Pub Date : 2025-02-01DOI: 10.1016/j.softx.2025.102055
Gabriel Dubus , Maëlle Torterotot , Julie Béesau , Mathieu Dupont , Anatole Gros-Martial , Mathilde Michel , Elodie Morin , Paul Nguyen Hong Duc , Pierre-Yves Raumer , Olivier Adam , Flore Samaran , Dorian Cazau
Emerging detection and classification algorithms based on deep learning models require manageable large-scale manual annotations of ground truth data. To date, the challenge of creating large and accurate annotated datasets of underwater sounds has been a major obstacle to the development of robust recognition algorithms. APLOSE (Annotation PLatform for Ocean Sound Explorers) is an open-source, web-based tool which facilitates collaborative annotation campaigns in underwater acoustics. The platform was used to carry out research projects on inter-annotator variability, to build training and testing data sets for detection algorithms and to perform bioacoustics analysis on noisy datasets. In the future, it will enable the creation of high-quality reference datasets to test and train the new detection and classification algorithms.
{"title":"APLOSE: A web-based annotation platform for underwater passive acoustic monitoring","authors":"Gabriel Dubus , Maëlle Torterotot , Julie Béesau , Mathieu Dupont , Anatole Gros-Martial , Mathilde Michel , Elodie Morin , Paul Nguyen Hong Duc , Pierre-Yves Raumer , Olivier Adam , Flore Samaran , Dorian Cazau","doi":"10.1016/j.softx.2025.102055","DOIUrl":"10.1016/j.softx.2025.102055","url":null,"abstract":"<div><div>Emerging detection and classification algorithms based on deep learning models require manageable large-scale manual annotations of ground truth data. To date, the challenge of creating large and accurate annotated datasets of underwater sounds has been a major obstacle to the development of robust recognition algorithms. APLOSE (Annotation PLatform for Ocean Sound Explorers) is an open-source, web-based tool which facilitates collaborative annotation campaigns in underwater acoustics. The platform was used to carry out research projects on inter-annotator variability, to build training and testing data sets for detection algorithms and to perform bioacoustics analysis on noisy datasets. In the future, it will enable the creation of high-quality reference datasets to test and train the new detection and classification algorithms.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102055"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092183","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 : 2025-02-01DOI: 10.1016/j.softx.2024.102028
Lorenzo Longobardi , Villiam Sozinho , Hamza Altarturi , E. Fernando Cagua , Alexander Tilley
Small-scale fisheries account for almost 90 % of global fisheries employment and are responsible for landing >40 % of the world's fish catch. Yet their importance to livelihoods and food and nutrition security in Least Developed Countries are only recently emerging due to the logistical, financial, and capacity challenges of gathering and interpreting data in this diverse, dispersed and informal sector. Peskas was designed as a low-cost solution to tackle this problem, providing a template workflow for ingestion and analysis to a decision dashboard, which can be adapted to different contexts and needs.
{"title":"Peskas: Automated analytics for small-scale, data-deficient fisheries","authors":"Lorenzo Longobardi , Villiam Sozinho , Hamza Altarturi , E. Fernando Cagua , Alexander Tilley","doi":"10.1016/j.softx.2024.102028","DOIUrl":"10.1016/j.softx.2024.102028","url":null,"abstract":"<div><div>Small-scale fisheries account for almost 90 % of global fisheries employment and are responsible for landing >40 % of the world's fish catch. Yet their importance to livelihoods and food and nutrition security in Least Developed Countries are only recently emerging due to the logistical, financial, and capacity challenges of gathering and interpreting data in this diverse, dispersed and informal sector. Peskas was designed as a low-cost solution to tackle this problem, providing a template workflow for ingestion and analysis to a decision dashboard, which can be adapted to different contexts and needs.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102028"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127768","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 : 2025-02-01DOI: 10.1016/j.softx.2025.102042
Gauthier Grimmer, Romain Wenger, Valentin Chardon
The RiverDetectWood tool automates the classification and characterization of wood in river systems using very high spatial resolution (VHSR) aerial photographs. River wood plays a dual role in river management: it contributes to habitat diversity and geomorphological changes but can also increase flood risk and infrastructure damage. Traditional field-based river wood surveys are time-consuming and geographically limited. By integrating machine learning techniques and remote sensing, RiverDetectWood offers a cost-effective, scalable solution for monitoring river wood presence and extracting key variables, such as length, diameter, area, and volume. This tool is designed for easy use by river managers and researchers, facilitating long-term monitoring and decision-making.
