An analysis of causation in aerospace accidents

Weiss, Konrad; Leveson, Nancy; Lundqvist, Kristina; Farid, Nima; Stringfellow, Margaret V.

doi:10.1109/dasc.2001.963364

Cited by 18 publications

(15 citation statements)

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“…It is imperative to address UAV mishap rates now so that their full potential is realized. When technology changes rapidly or new and radical designs are introduced, previous accident data may no longer be valid (32). This assessment of UAV mishaps using a validated hierarchical model of human error has identified key recurring factors at the organizational, supervisory, and preconditions levels which need to be addressed in order to make UAVs more viable in the near and distant future.…”

Section: Resultsmentioning

confidence: 99%

“…As noted by Weiss et al (32) in their discussion on the analysis of causation in aerospace accidents, filtering and bias occur in mishap reports due to the subjective interpretation of events by both the individuals involved in the mishap and the investigators. The accident model used by investigators also imposes patterns on the mishap and influences the data collected and the factors identified as causative (e.g., detection bias), either narrowing or expanding the consideration of certain factors.…”

Section: Discussionmentioning

confidence: 99%

“…Although Williams (34) provides a review of human factors in UAV mishaps in all three military services via the UAV systems they operate, he doesn't utilize a standardized accident model or human factors taxonomy that would allow for a hierarchical analysis of human error (1,28,29,32,33). The other studies do not provide an aggregate DoD-wide look at human factors in UAV mishaps, and with the exception of the studies by Seagle (27) and Ferguson (13) both examining Navy UAV mishaps, none utilize a similar human factors taxonomy allowing the direct comparison of findings.…”

Section: Introductionmentioning

confidence: 99%

“…Determining the prevalence of specific human factors would also allow the necessary prioritization of interventions given ever present resource limitations and identify those interventions best initiated at the joint (e.g., DoD) versus individual services level. Finally, utilization of a hierarchical model of human error to identify latent as well as active human failures would be of importance since latent failures have the tendency to contribute to more mishaps than active failures (1,32,33). Therefore, the purpose of this study is to provide a quantitative analysis of the role and patterns of active and latent human failures in UAV mishaps within the U.S. military services using a standardized human factors taxonomy (1).…”

Section: Introductionmentioning

confidence: 99%

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US Military Unmanned Aerial Vehicle Mishaps: Assessment of the Role of Human Factors Using Human Factors Analysis and Classification System (HFACS)

Tvaryanas¹,

Thompson²,

Constable³

2005

PsycEXTRA Dataset

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Washington Headquarters Service, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC 20503. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY)2. REPORT Approved for public release; distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTBackground: This study was a 10-year cross sectional analysis of human factors in U.S. military UAV mishaps. Methods: Class A-C UAV mishap reports were reviewed and human factors coded using the Human Factors Analysis and Classification System (HFACS). Binary logistic regression was used to create models predicting unsafe operator acts. Results: 133/221 (60.2%) UAV mishaps were human related. Predictors of unsafe acts were technological environment and cognitive factors in the Air Force (P < 0.010), organizational processes, psycho-behavioral factors, and crew resource management in the Army (P < 0.001), and work and attention and risk management in the Navy (P < 0.025). The frequency of specific types of unsafe acts differed between the services with skill-based errors more common in the Air Force (P = 0.001) and violations in the Army (P = 0.016). Conclusion: Recurring latent failures at the organizational, supervisory, and preconditions levels contributed to more than half of UAV mishaps. The patterns of latent failures and unsafe acts differed between the services. SUBJECT TERMSUnmanned aerial vehicles, Uninhabited aerial vehicles, Remotely piloted aircraft, Remotely piloted vehicles, Human factors analysis and classification system, Accidents, Mishaps, Human factors. .............................................................................................. ................................................................................................................................. 5 Study Design ..................................................... 5 D ata ..................................................................................................................................... 6 Human Factors Classification using HFACS ............................................................. The rapid rise in unmanned aerial vehicle (UAV) employment has been accompanied by increased attention to their high mishap rates which are several orders of magnitude greater than manned aviation. Such high rates have negative implications for UAV affordability and mission availability and are unacceptable in light of the Secretary of Defense's challenge to "reduce...

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

US Military Unmanned Aerial Vehicle Mishaps: Assessment of the Role of Human Factors Using Human Factors Analysis and Classification System (HFACS)

Tvaryanas¹,

Thompson²,

Constable³

2005

PsycEXTRA Dataset

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“…Hanks, et al have implicated breakdowns in the communication of domain knowledge as a major cause of requirements defects in high-assurance systems [5]. Weiss, et al have cited requirements misunderstanding in several recent disasters, stating, "software-related accidents almost always are due to misunderstanding about what the software should do" [15]. Similarly, in summarizing the results of a large defect-analysis study, Leszak, et al stated that "domain and system knowledge continue to be one of the largest underlying problems in software development" [7].…”

Section: Related Workmentioning

confidence: 99%

Using occurrence properties of defect report data to improve requirements

Wasson

Schmid

Lutz

et al. 2005

13th IEEE International Conference on Requirements Engineering (RE'05)

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Defect reports generated for faults found during testing provide a rich source of information regarding problematic phrases used in requirements documents. These reports indicate that faults often derive from instances of ambiguous, incorrect or otherwise deficient language. In this paper, we report on a method combining elements of linguistic theory and information retrieval to guide the discovery of problematic phrases throughout a requirements specification, using defect reports and correction requests generated during testing to seed our detection process. We found that phrases known from these materials to be problematic have occurrence properties in requirements documents that both allow the direction of resources to prioritize their correction, and generate insights characterizing more general locations of difficulty within the requirements. Our findings lead to some recommendations for more efficiently and effectively managing certain natural language issues in the creation and maintenance of requirements specifications.

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Envisioning a Requirements Specification Template for Medical Device Software

Wang¹,

Chen

Khédri

et al. 2014

Product-Focused Software Process Improvement

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Abstract. In many health jurisdictions, software is considered to be medical device software (MDS), when it is used to analyze patient data in order to render a diagnosis or monitor the patient's health; when it is to be used by a patient to diagnose an ailment; or when it is used to deliver functionality for a medical device. Flaws in MDS can result in patient harm, including death. Legislators and regulatory agencies publish guidelines and regulatory standards that are aimed at ensuring the safety, security and dependability of MDS. These guidelines and standards universally agree that a complete and consistent requirement specification is vital to the success of medical device software. Moreover, we observe that regulators are shifting from being process focused to being product focused in their approval guidance. In this paper, we review challenges associated with requirements used in the development of MDS, current standards and guidelines relevant to MDS, and existing templates for requirement specifications. We then propose a set of design objectives for a 'good' MDS requirements template and propose a template structure for MDS requirement specification fulfilling all the design objectives. Our template is, by design, tailored to facilitate the gathering and documenting of high quality requirements for MDS.

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An analysis of causation in aerospace accidents

Cited by 18 publications

References 0 publications

US Military Unmanned Aerial Vehicle Mishaps: Assessment of the Role of Human Factors Using Human Factors Analysis and Classification System (HFACS)

US Military Unmanned Aerial Vehicle Mishaps: Assessment of the Role of Human Factors Using Human Factors Analysis and Classification System (HFACS)

Using occurrence properties of defect report data to improve requirements

Envisioning a Requirements Specification Template for Medical Device Software

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