Statistical log differencing
Recent works have considered the problem of log differencing: given two or more system’s execution logs, output a model of their differences. Log differencing has potential applications in software evolution, testing, and security. In this paper we present statistical log differencing, which account...
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sg-smu-ink.sis_research-60982020-04-03T04:05:37Z Statistical log differencing BAO, Lingfeng BUSANY, Nimrod LO, David MAOZ, Shahar Recent works have considered the problem of log differencing: given two or more system’s execution logs, output a model of their differences. Log differencing has potential applications in software evolution, testing, and security. In this paper we present statistical log differencing, which accounts for frequencies of behaviors found in the logs. We present two algorithms, s2KDiff for differencing two logs, and snKDiff, for differencing of many logs at once, both presenting their results over a single inferred model. A unique aspect of our algorithms is their use of statistical hypothesis testing: we let the engineer control the sensitivity of the analysis by setting the target distance between probabilities and the statistical significance value, and report only (and all) the statistically significant differences. Our evaluation shows the effectiveness of our work in terms of soundness, completeness, and performance. It also demonstrates its effectiveness compared to previous work via a user-study and its potential applications via a case study using real-world logs. 2019-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5095 info:doi/10.1109/ASE.2019.00084 https://ink.library.smu.edu.sg/context/sis_research/article/6098/viewcontent/ase19_sld_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Log analysis Model inference software testing Software Engineering |
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Log analysis Model inference software testing Software Engineering BAO, Lingfeng BUSANY, Nimrod LO, David MAOZ, Shahar Statistical log differencing |
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Recent works have considered the problem of log differencing: given two or more system’s execution logs, output a model of their differences. Log differencing has potential applications in software evolution, testing, and security. In this paper we present statistical log differencing, which accounts for frequencies of behaviors found in the logs. We present two algorithms, s2KDiff for differencing two logs, and snKDiff, for differencing of many logs at once, both presenting their results over a single inferred model. A unique aspect of our algorithms is their use of statistical hypothesis testing: we let the engineer control the sensitivity of the analysis by setting the target distance between probabilities and the statistical significance value, and report only (and all) the statistically significant differences. Our evaluation shows the effectiveness of our work in terms of soundness, completeness, and performance. It also demonstrates its effectiveness compared to previous work via a user-study and its potential applications via a case study using real-world logs. |
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BAO, Lingfeng BUSANY, Nimrod LO, David MAOZ, Shahar |
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BAO, Lingfeng BUSANY, Nimrod LO, David MAOZ, Shahar |
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BAO, Lingfeng |
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Statistical log differencing |
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Statistical log differencing |
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Statistical log differencing |
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Statistical log differencing |
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Statistical log differencing |
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statistical log differencing |
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Institutional Knowledge at Singapore Management University |
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2019 |
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https://ink.library.smu.edu.sg/sis_research/5095 https://ink.library.smu.edu.sg/context/sis_research/article/6098/viewcontent/ase19_sld_av.pdf |
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