{"title":"RiverDetectWood: A tool for automatic classification and quantification of river wood in river systems using aerial imagery","authors":"Gauthier Grimmer, Romain Wenger, Valentin Chardon","doi":"10.1016/j.softx.2025.102042","DOIUrl":"10.1016/j.softx.2025.102042","url":null,"abstract":"<div><div>The RiverDetectWood tool automates the classification and characterization of wood in river systems using very high spatial resolution (VHSR) aerial photographs. River wood plays a dual role in river management: it contributes to habitat diversity and geomorphological changes but can also increase flood risk and infrastructure damage. Traditional field-based river wood surveys are time-consuming and geographically limited. By integrating machine learning techniques and remote sensing, RiverDetectWood offers a cost-effective, scalable solution for monitoring river wood presence and extracting key variables, such as length, diameter, area, and volume. This tool is designed for easy use by river managers and researchers, facilitating long-term monitoring and decision-making.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102042"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127993","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 : 2025-02-01DOI: 10.1016/j.softx.2025.102069
Mohamed A. Eltahir Elabbas, Luis Olmos Camacho, Ignacio Pérez-Arriaga
Networks play a pivotal role in the energy transition, integrating renewable energy sources and facilitating sustainable energy systems. A huge amount of investment in energy networks is still required to make the energy transition a reality. When constructing network infrastructures, especially among countries at the regional level, the allocation of costs must be well-aligned with the economic benefits each cost-bearing party expects to obtain from the corresponding investments. However, efficiently allocating the cost of infrastructure networks has proven to be a difficult task, especially for cross-border infrastructure. Such a task is expected to become even more difficult in the future as networks become more meshed and coupled between the different sectors. InfraFair is a cost allocation tool for networks, both national and regional. It allocates the costs of different assets in the network to users based on their expected or actual usage. InfraFair is the first open-source software to provide this functionality for all flow-based infrastructure networks, such as electricity, hydrogen, gas and heat. It has been used in studies to allocate transmission network costs in Africa at the regional power pool level and has been developed as part of the OpenMod4Africa project. It is now available open-source for use by the wider scientific community.
{"title":"InfraFair: Infrastructure cost allocation","authors":"Mohamed A. Eltahir Elabbas, Luis Olmos Camacho, Ignacio Pérez-Arriaga","doi":"10.1016/j.softx.2025.102069","DOIUrl":"10.1016/j.softx.2025.102069","url":null,"abstract":"<div><div>Networks play a pivotal role in the energy transition, integrating renewable energy sources and facilitating sustainable energy systems. A huge amount of investment in energy networks is still required to make the energy transition a reality. When constructing network infrastructures, especially among countries at the regional level, the allocation of costs must be well-aligned with the economic benefits each cost-bearing party expects to obtain from the corresponding investments. However, efficiently allocating the cost of infrastructure networks has proven to be a difficult task, especially for cross-border infrastructure. Such a task is expected to become even more difficult in the future as networks become more meshed and coupled between the different sectors. InfraFair is a cost allocation tool for networks, both national and regional. It allocates the costs of different assets in the network to users based on their expected or actual usage. InfraFair is the first open-source software to provide this functionality for all flow-based infrastructure networks, such as electricity, hydrogen, gas and heat. It has been used in studies to allocate transmission network costs in Africa at the regional power pool level and has been developed as part of the OpenMod4Africa project. It is now available open-source for use by the wider scientific community.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102069"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127996","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 : 2025-02-01DOI: 10.1016/j.softx.2025.102063
Javier Solís-García, Jose E. Sánchez-López, Belén Vega-Márquez, Isabel A. Nepomuceno-Chamorro
Sepsis is a life-threatening immune response to infections, leading to organ dysfunction. Despite technological advances, the application of AI in sepsis prediction faces challenges, particularly due to the lack of standardized approaches for data preprocessing and imputation. This work introduces a new framework aimed at simplifying data management, ensuring AI models trained on time series data are both reliable and comprehensive. The framework facilitates the construction, preprocessing, and imputation of the Mimic-III database from PhysioNet, providing a standardized benchmark for future AI research in early sepsis prediction.
{"title":"A flexible framework for sepsis prediction: Standardizing data management and imputation in time series using MIMIC-III","authors":"Javier Solís-García, Jose E. Sánchez-López, Belén Vega-Márquez, Isabel A. Nepomuceno-Chamorro","doi":"10.1016/j.softx.2025.102063","DOIUrl":"10.1016/j.softx.2025.102063","url":null,"abstract":"<div><div>Sepsis is a life-threatening immune response to infections, leading to organ dysfunction. Despite technological advances, the application of AI in sepsis prediction faces challenges, particularly due to the lack of standardized approaches for data preprocessing and imputation. This work introduces a new framework aimed at simplifying data management, ensuring AI models trained on time series data are both reliable and comprehensive. The framework facilitates the construction, preprocessing, and imputation of the Mimic-III database from PhysioNet, providing a standardized benchmark for future AI research in early sepsis prediction.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102063"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128000","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 : 2025-02-01DOI: 10.1016/j.softx.2024.102029
Md. Shahidul Salim , Sk Imran Hossain , Tanim Jalal , Dhiman Kumer Bose , Mohammad Jahid Ibna Basher
Large language model (LLM) based interactive chatbots have been gaining popularity as a tool to serve organizational information among people. Building such a tool goes through several development phases i.e. (a) Data collection and preprocessing, (b) LLM fine-tuning, testing, and inference, and (c) Chat interface development. To streamline this development process, in this paper, we present the LLM Question–Answer (QA) builder, a web application, which assembles all the steps and makes it easy for technical and non-technical users to develop the LLM QA chatbot. The system allows the instruction fine-tuning of following LLMs: Zepyhr, Mistral, Llama-3, Phi, Flan-T5, and user provided model for organization-specific information retrieval (IR), which can be further enhanced by Retrieval Augmented Generation (RAG) techniques. We have added an automatic web crawling based RAG data scrapper. Also, our system contains a human evaluation feature and RAG metrics for assessing model quality.
