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1. ProCon: An automated process-centric quality constraints checking framework

   https://doi.org/10.1016/j.jss.2023.111727  

   Mayr-Dorn, Christoph; Vierhauser, Michael; Bichler, Stefan; Keplinger, Felix; Cleland-Huang, Jane; Egyed, Alexander; Mehofer, Thomas (August 2023, Journal of Systems and Software)

   When dealing with safety–critical systems, various regulations, standards, and guidelines stipulate stringent requirements for certification and traceability of artifacts, but typically lack details with regards to the corresponding software engineering process. Given the industrial practice of only using semi-formal notations for describing engineering processes – with the lack of proper tool mapping – engineers and developers need to invest a significant amount of time and effort to ensure that all steps mandated by quality assurance are followed. The sheer size and complexity of systems and regulations make manual, timely feedback from Quality Assurance (QA) engineers infeasible. In order to address these issues, in this paper, we propose a novel framework for tracking, and “passively” executing processes in the background, automatically checking QA constraints depending on process progress, and informing the developer of unfulfilled QA constraints. We evaluate our approach by applying it to three case studies: a safety–critical open-source community system, a safety–critical system in the air-traffic control domain, and a non-safety–critical, web-based system. Results from our analysis confirm that trace links are often corrected or completed after the work step has been considered finished, and the engineer has already moved on to another step. Thus, support for timely and automated constraint checking has significant potential to reduce rework as the engineer receives continuous feedback already during their work step. 

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2. GRuM — A flexible model-driven runtime monitoring framework and its application to automated aerial and ground vehicles

   https://doi.org/10.1016/j.jss.2023.111733  

   Vierhauser, Michael; Garmendia, Antonio; Stadler, Marco; Wimmer, Manuel; Cleland-Huang, Jane (September 2023, Journal of Systems and Software)

   When dealing with safety-critical systems, various regulations, standards, and guidelines stipulate stringent requirements for certification and traceability of artifacts, but typically lack \rev{details} with regards to the corresponding software engineering process. Given the industrial practice of only using semi-formal notations for describing engineering processes with the lack of proper tool mapping engineers and developers need to invest a significant amount of time and effort to ensure that all steps mandated by quality assurance are followed. The sheer size and complexity of systems and regulations make manual, timely feedback from Quality Assurance (QA) engineers infeasible. In order to address these issues, in this paper, we propose a novel framework for tracking, and ``passively'' executing processes in the background, automatically checking QA constraints depending on process progress, and informing the developer of unfulfilled QA constraints. We evaluate our approach by applying it to three case studies: a safety-critical open-source community system, a safety-critical system in the air-traffic control domain, and a non-safety-critical, web-based system. Results from our analysis confirm that trace links are often corrected or completed after the work step has been considered finished, and the engineer has already moved on to another step. Thus, support for timely and automated constraint checking has significant potential to reduce rework as the engineer receives continuous feedback already during their work step. 

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3. A functional modeling approach for quality assurance in metal additive manufacturing

   https://doi.org/10.1108/RPJ-12-2018-0312  

   Seo, Gijeong; Ahsan, Md. RU; Lee, Yousub; Shin, Jong-Ho; Park, Hyungjun; Kim, Duck Bong (January 2021, Rapid Prototyping Journal)

   null (Ed.)

   Purpose Due to the complexity of and variations in additive manufacturing (AM) processes, there is a level of uncertainty that creates critical issues in quality assurance (QA), which must be addressed by time-consuming and cost-intensive tasks. This deteriorates the process repeatability, reliability and part reproducibility. So far, many AM efforts have been performed in an isolated and scattered way over several decades. In this paper, a systematically integrated holistic view is proposed to achieve QA for AM. Design/methodology/approach A systematically integrated view is presented to ensure the predefined part properties before/during/after the AM process. It consists of four stages, namely, QA plan, prospective validation, concurrent validation and retrospective validation. As a foundation for QA planning, a functional workflow and the required information flows are proposed by using functional design models: Icam DEFinition for Function Modeling. Findings The functional design model of the QA plan provides the systematically integrated view that can be the basis for inspection of AM processes for the repeatability and qualification of AM parts for reproducibility. Research limitations/implications A powder bed fusion process was used to validate the feasibility of this QA plan. Feasibility was demonstrated under many assumptions; real validation is not included in this study. Social implications This study provides an innovative and transformative methodology that can lead to greater productivity and improved quality of AM parts across industries. Furthermore, the QA guidelines and functional design models provide the foundation for the development of a QA architecture and management system. Originality/value This systematically integrated view and the corresponding QA plan can pose fundamental questions to the AM community and initiate new research efforts in the in-situ digital inspection of AM processes and parts. 

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4. LogiDebrief: A Signal-Temporal Logic Based Automated Debriefing Approach with Large Language Models Integration

   Chen, Zirong; An, Ziyan; Reynolds, Jennifer; Mullen, Kristin; Martini, Stephen; Ma, Meiyi (August 2025, Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence)

   Emergency response services are critical to public safety, with 9-1-1 call-takers playing a key role in ensuring timely and effective emergency operations. To ensure call-taking performance consistency, quality assurance is implemented to evaluate and refine call-takers' skillsets. However, traditional human-led evaluations struggle with high call volumes, leading to low coverage and delayed assessments. We introduce LogiDebrief, an AI-driven framework that automates traditional 9-1-1 call debriefing by integrating Signal-Temporal Logic (STL) with Large Language Models (LLMs) for fully-covered rigorous performance evaluation. LogiDebrief formalizes call-taking requirements as logical specifications, enabling systematic assessment of 9-1-1 calls against procedural guidelines. It employs a three-step verification process: (1) contextual understanding to identify responder types, incident classifications, and critical conditions; (2) STL-based runtime checking with LLM integration to ensure compliance; and (3) automated aggregation of results into quality assurance reports. Beyond its technical contributions, LogiDebrief has demonstrated real-world impact. Successfully deployed at Metro Nashville Department of Emergency Communications, it has assisted in debriefing 1,701 real-world calls, saving 311.85 hours of active engagement. Empirical evaluation with real-world data confirms its accuracy, while a case study and extensive user study highlight its effectiveness in enhancing call-taking performance. 

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5. Improving Data Quality of Automated Pavement Condition Data Collection: Summary of State of the Practices of Transportation Agencies and Views of Professionals

   Luo, X; Gong, H; Tao, J; Wang, F; Minifie, J (January 2022, Journal of transportation engineering)

   Automated or semi-automated pavement condition data collection is replacing manual data collection in many state and local highway agencies due to its advantages of reducing labor, time, and cost. However, the practical experience of highway agencies indicates that there are still data quality issues with the pavement condition data collected using existing image and sensor-based data collection technologies. This study aims to investigate the implementation experiences and issues of automated or semi-automated pavement condition surveys. An online questionnaire survey was conducted, along with scheduled virtual/phone interviews to gather information from government, industry, and academia about the state of the practice and state of the art. Open questions about the data quality and quality control & quality assurance (QC/QA) were used to receive first-hand inputs from highway agencies and pavement experts. The study has compiled the following observations: (1) Highway agencies urgently need a uniform data collection protocol for automated data collection; (2) the current QA requires too much human intervention; (3) cost ($100–$200 per mile) is a significant burden for state and local agencies; (4) the main issues regarding data quality are data inconsistencies and discrepancies; (5) agencies expect a greater accuracy once the image processing algorithms are improved using artificial intelligence technologies; and (6) existing automated data collection methods are not available for project-level data collection. 

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