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Accepted Papers

Title
A-COBREX : A Tool for Identifying Business Rules in COBOL Programs
Demonstrations
AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL
Demonstrations
Closing the Gap between Sensor Inputs and Driving Properties: A Scene Graph Generator for CARLA
Demonstrations
FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software
Demonstrations
GeMTest: A General Metamorphic Testing Framework
Demonstrations
GUIDE: LLM-Driven GUI Generation Decomposition for Automated Prototyping
Demonstrations
HyperCRX 2.0: A Comprehensive and Automated Tool for Empowering GitHub Insights
Demonstrations
IFSE: Taming Closed-box Functions in Symbolic Execution via Fuzz Solving
Demonstrations
LEGOS-SLEEC: Tool for Formalizing and Analyzing Normative Requirements
Demonstrations
OptCD: Optimizing Continuous Development
Demonstrations
SIT: An accurate, compliant SBOM generator with incremental construction
Demonstrations
The Software Librarian: Python Package Insights for Copilot
Demonstrations
VReqST: A Requirement Specification Tool for Virtual Reality Software Products
Demonstrations

Call for Demonstrations

The ICSE 2025 Demonstrations Track aims to make the software engineering community aware of new advances in our field through compelling demonstrations that help advance research and practice. The track is a highly interactive venue where researchers and practitioners can demonstrate their tools or technology and discuss them with attendees.

Tool-based demonstrations describe novel aspects of early prototypes or mature tools, including exciting new features of established tools. The tool demonstrations must communicate clearly the following information to the audience:

  • the envisioned users;
  • the software engineering challenge it proposes to address;
  • the methodology it implies for its users; and
  • the results of validation studies already conducted for mature tools, or the design of planned studies for early prototypes.

Highlighting scientific contributions through concrete artifacts is a critical supplement to the traditional ICSE research papers. A demonstration provides the opportunity to communicate how the scientific approach has been implemented or how a specific hypothesis has been assessed, including details such as implementation and usage issues, data models and representations, and APIs for tool and data access. Authors of regular research papers are thus also encouraged to submit an accompanying demonstration paper. In such cases, the authors must ensure that the tool details should not have already been discussed in the original paper and the tool paper provides more information on implementation and usage.

Evaluation

Each submission will be reviewed by at least three members of the Demonstrations Track program committee. The evaluation criteria include:

  • the relevance of the proposed demonstration for the ICSE audience;
  • the technical soundness of the submission;
  • the originality of its underlying ideas;
  • the quality of its presentation in the associated video;
  • the potential applications and usefulness of the tool; and
  • the degree to which it considers the relevant literature.

How to Submit

Submissions must conform to the IEEE conference submission and formatting instructions (title in 24pt font and full text in 10pt type, LaTeX users must use \documentclass[10pt,conference]{IEEEtran} without including the compsoc or compsocconf options). A demonstration submission may not exceed four pages, including text, references and figures.

Each submission must be accompanied by a short video (between three and five minutes long) illustrating the demonstration.

  • The submission must contain a link to the publicly available tool and its usage instructions. Optionally, if the tool is open-source, the submission should link to the corresponding repository.
  • The video should be made available online at the time of submission. Videos should:
    1. provide an overview of the tool capabilities and/or dataset characteristics;
    2. walk through of (some of) the tool capabilities and/or data analysis process;
    3. where appropriate, provide clarifying voice-over and/or annotation highlights; and
    4. be engaging and exciting for the viewer!
  • A submission must not have been previously published in a demonstration form. The paper submission must be in PDF.

  • The tool demonstrations track will be using the single-anonymous reviewing model (the authors do not know who the reviewers are), so please include the authors’ identities in the submission materials.
  • Upon acceptance, authors have the possibility to separately submit their supplementary material to the ICSE 2025 Artifact Evaluation track, for recognition of artifacts that are reusable, available, replicated or reproduced.

