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  1. Recent work has recognized the importance of developing and deploying software systems that reflect human values and has explored different approaches for eliciting these values from stakeholders. However, prior studies have also shown that it can be challenging for stakeholders to specify a diverse set of product-related human values. In this paper we therefore explore the use of ChatGPT for generating user stories that describe candidate human values. These generated stories provide inspiration to stakeholder discussions and enrich the human-created user stories. We engineer a series of ChatGPT prompts to retrieve a list of common stakeholders and candidate features for a targeted product, and then, for each pairwise combination of role and feature, and for each individual Schwartz value, we issue an additional prompt to generate a candidate user story reflecting that value. We present the candidate user-stories to stakeholders and, as part of a creative requirements engineering session, we ask them to assess and prioritize the generated user-stories, and then use them as inspiration for discussing and specifying their own product-related human values. Through conducting a series of focus groups we compare the human-values created by stakeholders with and without the benefit of the ChatGPT examples. Results are evaluated with respect to coverage of values, clarity of expression, internal completeness, and through feedback from our participants. Results from our analysis show that the ChatGPT-generated user stories are able to provide creativity triggers that help stakeholders to specify human values for a product. 
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    Free, publicly-accessible full text available September 1, 2024
  2. Software systems are increasingly expected to address a broad range of stakeholder values representing both personal and societal values as well as values ensconced as laws and regulations. Whereas laws and regulations must be fully addressed, other human values need to be carefully analyzed and prioritized within the context of candidate architectural designs. The majority of prior work has investigated requirements engineering techniques for either regulatory compliance or for human-values, we take an integrated approach which simultaneously considers laws and regulations as well as societal and personal human values throughout the system analysis, specification, and design process. We illustrate our approach through detailed examples drawn from a multi-drone system regulated by the USA Federal Aviation Authority (FAA) and operating in a domain rich with human and societal values. We then discuss requirements engineering challenges and solutions unique to identifying analyzing, and prioritizing human, societal, and regulatory requirements, and ultimately for designing accountable software systems. 
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    Free, publicly-accessible full text available September 1, 2024
  3. IEEE Requirements Engineering Conference (Ed.)
    Large Language Models (LLMs) have the potential to revolutionize automated traceability by overcoming the challenges faced by previous methods and introducing new possibilities. However, the optimal utilization of LLMs for automated traceability remains unclear. This paper explores the process of prompt engineering to extract link predictions from an LLM. We provide detailed insights into our approach for constructing effective prompts, offering our lessons learned. Additionally, we propose multiple strategies for leveraging LLMs to generate traceability links, improving upon previous zero-shot methods on the ranking of candidate links after prompt refinement. The primary objective of this paper is to inspire and assist future researchers and engineers by highlighting the process of constructing traceability prompts to effectively harness LLMs for advancing automatic traceability. 
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    Free, publicly-accessible full text available September 1, 2024
  4. 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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    Free, publicly-accessible full text available September 1, 2024
  5. 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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    Free, publicly-accessible full text available August 1, 2024
  6. Computer Vision (CV) is used in a broad range of Cyber-Physical Systems such as surgical and factory floor robots and autonomous vehicles including small Unmanned Aerial Systems (sUAS). It enables machines to perceive the world by detecting and classifying objects of interest, reconstructing 3D scenes, estimating motion, and maneuvering around objects. CV algorithms are developed using diverse machine learning and deep learning frameworks, which are often deployed on limited resource edge devices. As sUAS rely upon an accurate and timely perception of their environment to perform critical tasks, problems related to CV can create hazardous conditions leading to crashes or mission failure. In this paper, we perform a systematic literature review (SLR) of CV-related challenges associated with CV, hardware, and software engineering. We then group the reported challenges into five categories and fourteen sub-challenges and present existing solutions. As current literature focuses primarily on CV and hardware challenges, we close by discussing implications for Software Engineering, drawing examples from a CV-enhanced multi-sUAS system. 
