PubMedMetaTool, an open-source Python-based solution that automates the retrieval and transformation of bibliographic metadata from PubMed, using either article titles or Digital Object Identifiers as input. The tool implements a modular pipeline that extracts metadata using NCBI’s Entrez programming utilities and transforms it into formats compatible with tools such as Bibliometrix, VOSviewer, and pyBibX. Designed to be transparent and configurable, the tool improves bibliometric workflow efficiency, accuracy, and interoperability workflows.
{"title":"PubMedMetaTool: Automated metadata extraction from PubMed using Python for bibliometric analysis","authors":"Leandro Rodrigues da Silva Souza , Daniel Hilário da Silva , Caio Tonus Ribeiro , Daiane Alves da Silva , Slawomir J. Nasuto , Catherine M. Sweeney-Reed , Adriano de Oliveira Andrade , Adriano Alves Pereira","doi":"10.1016/j.simpa.2025.100766","DOIUrl":"10.1016/j.simpa.2025.100766","url":null,"abstract":"<div><div>Bibliometric analyses often depend on extracting metadata from large scientific databases, a process that is still largely manual, repetitive, and error prone. This paper presents PubMedMetaTool, an open-source Python-based solution that automates the retrieval and transformation of bibliographic metadata from PubMed, using either article titles or Digital Object Identifiers as input. The tool implements a modular pipeline that extracts metadata using NCBI’s Entrez programming utilities and transforms it into formats compatible with tools such as Bibliometrix, VOSviewer, and pyBibX. Designed to be transparent and configurable, the tool improves bibliometric workflow efficiency, accuracy, and interoperability workflows.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100766"},"PeriodicalIF":1.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-29DOI: 10.1016/j.simpa.2025.100764
Daqing Yun , Liudong Zuo , Yi Gu , Chase Wu
This work presents an automated tool for optimizing control parameters in performance testing of big data transfer over long-fat network connections. Supporting both TCP and UDT protocols, the tool identifies the optimal configurations to enhance the efficiency of large-scale data transfers. A stochastic approximation algorithm is employed for parameter optimization, streamlining the protocol and parameter selection. The tool has been evaluated in various network scenarios, including long-haul connections in real-world high-performance networks. Its modular design also enables straightforward integration of additional data transfer protocols and alternative optimization methods.
{"title":"An automated parameter optimizer for data transfer performance testing","authors":"Daqing Yun , Liudong Zuo , Yi Gu , Chase Wu","doi":"10.1016/j.simpa.2025.100764","DOIUrl":"10.1016/j.simpa.2025.100764","url":null,"abstract":"<div><div>This work presents an automated tool for optimizing control parameters in performance testing of big data transfer over long-fat network connections. Supporting both TCP and UDT protocols, the tool identifies the optimal configurations to enhance the efficiency of large-scale data transfers. A stochastic approximation algorithm is employed for parameter optimization, streamlining the protocol and parameter selection. The tool has been evaluated in various network scenarios, including long-haul connections in real-world high-performance networks. Its modular design also enables straightforward integration of additional data transfer protocols and alternative optimization methods.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100764"},"PeriodicalIF":1.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-29DOI: 10.1016/j.simpa.2025.100765
Sai Jeevan Puchakayala , Allen Bijo T. , Aswathy Ravikumar , Harini Sriraman
Nomad Analytix is an innovative business intelligence tool that uses state-of-the-art vision models to transform data analysis. This software automates complex tasks traditionally handled by data analysts, empowering non-technical teams such as marketing and sales to access advanced data analysis easily. By using natural language prompts, users can interact with data intuitively and gain valuable insights without needing extensive technical expertise. A prototype of the software, built on the Streamlit platform, will showcase its ability to generate visualizations from various data sources, including CSV, JSON, SQLite, Excel, and databases, with potential extensions to data warehouses. The integration of Vision Language Models GPT 4 Omni and GPT 4 Turbo- with this framework provides a seamless interface for data querying, visualization creation, and recommendation generation. Nomad Analytix serves as an inclusive, intelligent, and intuitive solution, bridging the gap between data and decision-making across diverse industries.
{"title":"Nomad Analytix: Text-rich visual reasoning using vision models for insights and recommendations","authors":"Sai Jeevan Puchakayala , Allen Bijo T. , Aswathy Ravikumar , Harini Sriraman","doi":"10.1016/j.simpa.2025.100765","DOIUrl":"10.1016/j.simpa.2025.100765","url":null,"abstract":"<div><div>Nomad Analytix is an innovative business intelligence tool that uses state-of-the-art vision models to transform data analysis. This software automates complex tasks traditionally handled by data analysts, empowering non-technical teams such as marketing and sales to access advanced data analysis easily. By using natural language prompts, users can interact with data intuitively and gain valuable insights without needing extensive technical expertise. A prototype of the software, built on the Streamlit platform, will showcase its ability to generate visualizations from various data sources, including CSV, JSON, SQLite, Excel, and databases, with potential extensions to data warehouses. The integration of Vision Language Models GPT 4 Omni and GPT 4 Turbo- with this framework provides a seamless interface for data querying, visualization creation, and recommendation generation. Nomad Analytix serves as an inclusive, intelligent, and intuitive solution, bridging the gap between data and decision-making across diverse industries.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100765"},"PeriodicalIF":1.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-28DOI: 10.1016/j.simpa.2025.100759
Johnny Chan, Brice Valentin Kok-Shun
This paper presents an AI-powered software solution for detecting and categorising sponsored advertisement segments in YouTube videos. By combining GPT-4 for ad identification, KeyBERT for keyword extraction, and custom prompts for grouping keywords into concise categories, the software provides a scalable and efficient alternative to traditional ad detection methods. It processes both auto-generated and manual transcripts, ensuring adaptability across varied contexts. The tool enables a deeper understanding of advertising strategies and ad-content alignment while maintaining ease of use and reproducibility. This work highlights the potential of AI in transforming digital advertisement analysis.
{"title":"An AI-powered solution for detecting and categorising sponsored ad segments in YouTube videos","authors":"Johnny Chan, Brice Valentin Kok-Shun","doi":"10.1016/j.simpa.2025.100759","DOIUrl":"10.1016/j.simpa.2025.100759","url":null,"abstract":"<div><div>This paper presents an AI-powered software solution for detecting and categorising sponsored advertisement segments in YouTube videos. By combining GPT-4 for ad identification, KeyBERT for keyword extraction, and custom prompts for grouping keywords into concise categories, the software provides a scalable and efficient alternative to traditional ad detection methods. It processes both auto-generated and manual transcripts, ensuring adaptability across varied contexts. The tool enables a deeper understanding of advertising strategies and ad-content alignment while maintaining ease of use and reproducibility. This work highlights the potential of AI in transforming digital advertisement analysis.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100759"},"PeriodicalIF":1.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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