1976ACM Computing SurveysRequires access

Data Flow Analysis in Software Reliability

Lloyd D. Fosdick, Leon J. Osterweil

Open publisher page 271 citations

Abstract

The ways that the methods of data flow analysis can be applied to improve software reliability are described. There is also a review of the basic terminology from graph theory and from data flow analysis in global program optimization. The notation of regular expressions is used to describe actions on data for sets of paths.

About this research paper

What this paper is about

The ways that the methods of data flow analysis can be applied to improve software reliability are described. There is also a review of the basic terminology from graph theory and from data flow analysis in global program optimization. The notation of regular expressions is used to describe actions on data for sets of paths.

Why it matters

OpenAlex reports 271 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

The ways that the methods of data flow analysis can be applied to improve software reliability are described. There is also a review of the basic terminology from graph theory and from data flow analysis in global program optimization. The notation of regular expressions is used to describe actions on data for sets of paths.

Key concepts: Computer science, Citation, Reliability (semiconductor), Software, Library science, Software engineering, Operating system, Quantum mechanics

Related papers

Back to paper searchBrowse research topicsOriginal source
Data Flow Analysis in Software Reliability — Research Paper | ScholarLens