Pub Date : 2025-02-13DOI: 10.1016/j.softx.2025.102088
Huseyin Atasoy , Yakup Kutlu
Neither machines nor even human can learn something not represented well enough. Therefore, feature extraction is one of the most important topics in machine learning. Deep convolutional neural networks are able to catch distinguishing features that can represent images or other digital signals. This makes them very popular in signal processing and especially in image processing community. Despite the proven success of these networks, training processes of them are often expensive in terms of time and required hardware capabilities. In this paper, a user-friendly standalone Windows application titled “Convolutional Neural Network Feature Extraction Tools” (CNNFET) is presented. The application consists of tools that extract features from image sets using certain layers of pre-trained CNNs, process them, perform classifications on them and export features for further processing in Matlab or the popular machine learning software Weka.
{"title":"CNNFET: Convolutional neural network feature Extraction Tools","authors":"Huseyin Atasoy , Yakup Kutlu","doi":"10.1016/j.softx.2025.102088","DOIUrl":"10.1016/j.softx.2025.102088","url":null,"abstract":"<div><div>Neither machines nor even human can learn something not represented well enough. Therefore, feature extraction is one of the most important topics in machine learning. Deep convolutional neural networks are able to catch distinguishing features that can represent images or other digital signals. This makes them very popular in signal processing and especially in image processing community. Despite the proven success of these networks, training processes of them are often expensive in terms of time and required hardware capabilities. In this paper, a user-friendly standalone Windows application titled “Convolutional Neural Network Feature Extraction Tools” (CNNFET) is presented. The application consists of tools that extract features from image sets using certain layers of pre-trained CNNs, process them, perform classifications on them and export features for further processing in Matlab or the popular machine learning software Weka.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102088"},"PeriodicalIF":2.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394437","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}
Pub Date : 2025-02-12DOI: 10.1016/j.softx.2025.102066
Johannes Cepicka , Lukas D. Sauer , Marietta Kirchner, Meinhard Kieser, Stella Erdmann
Sample size determination is crucial in phase II/III drug development programs, impacting the likelihood of meeting program objectives. Within a utility-based framework, methods for optimal designs were developed recently, i.e., optimal go/no-go decision rules (whether to stop or to proceed to phase III) and optimal sample sizes minimizing the cost while maximizing the chances of achieving the program objective. These approaches can accommodate diverse scenarios like multiple phase III trials, arms, or endpoints. To facilitate the usability, the drugdevelopR R package and R Shiny applications were implemented. A sophisticated quality validation concept consisting of measures for archiving, versioning, bug reporting, and code documentation was developed, assuring reliable results.
{"title":"drugdevelopR: Planning of phase II/III drug development programs with optimal sample size allocation and Go/No-go decision rules in R","authors":"Johannes Cepicka , Lukas D. Sauer , Marietta Kirchner, Meinhard Kieser, Stella Erdmann","doi":"10.1016/j.softx.2025.102066","DOIUrl":"10.1016/j.softx.2025.102066","url":null,"abstract":"<div><div>Sample size determination is crucial in phase II/III drug development programs, impacting the likelihood of meeting program objectives. Within a utility-based framework, methods for optimal designs were developed recently, i.e., optimal go/no-go decision rules (whether to stop or to proceed to phase III) and optimal sample sizes minimizing the cost while maximizing the chances of achieving the program objective. These approaches can accommodate diverse scenarios like multiple phase III trials, arms, or endpoints. To facilitate the usability, the <span>drugdevelopR R</span> package and <span>R</span> Shiny applications were implemented. A sophisticated quality validation concept consisting of measures for archiving, versioning, bug reporting, and code documentation was developed, assuring reliable results.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102066"},"PeriodicalIF":2.4,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386647","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}
Pub Date : 2025-02-12DOI: 10.1016/j.softx.2025.102075
Jiseung Pyun, Min Heo, Yehui Choe, Jongwook Jeong
Usability testing is crucial for enhancing the user experience of web applications, yet traditional methods remain prohibitively costly and complex. In this paper, we present WebTrace, a tool that facilitates lightweight and effective usability testing by remotely tracking user interactions, such as clicks and keyboard inputs, through a browser extension. This simplifies testing and deployment across diverse environments. WebTrace also includes features to manage usability testing and abstract repetitive data, thereby enhancing analysis efficiency. By simplifying participation and supporting thorough analysis, WebTrace reduces the overall costs of usability testing while improving both time and resource management.