{"title":"LLM based QA chatbot builder: A generative AI-based chatbot builder for question answering","authors":"Md. Shahidul Salim , Sk Imran Hossain , Tanim Jalal , Dhiman Kumer Bose , Mohammad Jahid Ibna Basher","doi":"10.1016/j.softx.2024.102029","DOIUrl":"10.1016/j.softx.2024.102029","url":null,"abstract":"<div><div>Large language model (LLM) based interactive chatbots have been gaining popularity as a tool to serve organizational information among people. Building such a tool goes through several development phases i.e. (a) Data collection and preprocessing, (b) LLM fine-tuning, testing, and inference, and (c) Chat interface development. To streamline this development process, in this paper, we present the LLM Question–Answer (QA) builder, a web application, which assembles all the steps and makes it easy for technical and non-technical users to develop the LLM QA chatbot. The system allows the instruction fine-tuning of following LLMs: Zepyhr, Mistral, Llama-3, Phi, Flan-T5, and user provided model for organization-specific information retrieval (IR), which can be further enhanced by Retrieval Augmented Generation (RAG) techniques. We have added an automatic web crawling based RAG data scrapper. Also, our system contains a human evaluation feature and RAG metrics for assessing model quality.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102029"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127772","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 : 2025-02-01DOI: 10.1016/j.softx.2024.102030
Giommaria Pilo, Nour Hezbri, André Pereira e Ferreira, Victor Quétu, Enzo Tartaglione
Large-scale models are the backbone of Computer Vision and Natural Language Processing, and their generalizability allows for transfer learning and deployment in different scenarios. However, their large size means that reducing their computational and memory demands remains a challenge. Recent research proposes to achieve “layer collapse”, a condition where multiple layers can be combined due to the collapse of non-linearities to linear operators. While this is an important discovery, most studies remain theoretical, often replacing non-linearities with simple identity functions and not providing a real implementation of the more compact architecture. Our contribution is LayerFold, a library that studies and implements the merging of collapsed layers. We address typical cases, from fully connected to convolutional layers, discussing constraints and prospective challenges. Our tests on edge devices reveal that merely reducing network depth does not always result in faster computation, even when GPU-equipped. This work raises important warnings and opens the door to further advances in efficient model deployment.
{"title":"LayerFold: A Python library to reduce the depth of neural networks","authors":"Giommaria Pilo, Nour Hezbri, André Pereira e Ferreira, Victor Quétu, Enzo Tartaglione","doi":"10.1016/j.softx.2024.102030","DOIUrl":"10.1016/j.softx.2024.102030","url":null,"abstract":"<div><div>Large-scale models are the backbone of Computer Vision and Natural Language Processing, and their generalizability allows for transfer learning and deployment in different scenarios. However, their large size means that reducing their computational and memory demands remains a challenge. Recent research proposes to achieve “layer collapse”, a condition where multiple layers can be combined due to the collapse of non-linearities to linear operators. While this is an important discovery, most studies remain theoretical, often replacing non-linearities with simple identity functions and not providing a real implementation of the more compact architecture. Our contribution is <span>LayerFold</span>, a library that studies and implements the merging of collapsed layers. We address typical cases, from fully connected to convolutional layers, discussing constraints and prospective challenges. Our tests on edge devices reveal that merely reducing network depth does not always result in faster computation, even when GPU-equipped. This work raises important warnings and opens the door to further advances in efficient model deployment.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102030"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127776","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 : 2025-02-01DOI: 10.1016/j.softx.2024.102004
Ke Chen, Lingling Qiu, Xianju Xie, Yuxing Bai
Smile analysis is essential for diagnosing and planning treatments in esthetic rehabilitation dentistry. Compared with static images, smile videos provide valuable data but are more complex to analyze. Traditionally, this process has relied on multiple software programs and involved tedious manual configuration and operation, which are labor-intensive and inconsistent. This study introduces Dynasmile, a universal software solution that integrates advanced artificial intelligence (AI) algorithms to streamline the analysis process. Dynasmile offers a comprehensive approach to video-based smile analysis, significantly improving both efficiency and accuracy, representing advancements in dental smile research.