Papers must be submitted electronically through the Demonstration Track submission site https://icse25demos.hotcrp.com. At the end of the abstract, make sure to append the URL at which your demo video can be found. Please note that, for consistency, we require that all videos be uploaded to YouTube and made accessible during the time of reviewing. Authors of successful submissions will have the opportunity to revise both the paper and the video (and its hosting location) by the camera-ready deadline.

For examples of previously successful short videos, please see the examples from ICSE 2018: https://www.youtube.com/playlist?list=PL6g5MCGbJtUF1iW4RSPvUtbKkemrVYrkP

Authors are encouraged to distribute their demonstration in an easy-to-use form, such as an active website that provides fast response time and will remain online indefinitely, a virtual machine image, a software container (e.g., Docker), or a system configuration (e.g., Puppet, Ansible, Salt, CFEngine).

By submitting your article to an IEEE Publication, you are hereby acknowledging that you and your co-authors are subject to all IEEE Publications Policies.

Please ensure that you and your co-authors obtain an ORCID ID, so you can complete the publishing process for your accepted paper.

Important Dates (AOE Time)

Submission Deadline: 10-Oct-2024

Camera Ready: 5-Feb-2025

Dates
Wed 30 Apr 2025
Thu 1 May 2025
Fri 2 May 2025
Tracks
ICSE Demonstrations
ICSE Journal-first Papers
ICSE New Ideas and Emerging Results (NIER)
ICSE Research Track
ICSE SE In Practice (SEIP)
ICSE SE in Society (SEIS)

This program is tentative and subject to change.

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Wed 30 Apr

Displayed time zone: Eastern Time (US & Canada) change

11:00 - 12:30
11:45
15m
Talk
GUIDE: LLM-Driven GUI Generation Decomposition for Automated Prototyping
Demonstrations
Kristian Kolthoff Institute for Software and Systems Engineering, Clausthal University of Technology, Felix Kretzer human-centered systems Lab (h-lab), Karlsruhe Institute of Technology (KIT) , Christian Bartelt , Alexander Maedche Human-Centered Systems Lab, Karlsruhe Institute of Technology, Simone Paolo Ponzetto Data and Web Science Group, University of Mannheim
16:00 - 17:30
16:15
15m
Talk
A-COBREX : A Tool for Identifying Business Rules in COBOL Programs
Demonstrations
Samveg Shah Indian Institute of Technology, Tirupati, Shivali Agarwal IBM, Saravanan Krishnan IBM India Research Lab, Vini Kanvar IBM Research, Sridhar Chimalakonda Indian Institute of Technology, Tirupati
16:00 - 17:30
RequirementsResearch Track / Demonstrations / New Ideas and Emerging Results (NIER) at 211
Chair(s): Jane Cleland-Huang University of Notre Dame
17:00
15m
Talk
VReqST: A Requirement Specification Tool for Virtual Reality Software Products
Demonstrations
Sai Anirudh Karre Software Engineering Research Center. IIIT Hyderabad, Amogha A Halhalli Software Engineering Research Center. IIIT Hyderabad, Raghu Reddy IIIT Hyderabad

Thu 1 May

Displayed time zone: Eastern Time (US & Canada) change

10:30 - 11:00
10:30
30m
Poster
HyperCRX 2.0: A Comprehensive and Automated Tool for Empowering GitHub Insights
Demonstrations
Yantong Wang East China Normal University, Shengyu Zhao Tongji University, will wang , Fenglin Bi East China Normal University
11:00 - 12:30
11:00
15m
Talk
SIT: An accurate, compliant SBOM generator with incremental construction
Demonstrations
Changguo Jia Peking University, NIANYU LI ZGC Lab, China, Kai Yang School of Computer, Electronics and Information, Guangxi University, Minghui Zhou Peking University
11:00 - 12:30
AI for Design and ArchitectureDemonstrations / SE In Practice (SEIP) / Research Track at 211
11:30
15m
Talk
The Software Librarian: Python Package Insights for Copilot
Demonstrations
Jasmine Latendresse Concordia University, Nawres Day ISSAT Sousse, SayedHassan Khatoonabadi Concordia University, Emad Shihab Concordia University
14:00 - 15:30
14:15
15m
Talk
AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL
Demonstrations
Tyler Stennett Georgia Institute of Technology, Myeongsoo Kim Georgia Institute of Technology, Saurabh Sinha IBM Research, Alessandro Orso Georgia Institute of Technology