{"title":"WebTrace: An enhanced system for remote user interaction tracking and analysis in web applications","authors":"Jiseung Pyun, Min Heo, Yehui Choe, Jongwook Jeong","doi":"10.1016/j.softx.2025.102075","DOIUrl":"10.1016/j.softx.2025.102075","url":null,"abstract":"<div><div>Usability testing is crucial for enhancing the user experience of web applications, yet traditional methods remain prohibitively costly and complex. In this paper, we present WebTrace, a tool that facilitates lightweight and effective usability testing by remotely tracking user interactions, such as clicks and keyboard inputs, through a browser extension. This simplifies testing and deployment across diverse environments. WebTrace also includes features to manage usability testing and abstract repetitive data, thereby enhancing analysis efficiency. By simplifying participation and supporting thorough analysis, WebTrace reduces the overall costs of usability testing while improving both time and resource management.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102075"},"PeriodicalIF":2.4,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394797","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}
Pub Date : 2025-02-12DOI: 10.1016/j.softx.2025.102078
Miguel Bermudo, Daniel Ayala, Inma Hernández, David Ruiz, Miguel Toro
Knowledge Graphs are powerful data structures used by large IT companies and the scientific community alike. They aid in the representation of related information by means of nodes connected through links indicating types of relations. These graphs are used as the basis for several smart applications, such as question answering or product recommendation. However, they are built in an automated unsupervised way, which leads to gaps in information, usually in the form of missing links between related entities in the original data source, which have to be added later by completion techniques.
SpaceRL is an end-to-end Python framework designed for the generation of reinforcement learning (RL) agents, which can be used to complete knowledge graphs through link discovery. The purpose of the generated agents is to help identify missing links in a knowledge graph by finding paths that implicitly connect two nodes, incidentally providing a reasoned explanation for the inferred new link. The generation of such agents is a complex task, even more so for a non-expert user.
SpaceRL is meant to overcome these limitations by providing a flexible set of tools designed with a wide variety of customization options, in order to adapt to different users’ needs, while also including a variety of state-of-the-art RL algorithms and several embedding models that can be combined to optimize the agents performance. Furthermore, SpaceRL offers different interfaces to make it available either locally (programmatically or via a GUI), or through an OpenAPI-compliant REST API.
{"title":"SpaceRL — A reinforcement learning-based knowledge graph driver","authors":"Miguel Bermudo, Daniel Ayala, Inma Hernández, David Ruiz, Miguel Toro","doi":"10.1016/j.softx.2025.102078","DOIUrl":"10.1016/j.softx.2025.102078","url":null,"abstract":"<div><div>Knowledge Graphs are powerful data structures used by large IT companies and the scientific community alike. They aid in the representation of related information by means of nodes connected through links indicating types of relations. These graphs are used as the basis for several smart applications, such as question answering or product recommendation. However, they are built in an automated unsupervised way, which leads to gaps in information, usually in the form of missing links between related entities in the original data source, which have to be added later by completion techniques.</div><div>SpaceRL is an end-to-end Python framework designed for the generation of reinforcement learning (RL) agents, which can be used to complete knowledge graphs through link discovery. The purpose of the generated agents is to help identify missing links in a knowledge graph by finding paths that implicitly connect two nodes, incidentally providing a reasoned explanation for the inferred new link. The generation of such agents is a complex task, even more so for a non-expert user.</div><div>SpaceRL is meant to overcome these limitations by providing a flexible set of tools designed with a wide variety of customization options, in order to adapt to different users’ needs, while also including a variety of state-of-the-art RL algorithms and several embedding models that can be combined to optimize the agents performance. Furthermore, SpaceRL offers different interfaces to make it available either locally (programmatically or via a GUI), or through an OpenAPI-compliant REST API.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102078"},"PeriodicalIF":2.4,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394798","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}
Pub Date : 2025-02-11DOI: 10.1016/j.softx.2025.102072
Mingyuan Li , Duan Wang , Erick Purwanto , Thomas Selig , Qing Zhang , Hai-Ning Liang
The abstract nature of algorithms and data structures poses challenges for students, and the integration of visualization into comprehensive learning systems remains underexplored. This article presents VisualCodeMOOC, incorporating VisualCodeChat, a conversational agent that enhances algorithm and data structure learning through dynamic visualizations and personalized feedback. The platform effectively addresses these challenges, improving student engagement and comprehension. With instructions structuring, novel response-based algorithm visualization, exercise design, VisualCodeMOOC provides a cohesive and supportive learning environment that promotes active learning. Evaluation results demonstrate its usability, responsiveness, and educational value, confirming its potential as a promising tool for advancing computer science education.
