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Performance Debugging in the Large via Mining Millions of Stack Traces.

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Title: Performance Debugging in the Large via Mining Millions of Stack Traces.
Authors: Han, Shi1 shihan@microsoft.com, Dang, Yingnong1 yidang@microsoft.com, Ge, Song1 songge@microsoft.com, Zhang, Dongmei1 dongmeiz@microsoft.com, Xie, Tao2 xie@csc.ncsu.edu
Source: ICSE: International Conference on Software Engineering. Feb2012, p145-155. 11p.
Subjects: Debugging, Computer system failure prevention, Data mining, Data security failures, Computer software, Microsoft Corp., Computer networks
Abstract: Given limited resource and time before software release, development-site testing and debugging become more and more insufficient to ensure satisfactory software performance. As a counterpart for debugging in the large pioneered by the Microsoft Windows Error Reporting (WER) system focusing on crashing/hanging bugs, performance debugging in the large has emerged thanks to available infrastructure support to collect execution traces with performance issues from a huge number of users at the deployment sites. However, performance debugging against these numerous and complex traces remains a significant challenge for performance analysts. In this paper, to enable performance debugging in the large in practice, we propose a novel approach, called StackMine, that mines callstack traces to help performance analysts effectively discover highly impactful performance bugs (e.g., bugs impacting many users with long response delay). As a successful technology-transfer effort, since December 2010, StackMine has been applied in performance-debugging activities at a Microsoft team for performance analysis, especially for a large number of execution traces. Based on real-adoption experiences of StackMine in practice, we conducted an evaluation of StackMine on performance debugging in the large for Microsoft Windows 7. We also conducted another evaluation on a third-party application. The results highlight substantial benefits offered by StackMine in performance debugging in the large for large-scale software systems. [ABSTRACT FROM AUTHOR]
Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Performance Debugging in the Large via Mining Millions of Stack Traces.
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  Data: <searchLink fieldCode="AR" term="%22Han%2C+Shi%22">Han, Shi</searchLink><relatesTo>1</relatesTo><i> shihan@microsoft.com</i><br /><searchLink fieldCode="AR" term="%22Dang%2C+Yingnong%22">Dang, Yingnong</searchLink><relatesTo>1</relatesTo><i> yidang@microsoft.com</i><br /><searchLink fieldCode="AR" term="%22Ge%2C+Song%22">Ge, Song</searchLink><relatesTo>1</relatesTo><i> songge@microsoft.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Dongmei%22">Zhang, Dongmei</searchLink><relatesTo>1</relatesTo><i> dongmeiz@microsoft.com</i><br /><searchLink fieldCode="AR" term="%22Xie%2C+Tao%22">Xie, Tao</searchLink><relatesTo>2</relatesTo><i> xie@csc.ncsu.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. Feb2012, p145-155. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Debugging%22">Debugging</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+system+failure+prevention%22">Computer system failure prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Data+security+failures%22">Data security failures</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software%22">Computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Microsoft+Corp%2E%22">Microsoft Corp.</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+networks%22">Computer networks</searchLink>
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  Data: Given limited resource and time before software release, development-site testing and debugging become more and more insufficient to ensure satisfactory software performance. As a counterpart for debugging in the large pioneered by the Microsoft Windows Error Reporting (WER) system focusing on crashing/hanging bugs, performance debugging in the large has emerged thanks to available infrastructure support to collect execution traces with performance issues from a huge number of users at the deployment sites. However, performance debugging against these numerous and complex traces remains a significant challenge for performance analysts. In this paper, to enable performance debugging in the large in practice, we propose a novel approach, called StackMine, that mines callstack traces to help performance analysts effectively discover highly impactful performance bugs (e.g., bugs impacting many users with long response delay). As a successful technology-transfer effort, since December 2010, StackMine has been applied in performance-debugging activities at a Microsoft team for performance analysis, especially for a large number of execution traces. Based on real-adoption experiences of StackMine in practice, we conducted an evaluation of StackMine on performance debugging in the large for Microsoft Windows 7. We also conducted another evaluation on a third-party application. The results highlight substantial benefits offered by StackMine in performance debugging in the large for large-scale software systems. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Computer system failure prevention
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      – SubjectFull: Data mining
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