EPSRC Reference: |
GR/M76775/01 |
Title: |
HIGH RELIABILITY TESTING FOR COMPLEX SOFTWARE USING BAYESIAN GRAPHICAL MODELLING AND PROGRAM COMPREHENSION |
Principal Investigator: |
Goldstein, Professor M |
Other Investigators: |
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Researcher Co-Investigators: |
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Project Partners: |
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Department: |
Mathematical Sciences |
Organisation: |
Durham, University of |
Scheme: |
Standard Research (Pre-FEC) |
Starts: |
01 October 1999 |
Ends: |
30 September 2002 |
Value (£): |
145,729
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EPSRC Research Topic Classifications: |
Statistics & Appl. Probability |
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EPSRC Industrial Sector Classifications: |
Communications |
Information Technologies |
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Related Grants: |
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Panel History: |
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Summary on Grant Application Form |
Software reliability is a problem of enormous practical importance. However, in most software testing, there is no quantification of the uncertainties associated with potential software failures. We address the problem of rigorously and efficiently testing complex systems such as computer software. We construct generalised Bayesian graphical models for system testing, synthesising probabilistic, Bayes linear, and imprecise components reflecting partial knowledge of the complex system. These models combine expert prior judgements with logical information about the structure of the software as provided by program comprehension analysis, to provide a unified framework for software comprehension. We shall develop a design methodology for these generalised graphical models, which is appropriate both for early stages of testing, where we must identify major faults as quickly as possible, and also for testing to high reliability later in the development cycle, where we must be confident that the system is acceptable if it successfully passes the final testing stage. The methodology may be adapted to cover many types of complex system which must be tested to high reliability under severe time constraints. The approach will be tested on a series of case studies provided by our collaborator BT.
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Key Findings |
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Potential use in non-academic contexts |
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Impacts |
Description |
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Summary |
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Date Materialised |
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Sectors submitted by the Researcher |
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Project URL: |
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Further Information: |
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Organisation Website: |
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