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Port, Dan

Publications and source records attributed to Port, Dan.

7 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Staffing Strategies for Maintenance of Critical Software Systems at the Jet Propulsion Laboratory

The Mission Design and Navigation Software Group at the Jet Propulsion Laboratory (JPL) maintains mission critical software systems. We have good empirical data and models for maintenance demand—when defects will occur, how many and how severe they will be, and how much effort is needed to address them. However, determining the level of staffing needed to address maintenance issues is an ongoing challenge and is often done ad-hoc. There are two common strategies are (1) reactive – add/remove staff as needed to respond to maintenance issues, and (2) capacitive – retain a given staff size to address issues as they occur, proactively address issues and prevent defects.

software maintenance↗
NASA NTRS2014

Empirical and Face Validity of Software Maintenance Defect Models Used at the Jet Propulsion Laboratory

At the Mission Design and Navigation Software Group at the Jet Propulsion Laboratory we make use of finite exponential based defect models to aid in maintenance planning and management for our widely used critical systems. However a number of pragmatic issues arise when applying defect models for a post-release system in continuous use. These include: how to utilize information from problem reports rather than testing to drive defect discovery and removal effort, practical model calibration, and alignment of model assumptions with our environment.

software reliability↗
NASA NTRS2013

Tool Use Within NASA Software Quality Assurance

As space mission software systems become larger and more complex, it is increasingly important for the software assurance effort to have the ability to effectively assess both the artifacts produced during software system development and the development process itself. Conceptually, assurance is a straightforward idea - it is the result of activities carried out by an organization independent of the software developers to better inform project management of potential technical and programmatic risks, and thus increase management's confidence in the decisions they ultimately make. In practice, effective assurance for large, complex systems often entails assessing large, complex software artifacts (e.g., requirements specifications, architectural descriptions) as well as substantial amounts of unstructured information (e.g., anomaly reports resulting from testing activities during development). In such an environment, assurance engineers can benefit greatly from appropriate tool support. In order to do so, an assurance organization will need accurate and timely information on the tool support available for various types of assurance activities. In this paper, we investigate the current use of tool support for assurance organizations within NASA, and describe on-going work at JPL for providing assurance organizations with the information about tools they need to use them effectively.

software assurance↗
NASA NTRS2011

Experiences with Text Mining Large Collections of Unstructured Systems Development Artifacts at JPL

Often repositories of systems engineering artifacts at NASA's Jet Propulsion Laboratory (JPL) are so large and poorly structured that they have outgrown our capability to effectively manually process their contents to extract useful information. Sophisticated text mining methods and tools seem a quick, low-effort approach to automating our limited manual efforts. Our experiences of exploring such methods mainly in three areas including historical risk analysis, defect identification based on requirements analysis, and over-time analysis of system anomalies at JPL, have shown that obtaining useful results requires substantial unanticipated efforts - from preprocessing the data to transforming the output for practical applications. We have not observed any quick 'wins' or realized benefit from short-term effort avoidance through automation in this area. Surprisingly we have realized a number of unexpected long-term benefits from the process of applying text mining to our repositories. This paper elaborates some of these benefits and our important lessons learned from the process of preparing and applying text mining to large unstructured system artifacts at JPL aiming to benefit future TM applications in similar problem domains and also in hope for being extended to broader areas of applications.

text mining↗