{"title":"Dynasmile: Video-based smile analysis software in orthodontics","authors":"Ke Chen, Lingling Qiu, Xianju Xie, Yuxing Bai","doi":"10.1016/j.softx.2024.102004","DOIUrl":"10.1016/j.softx.2024.102004","url":null,"abstract":"<div><div>Smile analysis is essential for diagnosing and planning treatments in esthetic rehabilitation dentistry. Compared with static images, smile videos provide valuable data but are more complex to analyze. Traditionally, this process has relied on multiple software programs and involved tedious manual configuration and operation, which are labor-intensive and inconsistent. This study introduces Dynasmile, a universal software solution that integrates advanced artificial intelligence (AI) algorithms to streamline the analysis process. Dynasmile offers a comprehensive approach to video-based smile analysis, significantly improving both efficiency and accuracy, representing advancements in dental smile research.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102004"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092972","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 : 2025-02-01DOI: 10.1016/j.softx.2025.102054
Abraham George, Alison Bartsch, Amir Barati Farimani
Across the robotics field, quality demonstrations are an integral part of many control pipelines. However, collecting high-quality demonstration trajectories remains time-consuming and difficult, often resulting in the number of demonstrations being the performance bottleneck. To address this issue, we present a method of Virtual Reality (VR) Teleoperation that uses an Oculus VR headset to teleoperate a Franka Emika Panda robot. Although other VR teleoperation methods exist, our code is open source, designed for readily available consumer hardware, easy to modify, agnostic to experimental setup, and simple to use.
{"title":"OpenVR: Teleoperation for manipulation","authors":"Abraham George, Alison Bartsch, Amir Barati Farimani","doi":"10.1016/j.softx.2025.102054","DOIUrl":"10.1016/j.softx.2025.102054","url":null,"abstract":"<div><div>Across the robotics field, quality demonstrations are an integral part of many control pipelines. However, collecting high-quality demonstration trajectories remains time-consuming and difficult, often resulting in the number of demonstrations being the performance bottleneck. To address this issue, we present a method of Virtual Reality (VR) Teleoperation that uses an Oculus VR headset to teleoperate a Franka Emika Panda robot. Although other VR teleoperation methods exist, our code is open source, designed for readily available consumer hardware, easy to modify, agnostic to experimental setup, and simple to use.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102054"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092185","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 : 2025-02-01DOI: 10.1016/j.softx.2024.101998
Saman Barakat , Alberto Martin-Lopez , Carlos Müller , Sergio Segura , Antonio Ruiz-Cortés
Web APIs may include inter-parameter dependencies that limit how input parameters can be combined to call services correctly. These dependencies are extremely common, appearing in 4 out of every 5 APIs. This paper presents the IDL tool suite, a set of software tools for managing inter-parameter dependencies in web APIs. The suite includes a specification language (IDL), an OpenAPI Specification extension (IDL4OAS), an analysis engine (IDLReasoner), a web API, a playground, an AI chatbot, and a website. We also highlight several contributions by different groups of authors where the IDL tool suite has proven useful in the domains of automated testing, code generation, and API gateways. To date, the IDL tool suite has contributed to the detection of more than 200 bugs in industrial APIs, including GitHub, Spotify, and YouTube, among others. Also, IDL has been used to boost automated code generation, generating up to 10 times more code than state-of-the-art generators for web APIs.
{"title":"The IDL tool suite: Specifying and analyzing inter-parameter dependencies in web APIs","authors":"Saman Barakat , Alberto Martin-Lopez , Carlos Müller , Sergio Segura , Antonio Ruiz-Cortés","doi":"10.1016/j.softx.2024.101998","DOIUrl":"10.1016/j.softx.2024.101998","url":null,"abstract":"<div><div>Web APIs may include inter-parameter dependencies that limit how input parameters can be combined to call services correctly. These dependencies are extremely common, appearing in 4 out of every 5 APIs. This paper presents the IDL tool suite, a set of software tools for managing inter-parameter dependencies in web APIs. The suite includes a specification language (IDL), an OpenAPI Specification extension (IDL4OAS), an analysis engine (IDLReasoner), a web API, a playground, an AI chatbot, and a website. We also highlight several contributions by different groups of authors where the IDL tool suite has proven useful in the domains of automated testing, code generation, and API gateways. To date, the IDL tool suite has contributed to the detection of more than 200 bugs in industrial APIs, including GitHub, Spotify, and YouTube, among others. Also, IDL has been used to boost automated code generation, generating up to 10 times more code than state-of-the-art generators for web APIs.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 101998"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092973","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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