A Proposal for Sniffer: a System that Understands Bugs

This paper proposes an interactive debugging aid that exhibits a deep understanding of a narrow class of bugs. This system, called Sniffer, will be able to find and identify errors, and explain them in terms which are relevant to the programmer. Sniffer is knowledgeable about side-effects. It is cap...

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Autor principal: Shapiro, Daniel G.
Formato: Working Paper
Idioma:en_US
Publicado em: MIT Artificial Intelligence Laboratory 2008
Acesso em linha:http://hdl.handle.net/1721.1/41154
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author Shapiro, Daniel G.
author_facet Shapiro, Daniel G.
author_sort Shapiro, Daniel G.
collection MIT
description This paper proposes an interactive debugging aid that exhibits a deep understanding of a narrow class of bugs. This system, called Sniffer, will be able to find and identify errors, and explain them in terms which are relevant to the programmer. Sniffer is knowledgeable about side-effects. It is capable of citing the data which was in effect at the time an error became manifest. The debugging knowledge in Sniffer is organized as a collection of independent experts which know about particular errors. The experts (sniffers) perform their function by applying a feature recognition process to the text for the program, and to the events which took place during the execution of the code. No deductive machinery is involved. The experts are supported by two systems; the cliche finder which identifies small portions of algorithms from a plan for the code, and the time rover which provides complete access to all program states that ever existed. Sniffer is embedded in a run-time debugging aid. The user of the system interacts with the debugger to focus attention onto a manageable subset of the code, and then submits a complaint to the sniffer system that describes the behavior which was desired. Sniffer outputs a detailed report about any error which is discovered.
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spelling mit-1721.1/411542019-04-10T22:36:22Z A Proposal for Sniffer: a System that Understands Bugs Shapiro, Daniel G. This paper proposes an interactive debugging aid that exhibits a deep understanding of a narrow class of bugs. This system, called Sniffer, will be able to find and identify errors, and explain them in terms which are relevant to the programmer. Sniffer is knowledgeable about side-effects. It is capable of citing the data which was in effect at the time an error became manifest. The debugging knowledge in Sniffer is organized as a collection of independent experts which know about particular errors. The experts (sniffers) perform their function by applying a feature recognition process to the text for the program, and to the events which took place during the execution of the code. No deductive machinery is involved. The experts are supported by two systems; the cliche finder which identifies small portions of algorithms from a plan for the code, and the time rover which provides complete access to all program states that ever existed. Sniffer is embedded in a run-time debugging aid. The user of the system interacts with the debugger to focus attention onto a manageable subset of the code, and then submits a complaint to the sniffer system that describes the behavior which was desired. Sniffer outputs a detailed report about any error which is discovered. MIT Artificial Intelligence Laboratory 2008-04-14T13:34:48Z 2008-04-14T13:34:48Z 1980-07 Working Paper http://hdl.handle.net/1721.1/41154 en_US MIT Artificial Intelligence Laboratory Working Papers, WP-202 application/pdf MIT Artificial Intelligence Laboratory
spellingShingle Shapiro, Daniel G.
A Proposal for Sniffer: a System that Understands Bugs
title A Proposal for Sniffer: a System that Understands Bugs
title_full A Proposal for Sniffer: a System that Understands Bugs
title_fullStr A Proposal for Sniffer: a System that Understands Bugs
title_full_unstemmed A Proposal for Sniffer: a System that Understands Bugs
title_short A Proposal for Sniffer: a System that Understands Bugs
title_sort proposal for sniffer a system that understands bugs
url http://hdl.handle.net/1721.1/41154
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