Fri 2 May

Displayed time zone: Eastern Time (US & Canada) change

11:00 - 12:30
Human and Social using AI 2Research Track / SE In Practice (SEIP) / Demonstrations at 207
11:15
15m
Talk
FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software
Demonstrations
Normen Yu Penn State, Luciana Carreon University of Texas at El Paso, Gang (Gary) Tan Pennsylvania State University, Saeid Tizpaz-Niari University of Illinois Chicago
13:00 - 13:30
13:00
30m
Poster
HyperCRX 2.0: A Comprehensive and Automated Tool for Empowering GitHub Insights
Demonstrations
Yantong Wang East China Normal University, Shengyu Zhao Tongji University, will wang , Fenglin Bi East China Normal University
14:00 - 15:30
14:00
15m
Talk
Closing the Gap between Sensor Inputs and Driving Properties: A Scene Graph Generator for CARLA
Demonstrations
Trey Woodlief University of Virginia, Felipe Toledo , Sebastian Elbaum University of Virginia, Matthew B Dwyer University of Virginia
14:15
15m
Talk
LEGOS-SLEEC: Tool for Formalizing and Analyzing Normative Requirements
Demonstrations
Kevin Kolyakov University of Toronto, Lina Marsso École Polytechnique de Montréal, Nick Feng University of Toronto, Junwei Quan University of Toronto, Marsha Chechik University of Toronto
14:00 - 15:30
14:15
15m
Talk
IFSE: Taming Closed-box Functions in Symbolic Execution via Fuzz Solving
Demonstrations
Qichang Wang East China Normal University, Chuyang Chen The Ohio State University, Ruiyang Xu East China Normal University, Haiying Sun East China Normal University, Chengcheng Wan East China Normal University, Ting Su East China Normal University, Yueling Zhang East China Normal University, Geguang Pu East China Normal University, China
16:00 - 17:30
16:15
15m
Talk
GeMTest: A General Metamorphic Testing Framework
Demonstrations
Simon Speth Technical University of Munich, Alexander Pretschner TU Munich
16:00 - 17:30
16:00
15m
Talk
OptCD: Optimizing Continuous Development
Demonstrations
Talank Baral George Mason University, Emirhan Oğul Middle East Technical University, Shanto Rahman The University of Texas at Austin, August Shi The University of Texas at Austin, Wing Lam George Mason University

The following papers have been accepted in the ICSE 2025 Demonstrations Track. The papers are will be published by the IEEE and appear in the IEEE and ACM digital libraries, subject to an author submitting their camera-ready and copyright forms, and registering to attend the conference. (Authors are required to present the papers in demonstration form at the conference, otherwise they will be withdrawn).

Normen Yu, Luciana Carreon, Gary Tan, Saeid Tizpaz-Niari, "FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software"

Abstract: Data-driven software solutions have significantly been used in critical domains with significant socio-economic, legal, and ethical implications. The rapid adoptions of data-driven solutions, however, pose major threats to the trustworthiness of automated decision-support software. A diminished understanding of the solution by the developer and historical/current biases in the data sets are primary challenges. To aid data-driven software developers and end-users, we present FairLay-ML, a debugging tool to test and explain the fairness implications of data-driven solutions. FairLay-ML visualizes the logic of datasets, trained models, and decisions for a given data point. In addition, it trains various models with varying fairness-accuracy trade-offs. Crucially, FairLay-ML incorporates counterfactual fairness testing that finds bugs beyond the development datasets. We conducted two studies through FairLay-ML that allowed us to measure false positives/negatives in prevalent counterfactual testing and understand the human perception of counterfactual test cases in a class survey. FairLay-ML and its benchmarks are publicly available at https://github.com/Pennswood/FairLay-ML. The live version of the tool is available at https://fairlayml-v2.streamlit.app/. We provide a video demo of the tool at https://youtu.be/wNI9UWkywVU?t=133.

