Engineering topics

Miller, Douglas R.

Publications and source records attributed to Miller, Douglas R..

9 records

NASA NTRS1992

Statistical modeling of software reliability

This working paper discusses the statistical simulation part of a controlled software development experiment being conducted under the direction of the System Validation Methods Branch, Information Systems Division, NASA Langley Research Center. The experiment uses guidance and control software (GCS) aboard a fictitious planetary landing spacecraft: real-time control software operating on a transient mission. Software execution is simulated to study the statistical aspects of reliability and other failure characteristics of the software during development, testing, and random usage. Quantification of software reliability is a major goal. Various reliability concepts are discussed. Experiments are described for performing simulations and collecting appropriate simulated software performance and failure data. This data is then used to make statistical inferences about the quality of the software development and verification processes as well as inferences about the reliability of software versions and reliability growth under random testing and debugging.

Miller, Douglas R.↗

NASA NTRS1991

Statistical modelling of software reliability

During the six-month period from 1 April 1991 to 30 September 1991 the following research papers in statistical modeling of software reliability appeared: (1) A Nonparametric Software Reliability Growth Model; (2) On the Use and the Performance of Software Reliability Growth Models; (3) Research and Development Issues in Software Reliability Engineering; (4) Special Issues on Software; and (5) Software Reliability and Safety.

Miller, Douglas R.↗

NASA NTRS1991

A nonparametric software-reliability growth model

The authors (1985) previously introduced a nonparametric model for software-reliability growth which is based on complete monotonicity of the failure rate. The authors extend the completely monotone software model by developing a method for providing long-range predictions of reliability growth, based on the model. They derive upper and lower bounds on extrapolation of the failure rate and the mean function. These are then used to obtain estimates for the future software failure rate and the mean future number of failures. Preliminary evaluation indicates that the method is competitive with parametric approaches, while being more robust.

Sofer, Ariela↗

NASA NTRS1991

On the use and the performance of software reliability growth models

We address the problem of predicting future failures for a piece of software. The number of failures occurring during a finite future time interval is predicted from the number failures observed during an initial period of usage by using software reliability growth models. Two different methods for using the models are considered: straightforward use of individual models, and dynamic selection among models based on goodness-of-fit and quality-of-prediction criteria. Performance is judged by the relative error of the predicted number of failures over future finite time intervals relative to the number of failures eventually observed during the intervals. Six of the former models and eight of the latter are evaluated, based on their performance on twenty data sets. Many open questions remain regarding the use and the performance of software reliability growth models.

Keiller, Peter A.↗

NASA NTRS1989

Conceptual modeling of coincident failures in multiversion software

Recent work by Eckhardt and Lee (1985) shows that independently developed program versions fail dependently (specifically, simultaneous failure of several is greater than would be the case under true independence). The present authors show there is a precise duality between input choice and program choice in this model and consider a generalization in which different versions can be developed using diverse methodologies. The use of diverse methodologies is shown to decrease the probability of the simultaneous failure of several versions. Indeed, it is theoretically possible to obtain versions which exhibit better than independent failure behavior. The authors try to formalize the notion of methodological diversity by considering the sequence of decision outcomes that constitute a methodology. They show that diversity of decision implies likely diversity of behavior for the different verions developed under such forced diversity. For certain one-out-of-n systems the authors obtain an optimal method for allocating diversity between versions. For two-out-of-three systems there seem to be no simple optimality results which do not depend on constraints which cannot be verified in practice.

Littlewood, Bev↗

NASA NTRS1988

Making statistical inferences about software reliability

Failure times of software undergoing random debugging can be modelled as order statistics of independent but nonidentically distributed exponential random variables. Using this model inferences can be made about current reliability and, if debugging continues, future reliability. This model also shows the difficulty inherent in statistical verification of very highly reliable software such as that used by digital avionics in commercial aircraft.

Miller, Douglas R.↗

NASA NTRS1988

A nonparametric software reliability growth model

Miller and Sofer have presented a nonparametric method for estimating the failure rate of a software program. The method is based on the complete monotonicity property of the failure rate function, and uses a regression approach to obtain estimates of the current software failure rate. This completely monotone software model is extended. It is shown how it can also provide long-range predictions of future reliability growth. Preliminary testing indicates that the method is competitive with parametric approaches, while being more robust.

Miller, Douglas R.↗

NASA NTRS1987

Software reliability models for fault-tolerant avionics computers and related topics

Software reliability research is briefly described. General research topics are reliability growth models, quality of software reliability prediction, the complete monotonicity property of reliability growth, conceptual modelling of software failure behavior, assurance of ultrahigh reliability, and analysis techniques for fault-tolerant systems.

Miller, Douglas R.↗

NASA NTRS1986

Making statistical inferences about software reliability

Failure times of software undergoing random debugging can be modeled as order statistics of independent but nonidentically distributed exponential random variables. Using this model inferences can be made about current reliability and, if debugging continues, future reliability. This model also shows the difficulty inherent in statistical verification of very highly reliable software such as that used by digital avionics in commercial aircraft.

Miller, Douglas R.↗