{"title":"VisualCodeMOOC: A course platform for algorithms and data structures integrating a conversational agent for enhanced learning through dynamic visualizations","authors":"Mingyuan Li , Duan Wang , Erick Purwanto , Thomas Selig , Qing Zhang , Hai-Ning Liang","doi":"10.1016/j.softx.2025.102072","DOIUrl":"10.1016/j.softx.2025.102072","url":null,"abstract":"<div><div>The abstract nature of algorithms and data structures poses challenges for students, and the integration of visualization into comprehensive learning systems remains underexplored. This article presents VisualCodeMOOC, incorporating VisualCodeChat, a conversational agent that enhances algorithm and data structure learning through dynamic visualizations and personalized feedback. The platform effectively addresses these challenges, improving student engagement and comprehension. With instructions structuring, novel response-based algorithm visualization, exercise design, VisualCodeMOOC provides a cohesive and supportive learning environment that promotes active learning. Evaluation results demonstrate its usability, responsiveness, and educational value, confirming its potential as a promising tool for advancing computer science education.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102072"},"PeriodicalIF":2.4,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379181","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}
Pub Date : 2025-02-11DOI: 10.1016/j.softx.2025.102083
Boaz B. Tulu , Fitsum Teshome , Yiannis Ampatzidis , Niguss Solomon Hailegnaw , Haimanote K Bayabil
Unmanned Aerial Vehicles (UAVs) equipped with thermal and multispectral imaging capabilities, which offer high spatial and temporal resolutions, are becoming increasingly valuable for timely crop monitoring and informed decision-making in precision agriculture. However, processing and extracting useful information from UAV images is often complex, time-consuming, and requires specialized software, which limits its broader adoption for practical field implementations. To address these challenges, the Agriculture Sensing and Artificial Intelligence (AgriSenAI), a user-friendly Python-based desktop application, was developed to automate processing and information extraction from UAV-acquired thermal and multispectral imagery. AgriSenAI was developed by integrating advanced image processing with geospatial analysis to streamline field and plot extraction, plant canopy detection, noise removal, and extraction of information at pixel, plot, and field scales. The application was designed and tested using UAV-based thermal and multispectral imagery collected daily for three years from a research field at the University of Florida's Tropical Research and Education Center in Homestead, Florida. The research field consisted of 12 plots of green beans and 12 plots of sweet corn. The processing time and accuracy of AgriSenAI were evaluated. Results showed that AgriSenAI had a very high level of accuracy in extracting pixel values and significantly reduced processing time and costs compared with traditional approaches involving commercial software. The streamlined AgriSenAI workflow produced reliable canopy temperature information and vegetation indices, demonstrating the capacity to handle large-scale datasets and enhance precision agriculture through improved efficiency and accuracy in remote sensing data processing and information extraction, which could potentially be used to inform timely and data-driven crop management decisions.