 Tags: "AI for SE", "Human/Social"  
 
Samveg Shah, Shivali Agarwal, Saravanan Krishnan, Vini Kanvar, Sridhar Chimalakonda, "A-COBREX : A Tool for Identifying Business Rules in COBOL Programs"

Abstract: There are many organizations, especially in domains such as banking, insurance, airline that are looking for tools to identify and extract business rules from legacy mainframe code. Existing works have considered execution paths for a single business variable as the granularity of business rules which limits the identification of complex rules. In our work, we address this limitation and provide a tool called A-COBREX, which implements a novel technique to identify business rules involving multiple business variables from the source code. We have evaluated the same on 27 programs with ground truth annotations. It has a recall of 74.12% and precision of 62.21% for fuzzy match between ground truth and extracted rules. The screencast is available at https://youtu.be/adriX4q41PA, and the tool at https://github.com/SaravananKrishnan/BRE.

 Tags: "Business", "Prog Comprehension/Reeng/Maint", "MSR"  
 
Tyler Stennett, Myeongsoo Kim, Saurabh Sinha, Alessandro Orso, "AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL"

Abstract: As REST APIs have become widespread in modern web services, comprehensive testing of these APIs has become increasingly crucial. Due to the vast search space consisting of operations, parameters, and parameter values along with their complex dependencies and constraints, current testing tools suffer from low code coverage, leading to suboptimal fault detection. To address this limitation, we present a novel tool, AutoRestTest, which integrates the Semantic Operation Dependency Graph (SODG) with Multi-Agent Reinforcement Learning (MARL) and large language models (LLMs) for effective REST API testing. AutoRestTest determines operation-dependent parameters using the SODG and employs five specialized agents (operation, parameter, value, dependency, and header) to identify dependencies of operations and generate operation sequences, parameter combinations, and values. AutoRestTest provides a command-line interface and continuous telemetry on successful operation count, unique server errors detected, and time elapsed. Upon completion, AutoRestTest generates a detailed report highlighting errors detected and operations exercised. In this paper, we introduce our tool and present preliminary results.

 Tags: "Testing and Quality", "AI for SE"  
 
Jasmine Latendresse, Nawres Day, SayedHassan Khatoonabadi, Emad Shihab, "The Software Librarian: Python Package Insights for Copilot"

Abstract: Software packages form the backbone of software systems, significantly influencing their functionality, efficiency, and long-term maintainability. As developers increasingly turn to Large Language Models (LLMs) to streamline software development tasks, the ability of these models to accurately recommend suitable packages becomes critical. However, LLMs lack the ability to provide real-time information about package details such as license, dependencies, or even their existence. This can lead to the integration of outdated, incompatible, or legally restrictive packages, which could compromise the software's quality and legal standing. In this paper, we introduce the Software Librarian, a tool that provides real-time information about Python packages recommended as part of the generated code by GitHub Copilot, including license details, deprecation status, and package health. Our tool ensures that the recommended packages are not only valid but are also suitable for integration. To support future research, we have made the Software Librarian available on the Visual Studio Marketplace (https://marketplace.visualstudio.com/items?itemName=jaslatendresse.software-librarian) and released the code online (https://github.com/jaslatendresse/software-librarian-prod). A demonstration can be viewed at https://youtu.be/hnPr0rvL8lk.

