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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Storage management in Ada. Three reports. Volume 1: Storage management in Ada as a risk to the development of reliable software. Volume 2: Relevant aspects of language. Volume 3: Requirements of the language versus manifestations of current implementations

The risk to the development of program reliability is derived from the use of a new language and from the potential use of new storage management techniques. With Ada and associated support software, there is a lack of established guidelines and procedures, drawn from experience and common usage, which assume reliable behavior. The risk is identified and clarified. In order to provide a framework for future consideration of dynamic storage management on Ada, a description of the relevant aspects of the language is presented in two sections: Program data sources, and declaration and allocation in Ada. Storage-management characteristics of the Ada language and storage-management characteristics of Ada implementations are differentiated. Terms that are used are defined in a narrow and precise sense. The storage-management implications of the Ada language are described. The storage-management options available to the Ada implementor and the implications of the implementor's choice for the Ada programmer are also described.

Auty, David↗
NASA NTRS1986

Detection of faults and software reliability analysis

Multiversion or N-version programming was proposed as a method of providing fault tolerance in software. The approach requires the separate, independent preparation of multiple versions of a piece of software for some application. Specific topics addressed are: failure probabilities in N-version systems, consistent comparison in N-version systems, descriptions of the faults found in the Knight and Leveson experiment, analytic models of comparison testing, characteristics of the input regions that trigger faults, fault tolerance through data diversity, and the relationship between failures caused by automatically seeded faults.

Knight, J. C.↗
NASA NTRS1987

A measurement-based model of software reliability in a production environment

In this paper, a semi-Markov model is built to describe the software error and recovery process in a large mainframe system. The model is based on low-level error data from the MVS operating system running on an IBM 3081 machine. The semi-Markov model developed provides a quantification of system error characteristics and the interaction between different types of errors. As an example, a detailed model is provided, and an analysis is made of multiple errors, which constitute approximately an 17 percent of all software errors and result in considerable recovery overhead.

Hsueh, M. C.↗
NASA NTRS1984

Software reliability: Additional investigations into modeling with replicated experiments

The effects of programmer experience level, different program usage distributions, and programming languages are explored. All these factors affect performance, and some tentative relational hypotheses are presented. An analytic framework for replicated and non-replicated (traditional) software experiments is presented. A method of obtaining an upper bound on the error rate of the next error is proposed. The method was validated empirically by comparing forecasts with actual data. In all 14 cases the bound exceeded the observed parameter, albeit somewhat conservatively. Two other forecasting methods are proposed and compared to observed results. Although demonstrated relative to this framework that stages are neither independent nor exponentially distributed, empirical estimates show that the exponential assumption is nearly valid for all but the extreme tails of the distribution. Except for the dependence in the stage probabilities, Cox's model approximates to a degree what is being observed.

Nagel, P. M.↗
NASA NTRS1975

Automatic documentation system extension to multi-manufacturers' computers and to measure, improve, and predict software reliability. Appendix A and B

A manual which explains how to use the DOMONIC command language is presented. The manual consists of the following sections: 1. Introduction 2. What You Must Know to Use DOMONIC 3. Functions of DOMONIC 4. Entering and Manipulating Data 5. Templates and Data Definitions 6. Recipes and Document Generation 7. Initiating A Project and 8. Entering and Changing Security Controls. The first four sections must be known by all DOMONIC users. Sections five, seven and eight describe functions normally used only by project management. Section six tells how to generate documents. The manual specifies what commands to use in performing each of the functions mentioned above.

Simmons, D. B.↗
NASA NTRS1987

Detection of faults and software reliability analysis

Specific topics briefly addressed include: the consistent comparison problem in N-version system; analytic models of comparison testing; fault tolerance through data diversity; and the relationship between failures caused by automatically seeded faults.

Knight, J. C.↗
NASA NTRS1990

Software reliability through fault-avoidance and fault-tolerance

The use of back-to-back, or comparison, testing for regression test or porting is examined. The efficiency and the cost of the strategy is compared with manual and table-driven single version testing. Some of the key parameters that influence the efficiency and the cost of the approach are the failure identification effort during single version program testing, the extent of implemented changes, the nature of the regression test data (e.g., random), and the nature of the inter-version failure correlation and fault-masking. The advantages and disadvantages of the technique are discussed, together with some suggestions concerning its practical use.

Vouk, Mladen A.↗
NASA NTRS1987

A NASA initiative: Software engineering for reliable complex systems

The objective is the development of methods, technology, and skills that will enable NASA to cost-effectively specify, build, and manage reliable software which can evolve and be maintained over an extended period. The need for such software is rooted in the increasing integration of software and computing components into NASA systems. Current NASA Software Engineering expertise was applied toward some of the largest reliable systems including: shuttle launch; ground support; shuttle simulation; minor control; satellite tracking; and scientific data systems. Unfortunately, no theory exists for reliable complex software systems. NASA is seeking to fill this theoretical gap through a number of approaches. One such approach is to conduct research on theoretical foundations for managing complex software systems. It includes: communication models, new and modified paradigms, and life-cycle models. Another approach is research in the theoretical foundations for reliable software development and validation. It focuses upon formal specifications, programming languages, software engineering systems, software reuse, formal verification, and software safety. Further approaches involve benchmarking a NASA software environment, experimentation within the NASA context, evolution of present NASA methodology, and transfer of technology to the space station software support environment.

Holcomb, Lee B.↗
NASA NTRS1995

Software For Computing Reliability Of Other Software

Computer Aided Software Reliability Estimation (CASRE) computer program developed for use in measuring reliability of other software. Easier for non-specialists in reliability to use than many other currently available programs developed for same purpose. CASRE incorporates mathematical modeling capabilities of public-domain Statistical Modeling and Estimation of Reliability Functions for Software (SMERFS) computer program and runs in Windows software environment. Provides menu-driven command interface; enabling and disabling of menu options guides user through (1) selection of set of failure data, (2) execution of mathematical model, and (3) analysis of results from model. Written in C language.

Nikora, Allen↗