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  7. Missing person searches are typically initiated with a description of a person that includes their age, race, clothing, and gender, possibly supported by a photo. Unmanned Aerial Systems (sUAS) imbued with Computer Vision (CV) capabilities, can be deployed to quickly search an area to find the missing person; however, the search task is far more difficult when a crowd of people is present, and only the person described in the missing person report must be identified. It is particularly challenging to perform this task on the potentially limited resources of an sUAS. We therefore propose AirSight, as a new model that hierarchically combines multiple CV models, exploits both onboard and off-board computing capabilities, and engages humans interactively in the search. For illustrative purposes, we use AirSight to show how a person's image, extracted from an aerial video can be matched to a basic description of the person. Finally, as a work-in-progress paper, we describe ongoing efforts in building an aerial dataset of partially occluded people and physically deploying AirSight on our sUAS. 
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  8. In emergency response scenarios, autonomous small Unmanned Aerial Systems (sUAS) must be configured and deployed quickly and safely to perform mission-specific tasks. In this paper, we present \DR, a Software Product Line for rapidly configuring and deploying a multi-role, multi-sUAS mission whilst guaranteeing a set of safety properties related to the sequencing of tasks within the mission. Individual sUAS behavior is governed by an onboard state machine, combined with coordination handlers which are configured dynamically within seconds of launch and ultimately determine the sUAS' behaviors, transition decisions, and interactions with other sUAS, as well as human operators. The just-in-time manner in which missions are configured precludes robust upfront testing of all conceivable combinations of features -- both within individual sUAS and across cohorts of collaborating ones. To ensure the absence of common types of configuration failures and to promote safe deployments, we check vital properties of the dynamically generated sUAS specifications and coordination handlers before sUAS are assigned their missions. We evaluate our approach in two ways. First, we perform validation tests to show that the end-to-end configuration process results in correctly executed missions, and second, we apply fault-based mutation testing to show that our safety checks successfully detect incorrect task sequences. 
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  9. Abstract

    We present MIRI Medium-resolution Spectrograph observations of the large, multi-gapped protoplanetary disk around the T Tauri star AS 209. The observations reveal hundreds of water vapor lines from 4.9–25.5μm toward the inner ∼1 au in the disk, including the first detection of rovibrational water emission in this disk. The spectrum is dominated by hot (∼800 K) water vapor and OH gas, with only marginal detections of CO2, HCN, and a possible colder water vapor component. Using slab models with a detailed treatment of opacities and line overlap, we retrieve the column density, emitting area, and excitation temperature of water vapor and OH, and provide upper limits for the observable mass of other molecules. Compared to MIRI spectra of other T Tauri disks, the inner disk of AS 209 does not appear to be atypically depleted in CO2nor HCN. Based on Spitzer Infrared Spectrograph observations, we further find evidence for molecular emission variability over a 10 yr baseline. Water, OH, and CO2line luminosities have decreased by factors of 2–4 in the new MIRI epoch, yet there are minimal continuum emission variations. The origin of this variability is yet to be understood.

     
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  10. Many organizations seek to increase their agility in order to deliver more timely and competitive products. However, in safety-critical systems such as medical devices, autonomous vehicles, or factory floor robots, the release of new features has the potential to introduce hazards that potentially lead to run-time failures that impact software safety. As a result, many projects suffer from a phenomenon referred to as the big freeze. SAFA is designed to address this challenge. Through the use of cutting-edge deep-learning solutions, it generates trees of requirements, designs, code, tests, and other artifacts that visually depict how hazards are mitigated in the system, and it automatically warns the user when key artifacts are missing. It also uses a combination of colors, annotations, and recommendations to dynamically visualize change across software versions and augments safety cases with visual annotations to aid users in detecting and analyzing potentially adverse impacts of change upon system safety. A link to our tool demo can be found at https://www.youtube.com/watch?v=r-CwxerbSVA. 
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