 Tags: "Design/Architecture", "AI for SE"  
 
Changguo Jia, Nianyu Li, Kai Yang, Minghui Zhou, "SIT: An accurate, compliant SBOM generator with incremental construction"

Abstract: SBOM (Software Bill of Materials) is a comprehensive list of components, relationships and metadata associated with software, essential for ensuring software component transparency in the software supply chain. The complexity of SBOM and the massive workload of writing SBOMs call for the assistance of automation. However, existing automated tools excessively rely on parsing dependency manifest and source code without verifying the accuracy of the information. Worse, existing SBOM generators sometimes fail to yield a specification-compliant SBOM. Additionally, existing SBOM generators can not compose a complete SBOM with information that developers know best and entries hidden in the dependencies’ metadata in one go. To address the inaccuracy, non-compliance and incompleteness issues of SBOM generation, we propose SIT, an accurate, compliant SBOM generator with incremental construction. Through incremental construction, SIT aggregates manually maintained SBOMs and dependency SBOMs and exports SBOMs for editing, enhancing the correctness and completeness of SBOMs. This capability is built on SBOM IR, a flexible intermediate format that consolidates essential information and acts as a bridge for software representations. By integrating SBOM IR with official SBOM JSON schemas, SIT ensures all generated SBOMs are compliant to SBOM specifications. Additionally, SIT enhances SBOM accuracy with cross-validation, resolving inconsistencies with the real environment. SIT is publicly available at https://github.com/osslab-pku/SIT, and a demonstration video can be found at https://youtu.be/LbzslijVPLc.

 Tags: "Analysis", "Process", "Open Source"  
 
Yantong Wang, Shengyu Zhao, Wei Wang, Fenglin Bi, "HyperCRX 2.0: A Comprehensive and Automated Tool for Empowering GitHub Insights"

Abstract: HyperCRX is a browser extension designed for the GitHub platform, aimed at enhancing the open-source experience by providing in-depth insights into projects and developers. Unlike traditional tools, HyperCRX seamlessly integrates with GitHub, offering real-time analysis of project ecosystems, developer contributions, and community activities. This helps users better understand and manage open-source projects. In this work, we have extended HyperCRX to deepen the level of insights and integrated LLMs capabilities to enable intelligent open-source operational support and automated project insight analysis. The plugin caters to a wide range of user groups, including developers, project maintainers, and open-source operators, helping them improve efficiency and make data-driven decisions. The source code for HyperCRX is open-sourced on GitHub at https://github.com/hypertrons/hypertrons-crx. A demonstration of the features is available at https://youtu.be/y4mCCfFVux0.

 Tags: "Analysis", "AI for SE"  
 
Qichang Wang, Chuyang Chen, Ruiyang Xu, Haiying Sun, Chengcheng Wan, Ting Su, Yueling Zhang, Geguang Pu, "IFSE: Taming Closed-box Functions in Symbolic Execution via Fuzz Solving"

Abstract: Modern symbolic execution techniques face the challenge of handling \textit{closed-box (CB)} functions (\eg, system calls, library functions) whose source code is unavailable. One interesting solution in the literature is deferred concretization with fuzz solving. However, no open-source implementation of such techniques exists, and thus it is difficult to evaluate and investigate the effectiveness. In this paper, we present IFSE (\textbf{I}ntegrating \textbf{F}uzz Solving into \textbf{S}ymbolic \textbf{E}xecution), an open-sourced tool implementing the relevant techniques on top of KLEE to handle the CB functions in symbolic execution. We evaluated IFSE on GNU Coreutils. The results show that IFSE achieves the line and branch code coverage improvement by 28.3\% and 12.2\% respectively compared to vanilla KLEE. The satisfaction rate of fuzz solver achieves 80.2\%, demonstrating its ability to reason CB function related constraints. IFSE is publicly available at https://github.com/ecnusse/ifse and a demonstration video is at https://youtu.be/xMv6_MOlE-I.