{"title":"AgriSenAI: Automating UAV thermal and multispectral image processing for precision agriculture","authors":"Boaz B. Tulu , Fitsum Teshome , Yiannis Ampatzidis , Niguss Solomon Hailegnaw , Haimanote K Bayabil","doi":"10.1016/j.softx.2025.102083","DOIUrl":"10.1016/j.softx.2025.102083","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) equipped with thermal and multispectral imaging capabilities, which offer high spatial and temporal resolutions, are becoming increasingly valuable for timely crop monitoring and informed decision-making in precision agriculture. However, processing and extracting useful information from UAV images is often complex, time-consuming, and requires specialized software, which limits its broader adoption for practical field implementations. To address these challenges, the Agriculture Sensing and Artificial Intelligence (AgriSenAI), a user-friendly Python-based desktop application, was developed to automate processing and information extraction from UAV-acquired thermal and multispectral imagery. AgriSenAI was developed by integrating advanced image processing with geospatial analysis to streamline field and plot extraction, plant canopy detection, noise removal, and extraction of information at pixel, plot, and field scales. The application was designed and tested using UAV-based thermal and multispectral imagery collected daily for three years from a research field at the University of Florida's Tropical Research and Education Center in Homestead, Florida. The research field consisted of 12 plots of green beans and 12 plots of sweet corn. The processing time and accuracy of AgriSenAI were evaluated. Results showed that AgriSenAI had a very high level of accuracy in extracting pixel values and significantly reduced processing time and costs compared with traditional approaches involving commercial software. The streamlined AgriSenAI workflow produced reliable canopy temperature information and vegetation indices, demonstrating the capacity to handle large-scale datasets and enhance precision agriculture through improved efficiency and accuracy in remote sensing data processing and information extraction, which could potentially be used to inform timely and data-driven crop management decisions.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102083"},"PeriodicalIF":2.4,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386646","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}
Pub Date : 2025-02-10DOI: 10.1016/j.softx.2025.102079
Tomasz P. Pawlak, Jędrzej Potoniec
Contemporary software guides and executes various business processes, such as production, delivery of goods, sales, and official procedures. However, due to software flexibility, unforeseen circumstances, and exceptional conditions, processes may not always be executed as intended. Without proper monitoring, these deviations can go unnoticed. ProcessM is an open-source software designed for users with limited technical expertise specializing in business process analysis. It integrates with multiple database management systems to extract and transform data into event logs, and monitor databases for new events. ProcessM generates process models in standard representations like BPMN and tracks changes in processes. It also calculates Key Performance Indicators (KPIs), breaking them down by individual activities and relationships. ProcessM is designed for auditing, decision-making, and operations management across industries.
{"title":"ProcessM: Intelligent Process Mining software","authors":"Tomasz P. Pawlak, Jędrzej Potoniec","doi":"10.1016/j.softx.2025.102079","DOIUrl":"10.1016/j.softx.2025.102079","url":null,"abstract":"<div><div>Contemporary software guides and executes various business processes, such as production, delivery of goods, sales, and official procedures. However, due to software flexibility, unforeseen circumstances, and exceptional conditions, processes may not always be executed as intended. Without proper monitoring, these deviations can go unnoticed. ProcessM is an open-source software designed for users with limited technical expertise specializing in business process analysis. It integrates with multiple database management systems to extract and transform data into event logs, and monitor databases for new events. ProcessM generates process models in standard representations like BPMN and tracks changes in processes. It also calculates Key Performance Indicators (KPIs), breaking them down by individual activities and relationships. ProcessM is designed for auditing, decision-making, and operations management across industries.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102079"},"PeriodicalIF":2.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376612","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}
Pub Date : 2025-02-10DOI: 10.1016/j.softx.2025.102068
Adolfo Esteban , Julio Hernández , Javier Tajuelo , Miguel Ángel Rubio
The numerical simulation of the flow around a rigid object, such as a probe, placed at a complex interface to reproduce certain experimental conditions is a useful tool for the analysis and design of interfacial stress rheometers. To perform such simulations, we have developed iRheoFoam, an open-source package implemented in the OpenFOAM framework. It is based on the Boussinesq–Scriven constitutive law and the Navier–Stokes equations, includes various solvers for both time-dependent and steady problems, and allows easy incorporation of new interface models and solvers, making it a versatile and powerful tool.
