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Results for “Orthogonal Defect Classification”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Adapting ODC for Empirical Evaluation of Pre-Launch Anomalies

This slide presentation reviews the concept of using Orthogonal Defect Classification (ODC) to identify pre-launch anomalies in software. The goals of this work are: (1) To characterize pre-launch software anomalies, using data from multiple spacecraft projects, by means of a defect-analysis technology, Orthogonal Defect Classification (ODC). (2) To support transfer of ODC to NASA projects through applications and demonstrations. Approach: Analyzed anomaly data using adaptation of Orthogonal Defect Classification (ODC) method. This project has adapted ODC for NASA use and applied to NASA projects.

defect analysis↗
DOE OSTI2025

Automating Bug Report Classification with Few Shot Learning

Orthogonal defect classification (ODC) is a method used to categorize software defects, providing valuable insights into the development process. This study focuses on automating the classification of software bug reports into different ODC defect types using few shot learning, a machine learning approach that requires minimal labeled data. Previous research has manually classified bug reports or used traditional machine learning algorithms like linear support vector machine, achieving limited success. Our approach uses few shot learning to improve classification accuracy and efficiency. The results show a harmonic mean of recall and precision (i.e., the F1 score) of around 0.6 which is a performance improvement over previous methods. The results highlight the potential benefit of few shot learning techniques and their application in enhancing the safety and reliability of nuclear digital instrumentation and control (DI&C) systems. Future work will explore incorporating advanced techniques to supplement the model's training data and achieve better results.

42 - ENGINEERING↗
NASA NTRS2004

Research Infusion Collaboration: Finding Defect Patterns in Reused Code

The 'Finding Defect Patterns in Reused Code' Research Infusion Collaboration was performed by Jet Propulsion Laboratory/Caltech under Contract 104-07-02.679 102 197 08.14.4. This final report describes the collaboration and documents the findings, including lessons learned.The research infusion collaboration characterized, using Orthogonal Defect Classification, defect reports for code that will be reused in mission-critical software on Deep Space Network Antenna controllers. Code reuse is estimated to be 90%, so it is important to identify systemic defects, or patterns, prior to reuse of this code. The work also identified ways to avoid certain types of defects and to test more efficiently.The primary objectives of the project were:to analyze defect patterns of the code to be reused based on the defects'Orthogonal Defect Classification (ODC)and to achieve a successful infusion of ODC to a project.

orthogonal defect classification (ODC)↗