RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers

Islam, Md Nafee Al; Ma, Yihong; Alarcón, Pedro A. de; Chawla, Nitesh V.; Cleland‐Huang, Jane

doi:10.1109/re54965.2022.00020

Cited by 11 publications

(1 citation statement)

References 32 publications

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“…We envision ones based on computer vision, data analytics, and configuration checks. Initially, we focus on the heuristics implemented in this paper, which monitor controller instability thresholds; however, more sophisticated approaches based on deep-learning are also feasible and will be included in future work [24].…”

Section: Adaptation Actionsmentioning

confidence: 99%

Self-Adaptive Mechanisms for Misconfigurations in Small Uncrewed Aerial Systems

Purandare

Sinha

Islam

et al. 2023

2023 IEEE/ACM 18th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

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Small uncrewed aerial systems, sUAS, provide an invaluable resource for performing a variety of surveillance, search, and delivery tasks in remote or hostile terrains which may not be accessible by other means. Due to the critical role sUAS play in these situations, it is vital that they are well configured in order to ensure a safe and stable flight. However, it is not uncommon for mistakes to occur in configuration and calibration, leading to failures or incomplete missions. To address this problem, we propose a set of self-adaptive mechanisms and implement them into a self-adaptive framework, CICADA, for Controller Instability-preventing Configuration Aware Drone Adaptation. CICADA dynamically detects unstable drone behavior during flight and adapts to mitigate this threat. We have built a prototype of CICADA using a popular open source sUAS simulator and experimented with a large number of different configurations. Experimental results show that CICADA's adaptations reduce controller instability and enable the sUAS to recover from a significant number of poor configurations. In cases where we cannot complete the intended mission, invoking alternative adaptations may still help by allowing the vehicle to loiter or land safely in place, avoiding potentially catastrophic crashes.

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Section: Adaptation Actionsmentioning

confidence: 99%

Self-Adaptive Mechanisms for Misconfigurations in Small Uncrewed Aerial Systems

Purandare

Sinha

Islam

et al. 2023

2023 IEEE/ACM 18th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

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Requirements and software engineering for automotive perception systems: an interview study

Habibullah,

Heyn,

Gay

et al. 2024

Requirements Eng

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Driving automation systems, including autonomous driving and advanced driver assistance, are an important safety-critical domain. Such systems often incorporate perception systems that use machine learning to analyze the vehicle environment. We explore new or differing topics and challenges experienced by practitioners in this domain, which relate to requirements engineering (RE), quality, and systems and software engineering. We have conducted a semi-structured interview study with 19 participants across five companies and performed thematic analysis of the transcriptions. Practitioners have difficulty specifying upfront requirements and often rely on scenarios and operational design domains (ODDs) as RE artifacts. RE challenges relate to ODD detection and ODD exit detection, realistic scenarios, edge case specification, breaking down requirements, traceability, creating specifications for data and annotations, and quantifying quality requirements. Practitioners consider performance, reliability, robustness, user comfort, and—most importantly—safety as important quality attributes. Quality is assessed using statistical analysis of key metrics, and quality assurance is complicated by the addition of ML, simulation realism, and evolving standards. Systems are developed using a mix of methods, but these methods may not be sufficient for the needs of ML. Data quality methods must be a part of development methods. ML also requires a data-intensive verification and validation process, introducing data, analysis, and simulation challenges. Our findings contribute to understanding RE, safety engineering, and development methodologies for perception systems. This understanding and the collected challenges can drive future research for driving automation and other ML systems.

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How mature is requirements engineering for AI-based systems? A systematic mapping study on practices, challenges, and future research directions

Habiba,

Haug,

Bogner

et al. 2024

Requirements Eng

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Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward.

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RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers

Cited by 11 publications

References 32 publications

Self-Adaptive Mechanisms for Misconfigurations in Small Uncrewed Aerial Systems

Self-Adaptive Mechanisms for Misconfigurations in Small Uncrewed Aerial Systems

Requirements and software engineering for automotive perception systems: an interview study

How mature is requirements engineering for AI-based systems? A systematic mapping study on practices, challenges, and future research directions

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