 Tags: "Testing and Quality", "Analysis"  
 
Sai Anirudh Karre, Amogha A Halhalli, Y. Raghu Reddy, "VReqST: A Requirement Specification Tool for Virtual Reality Software Products"

Abstract: Developing Virtual Reality (VR) software products with discrete and incremental requirements is a challenging task for VR practitioners. A domain expert's assistance plays a key role in VR product completeness, as most VR requirements are abstract or under-specified during the early stages. Slight changes to the requirements can significantly impact the overall flow of the VR software development process. In this paper, we introduce VReqST, a tool for VR requirement analysts to specify requirements for building effective VR software products. It is developed based on a Role-based model template for specifying virtual environments, custom behaviors, VR-specific algorithms, user-flows, action responses, timeline of events, etc. The tool has been developed after several interactions with VR practitioners from industry & academia. We share our insights on the effectiveness of this tool in practice.

 Tags: "Requirements"  
 
Simon Speth, Alexander Pretschner, "GeMTest: A General Metamorphic Testing Framework"

Abstract: Metamorphic testing (MT) is an established testing methodology suitable for testing various types of systems. While performing MT, software testers face the challenge of identifying and implementing metamorphic relations (MRs) for their software systems. This paper introduces GeMTest, a general-purpose metamorphic testing framework that is domain-independent, enabling software testers to implement MRs in Python and execute them with pytest. The implementation of MRs is done by annotating Python functions, which implement the follow-up generation function, the metamorphic oracle, and the system under test with decorators provided by GeMTest. This allows GeMTest to automatically create and execute a pytest test suite, containing multiple metamorphic test cases derived from the user-defined MRs. We evaluate GeMTest by implementing 218 MRs from 16 program domains, ranging from SAT solvers to deep learning image classifiers. To enable the adoption and encourage further extension, an open-source implementation of GeMTest is available. Our demo video is available at https://youtu.be/c8kShGg5rCY.

 Tags: "Testing and Quality"  
 
Talank Baral, Emirhan Oğul, Shanto Rahman, August Shi, Wing Lam, "OptCD: Optimizing Continuous Development"

Abstract: We present OptCD, a tool for optimizing continuous development (CD). Developers may mistakenly configure the CD workflow to generate unused files, such as code coverage reports, that are never uploaded anywhere for the developers to use before the files are destroyed. Generating unused files is a waste of time and effort that slows down the CD process. OptCD operates on Maven-based, Java projects that use GitHub Actions as their CD service, automatically identifying unnecessary files and directories that are generated or modified but never used afterwards. OptCD then pinpoints the Maven plugin (the process that performs build tasks for Maven) by analyzing the timestamps of generated files and matching against which plugin was running at that time. Finally, OptCD interfaces with Gemini, a large lan- guage model, to generate a fix to the Maven command contained within the CD configuration file to have the corresponding Maven plugin stop generating the unnecessary directories. Compared to our prior work, we (1) streamline and substantially simplify the process to use OptCD (e.g., reducing five steps that required nontrivial manual effort to one automated step) and (2) conduct an extensive evaluation of OptCD on 89 projects (compared to just 22 from before); OptCD is able to find 6800 unnecessary directories from 62 projects. A video demo of OptCD can be found at https://www.youtube.com/watch?v=G3_W9nQmJUI. Our tool is publicly available at https://github.com/software-research/optCD-demo.

 Tags: "Process", "AI for SE"  
 
Kevin Kolyakov, Lina Marsso, Nick Feng, Junwei Quan, Marsha Chechik, "LEGOS-SLEEC: Tool for Formalizing and Analyzing Normative Requirements"

Abstract: Systems interacting with humans, such as assistive robots or chatbots, are increasingly integrated into our society. To prevent these systems from causing social, legal, ethical, empathetic, or cultural (SLEEC) harms, normative requirements specify the permissible range of their behaviors. These requirements encompass both functional and non-functional aspects and are defined with respect to time. Typically, these requirements are specified by stakeholders from a broad range of fields, such as lawyers, ethicists, or philosophers, who may lack technical expertise. Because such stakeholders often have different goals, responsibilities, and objectives, ensuring that these requirements are well-formed is crucial. SLEEC DSL, a domain-specific language resembling natural language, has been developed to formalize these requirements as SLEEC rules. In this paper, we present LEGOS-SLEEC, a tool designed to support interdisciplinary stakeholders in specifying normative requirements as SLEEC rules, and in analyzing and debugging their well-formedness. LEGOS-SLEEC is built using four previously published components, which have been shown to be effective and usable across nine case studies. Reflecting on this experience, we have significantly improved the user interface of LEGOS-SLEEC and its diagnostic support, and demonstrate the effectiveness of these improvements with four interdisciplinary stakeholders. Showcase video URL: https://youtu.be/LLaBLGxSi8A

