Engineering topics

Rushby, John

Publications and source records attributed to Rushby, John.

23 records · Page 2

NASA NTRS1990

A verified model of fault-tolerance

The main objectives are: a model of a replicated system with exact-match voting; a fault model that includes transients; a theorem that establishes the conditions under which the system provides fault tolerance; a formal specification of the model; and a mechanically checked verification of the theorem that is consonant with the journal-level presentation. Formal specification and verification revealed typos in the original report, exposed omission in original proof, led to the stronger theorem and more elegant proof, and confirmed that Enhanced Hierarchical Development Methodology (EHDM) has the capability to specify interesting and useful properties in a direct, natural, and readable manner.

Rushby, John↗

NASA NTRS1989

Formal verification of a fault tolerant clock synchronization algorithm

A formal specification and mechanically assisted verification of the interactive convergence clock synchronization algorithm of Lamport and Melliar-Smith is described. Several technical flaws in the analysis given by Lamport and Melliar-Smith were discovered, even though their presentation is unusally precise and detailed. It seems that these flaws were not detected by informal peer scrutiny. The flaws are discussed and a revised presentation of the analysis is given that not only corrects the flaws but is also more precise and easier to follow. Some of the corrections to the flaws require slight modifications to the original assumptions underlying the algorithm and to the constraints on its parameters, and thus change the external specifications of the algorithm. The formal analysis of the interactive convergence clock synchronization algorithm was performed using the Enhanced Hierarchical Development Methodology (EHDM) formal specification and verification environment. This application of EHDM provides a demonstration of some of the capabilities of the system.

Rushby, John↗

NASA NTRS1989

Formal verification of AI software

The application of formal verification techniques to Artificial Intelligence (AI) software, particularly expert systems, is investigated. Constraint satisfaction and model inversion are identified as two formal specification paradigms for different classes of expert systems. A formal definition of consistency is developed, and the notion of approximate semantics is introduced. Examples are given of how these ideas can be applied in both declarative and imperative forms.

Rushby, John↗

NASA NTRS1988

Specifying real-time systems with interval logic

Pure temporal logic makes no reference to time. An interval temporal logic and an extension to that logic which includes real time constraints are described. The application of this logic by giving a specification for the well-known lift (elevator) example is demonstrated. It is shown how interval logic can be extended to include a notion of process. How the specification language and verification environment of EHDM could be enhanced to support this logic is described. A specification of the alternating bit protocol in this extended version of the specification language of EHDM is given.

Rushby, John↗

NASA NTRS1988

Quality measures and assurance for AI (Artificial Intelligence) software

This report is concerned with the application of software quality and evaluation measures to AI software and, more broadly, with the question of quality assurance for AI software. Considered are not only the metrics that attempt to measure some aspect of software quality, but also the methodologies and techniques (such as systematic testing) that attempt to improve some dimension of quality, without necessarily quantifying the extent of the improvement. The report is divided into three parts Part 1 reviews existing software quality measures, i.e., those that have been developed for, and applied to, conventional software. Part 2 considers the characteristics of AI software, the applicability and potential utility of measures and techniques identified in the first part, and reviews those few methods developed specifically for AI software. Part 3 presents an assessment and recommendations for the further exploration of this important area.

Rushby, John↗