2011Unpublished venueRequires access

Precomputing possible configuration error diagnoses

Ariel Rabkin, Randy H. Katz

Open publisher page 86 citations

Abstract

Complex software packages, particularly systems software, often require substantial customization before being used. Small mistakes in configuration can lead to hard-todiagnose error messages. We demonstrate how to build a map from each program point to the options that might cause an error at that point. This can aid users in troubleshooting these errors without any need to install or use additional tools. Our approach relies on static dataflow analysis, meaning all the analysis is done in advance. We evaluate our work in detail on two substantial systems, Hadoop and the JChord program analysis toolkit, using failure injection and also by using log messages as a source of labeled program points. When logs and stack traces are available, they can be incorporated into the analysis. This reduces the number of false positives by nearly a factor of four for Hadoop, at the cost of approximately one minute's work per unique query.

About this research paper

What this paper is about

Complex software packages, particularly systems software, often require substantial customization before being used. Small mistakes in configuration can lead to hard-todiagnose error messages. We demonstrate how to build a map from each program point to the options that might cause an error at that point. This can aid users in troubleshooting these errors without any need to install or use additional tools. Our approach relies on static dataflow analysis, meaning all the analysis is done in advance. We evaluate our work in detail on two substantial systems, Hadoop and the JChord program analysis toolkit, using failure injection and also by using log messages as a source of labeled program points. When logs and stack traces are available, they can be incorporated into the analysis. This reduces the number of false positives by nearly a factor of four for Hadoop, at the cost of approximately one minute's work per unique query.

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OpenAlex reports 86 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Complex software packages, particularly systems software, often require substantial customization before being used. Small mistakes in configuration can lead to hard-todiagnose error messages. We demonstrate how to build a map from each program point to the options that might cause an error at that point. This can aid users in troubleshooting these errors without any need to install or use additional tools. Our approach relies on static dataflow analysis, meaning all the analysis is done in advance. We evaluate our work in detail on two substantial systems, Hadoop and the JChord program analysis toolkit, using failure injection and also by using log messages as a source of labeled program points. When logs and stack traces are available, they can be incorporated into the analysis. This reduces the number of false positives by nearly a factor of four for Hadoop, at the cost of approximately one minute's work per unique query.

Key concepts: Computer science, Troubleshooting, Dataflow, False positive paradox, Software, Point (geometry), Program analysis, Static analysis

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