 Tags: "Requirements", "Human/Social", "Real-Time"  
 
Trey Woodlief, Felipe Toledo, Sebastian Elbaum, Matthew B Dwyer, "Closing the Gap between Sensor Inputs and Driving Properties: A Scene Graph Generator for CARLA"

Abstract: The software engineering community has increasingly taken up the task of assuring safety in autonomous driving systems by applying software engineering principles to create techniques to develop, validate, and verify these systems. However, developing and analyzing these techniques requires extensive sensor data sets and execution infrastructure with the relevant features and \textit{known semantics} for the task at hand. While the community has invested substantial effort in gathering and cultivating large-scale data sets and developing simulation infrastructure with varying features, semantic understanding of this data has remained out of reach, relying on limited, manually-crafted data sets or bespoke simulation environments to ensure the desired semantics are met. To address this, we developed a plugin for the widely-used ADS simulator CARLA called CarlaSGG, that extracts relevant ground-truth spatial and semantic information from the simulator state at runtime in the form of \textit{scene graphs}, enabling online and post-hoc automatic reasoning about the semantics of the scenario and associated sensor data. The tool has been successfully deployed in multiple previous software engineering approach evaluations which we describe to demonstrate the utility of the tool. The precision of the semantic information captured in the scene graph can be adjusted by the client application to suit the needs of the implementation. We provide a detailed description of the tool's design, capabilities, and configurations, with additional documentation available accompanying the tool's online source: https://github.com/less-lab-uva/carla_scene_graphs.

 Tags: "Real-Time", "AI for SE", "Analysis"  
 
Kristian Kolthoff, Felix Kretzer, Christian Bartelt, Alexander Maedche, Simone Paolo Ponzetto, "GUIDE: LLM-Driven GUI Generation Decomposition for Automated Prototyping"

Abstract: Graphical user interface (GUI) prototyping serves as one of the most valuable techniques for enhancing the elicitation of requirements, facilitating the visualization and refinement of customer needs and closely integrating the customer into the development activities. While GUI prototyping has a positive impact on the software development process, it simultaneously demands significant effort and resources. The emergence of Large Language Models (LLMs) with their impressive code generation capabilities offers a promising approach for automating GUI prototyping. Despite their potential, there is a gap between current LLM-based prototyping solutions and traditional user-based GUI prototyping approaches which provide visual representations of the GUI prototypes and direct editing functionality. In contrast, LLMs merely produce text sequences or non-editable image outputs, which lacks both mentioned aspects and therefore impede supporting GUI prototyping. Moreover, minor changes requested by the user typically leads to an inefficient regeneration of the entire GUI prototype when using LLMs directly. In this work, we propose GUIDE, a novel LLM-driven GUI generation decomposition approach seamlessly integrated into the popular prototyping framework Figma. Our approach initially decomposes high-level GUI descriptions into fine-granular GUI requirements, which are subsequently translated into Material Design GUI prototypes, enabling higher controllability and more efficient adaption of changes. To efficiently conduct prompting-based generation of Material Design GUI prototypes, we propose a Retrieval-Augmented Generation (RAG) approach to integrate the component library. Our preliminary evaluation demonstrates the effectiveness of GUIDE in bridging the gap between LLM generation capabilities and traditional GUI prototyping workflows, offering a more effective and controlled user-based approach to LLM-driven GUI prototyping. Video presentation of GUIDE is available at: https://youtu.be/ODktyuQxSqo

 Tags: "Requirements", "AI for SE", "User experience"  
 
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