{"title":"iRheoFoam: A package for simulating complex planar interfaces","authors":"Adolfo Esteban , Julio Hernández , Javier Tajuelo , Miguel Ángel Rubio","doi":"10.1016/j.softx.2025.102068","DOIUrl":"10.1016/j.softx.2025.102068","url":null,"abstract":"<div><div>The numerical simulation of the flow around a rigid object, such as a probe, placed at a complex interface to reproduce certain experimental conditions is a useful tool for the analysis and design of interfacial stress rheometers. To perform such simulations, we have developed <em>iRheoFoam</em>, an open-source package implemented in the OpenFOAM framework. It is based on the Boussinesq–Scriven constitutive law and the Navier–Stokes equations, includes various solvers for both time-dependent and steady problems, and allows easy incorporation of new interface models and solvers, making it a versatile and powerful tool.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102068"},"PeriodicalIF":2.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376613","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}
Pub Date : 2025-02-09DOI: 10.1016/j.softx.2025.102081
Faris Serdar Taşel , Efe Çi̇ftci̇
Recent studies suggest a potential link between the physical structure of mitochondria and neurodegenerative diseases. With advances in Electron Microscopy techniques, it has become possible to visualize the boundary and cristae structures of mitochondria in detail. Segmenting mitochondria from microscopy images remains challenging due to image quality and complex morphology of mitochondria, including cristae and the other subcellular structures. It is crucial to automatically segment mitochondria from images exhibiting different mitochondrial boundary and crista characteristics to investigate the relationship between mitochondria and diseases. In this paper, we present a software solution for mitochondrial segmentation using an automatic validation scheme based on the general physical properties of mitochondria, highlighting boundaries in electron microscopy tomography images and generating corresponding 3D meshes. These capabilities help researchers conduct further investigations into mitochondrial morphology and explore its role in the mechanisms of neurodegenerative diseases.
{"title":"MitoSeg: Mitochondria segmentation tool","authors":"Faris Serdar Taşel , Efe Çi̇ftci̇","doi":"10.1016/j.softx.2025.102081","DOIUrl":"10.1016/j.softx.2025.102081","url":null,"abstract":"<div><div>Recent studies suggest a potential link between the physical structure of mitochondria and neurodegenerative diseases. With advances in Electron Microscopy techniques, it has become possible to visualize the boundary and cristae structures of mitochondria in detail. Segmenting mitochondria from microscopy images remains challenging due to image quality and complex morphology of mitochondria, including cristae and the other subcellular structures. It is crucial to automatically segment mitochondria from images exhibiting different mitochondrial boundary and crista characteristics to investigate the relationship between mitochondria and diseases. In this paper, we present a software solution for mitochondrial segmentation using an automatic validation scheme based on the general physical properties of mitochondria, highlighting boundaries in electron microscopy tomography images and generating corresponding 3D meshes. These capabilities help researchers conduct further investigations into mitochondrial morphology and explore its role in the mechanisms of neurodegenerative diseases.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102081"},"PeriodicalIF":2.4,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372721","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}
Pub Date : 2025-02-08DOI: 10.1016/j.softx.2025.102082
Razvan Raducu, Alain Villagrasa-Labrador, Ricardo J. Rodríguez, Pedro Álvarez
Malware attacks have been growing steadily in recent years, making more sophisticated detection methods necessary. These approaches typically rely on analyzing the behavior of malicious applications, for example by examining execution traces that capture their runtime behavior. However, many existing execution trace datasets are simplified, often resulting in the omission of relevant contextual information, which is essential to capture the full scope of a malware sample’s behavior. This paper introduces MALVADA, a flexible framework designed to generate extensive datasets of execution traces from Windows malware. These traces provide detailed insights into program behaviors and help malware analysts to classify a malware sample. MALVADA facilitates the creation of large datasets with minimal user effort, as demonstrated by the WinMET dataset, which includes execution traces from approximately 10,000 Windows malware samples.
{"title":"MALVADA: A framework for generating datasets of malware execution traces","authors":"Razvan Raducu, Alain Villagrasa-Labrador, Ricardo J. Rodríguez, Pedro Álvarez","doi":"10.1016/j.softx.2025.102082","DOIUrl":"10.1016/j.softx.2025.102082","url":null,"abstract":"<div><div>Malware attacks have been growing steadily in recent years, making more sophisticated detection methods necessary. These approaches typically rely on analyzing the behavior of malicious applications, for example by examining execution traces that capture their runtime behavior. However, many existing execution trace datasets are simplified, often resulting in the omission of relevant contextual information, which is essential to capture the full scope of a malware sample’s behavior. This paper introduces MALVADA, a flexible framework designed to generate extensive datasets of execution traces from Windows malware. These traces provide detailed insights into program behaviors and help malware analysts to classify a malware sample. MALVADA facilitates the creation of large datasets with minimal user effort, as demonstrated by the WinMET dataset, which includes execution traces from approximately 10,000 Windows malware samples.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102082"},"PeriodicalIF":2.4,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350566","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}
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