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Larson, Steve

Publications and source records attributed to Larson, Steve.

Analysis of Phoenix Anomalies and IV and V Findings Applied to the GRAIL Mission

Analysis of patterns in IV&V findings and their correlation with post-launch anomalies allowed GRAIL to make more efficient use of IV&V services . Fewer issues. . Higher fix rate. . Better communication. . Increased volume of potential issues vetted, at lower cost. . Hard to make predictions of post-launch performance based on IV&V findings . Phoenix made sound fix/use as-is decisions . Things that were fixed eliminated some problems, but hard to quantify. . Broad predictive success in one area, but inverse relationship in others.

Gravity Recovery and Interior Laboratory (GRAIL)

Cognitive Bias in Systems Verification

Working definition of cognitive bias: Patterns by which information is sought and interpreted that can lead to systematic errors in decisions. Cognitive bias is used in diverse fields: Economics, Politics, Intelligence, Marketing, to name a few. Attempts to ground cognitive science in physical characteristics of the cognitive apparatus exceed our knowledge. Studies based on correlations; strict cause and effect is difficult to pinpoint. Effects cited in the paper and discussed here have been replicated many times over, and appear sound. Many biases have been described, but it is still unclear whether they are all distinct. There may only be a handful of fundamental biases, which manifest in various ways. Bias can effect system verification in many ways . Overconfidence -> Questionable decisions to deploy. Availability -> Inability to conceive critical tests. Representativeness -> Overinterpretation of results. Positive Test Strategies -> Confirmation bias. Debiasing at individual level very difficult. The potential effect of bias on the verification process can be managed, but not eliminated. Worth considering at key points in the process.

Cognitive bias

Analysis of Phoenix Anomalies and IV & V Findings Applied to the GRAIL Mission

NASA IV&V was established in 1993 to improve safety and cost-effectiveness of mission critical software. Since its inception the tools and strategies employed by IV&V have evolved. This paper examines how lessons learned from the Phoenix project were developed and applied to the GRAIL project. Shortly after selection, the GRAIL project initiated a review of the issues documented by IV&V for Phoenix. The motivation was twofold: the learn as much as possible about the types of issues that arose from the flight software product line slated for use on GRAIL, and to identify opportunities for improving the effectiveness of IV&V on GRAIL. The IV&V Facility provided a database dump containing 893 issues. These were categorized into 16 bins, and then analyzed according to whether the project responded by changing the affected artifacts or using as-is. The results of this analysis were compared to a similar assessment of post-launch anomalies documented by the project. Results of the analysis were discussed with the IV&V team assigned to GRAIL. These discussions led to changes in the way both the project and IV&V approached the IV&V task, and improved the efficiency of the activity.

Larson, Steve

Cognitive Bias in the Verification and Validation of Space Flight Systems

Cognitive bias is generally recognized as playing a significant role in virtually all domains of human decision making. Insight into this role is informally built into many of the system engineering practices employed in the aerospace industry. The review process, for example, typically has features that help to counteract the effect of bias. This paper presents a discussion of how commonly recognized biases may affect the verification and validation process. Verifying and validating a system is arguably more challenging than development, both technically and cognitively. Whereas there may be a relatively limited number of options available for the design of a particular aspect of a system, there is a virtually unlimited number of potential verification scenarios that may be explored. The probability of any particular scenario occurring in operations is typically very difficult to estimate, which increases reliance on judgment that may be affected by bias. Implementing a verification activity often presents technical challenges that, if they can be overcome at all, often result in a departure from actual flight conditions (e.g., 1-g testing, simulation, time compression, artificial fault injection) that may raise additional questions about the meaningfulness of the results, and create opportunities for the introduction of additional biases. In addition to mitigating the biases it can introduce directly, the verification and validation process must also overcome the cumulative effect of biases introduced during all previous stages of development. A variety of cognitive biases will be described, with research results for illustration. A handful of case studies will be presented that show how cognitive bias may have affected the verification and validation process on recent JPL flight projects, identify areas of strength and weakness, and identify potential changes or additions to commonly used techniques that could provide a more robust verification and validation of future systems.

Larson, Steve

AVIRIS ground data processing system

During the last year and a half, Feb. 1991 to Jun. 1992, a major upgrade of the Airborne Visible/Infrared Imaging Spectrometers (AVIRIS) ground data processing system took place. Both the hardware and software components were changed significantly to improve the processing capacity and performance and to structure a data facility capable of handling the projected work load into the near future. A summary report of these changes and some projections for the future are provided. The objectives of the AVIRIS data facility are to decommutate and archive AVIRIS data and to provide raw or radiometrically calibrated data products to the science investigator. These primary objectives have not changed from the initial concepts. The upgrade effort has greatly improved the processing system. These objectives can now be accomplished in a more timely fashion at a reasonable cost and there is sufficient capacity to manage the current processing load and provide for future growth. The method of implementation added the flexibility to provide better service to the investigator and allow for future changes.

Hansen, Earl G.