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Automated Statistical Approach for Memory Leak Detection: Case Studies

  • Conference paper
On the Move to Meaningful Internet Systems: OTM 2011 (OTM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7045))

Abstract

Applications written in JavaTM language, and in other programming languages running on JavaTM Virtual Machine (JVM) are widely used in cloud environments. Although JVM features garbage collection, memory leaks can still happen in these applications. Current solutions for finding memory leaks have several drawbacks which become critical when deployed in distributed and dynamic environments like cloud. Statistical approach for memory leak detection gives good results in terms of false positives and we have implemented automatic statistical approach for memory leak detection in JavaTM applications. To test its correctness and performance we have conducted several experiments by finding memory leaks in a large web-application and searching for related bugs in open source projects from Apache Software Foundation. This paper presents the results of these experiments and concludes that automated statistical method for memory leak detection is efficient and can be used also in production systems to find hardly reproducible leaks.

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References

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Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Tartu, J. Liivi 2, Tartu, Estonia

    Vladimir Šor & Satish Narayana Srirama

  2. AS Webmedia R&D, Raatuse 20, Tartu, Estonia

    Vladimir Šor & Nikita Salnikov-Tarnovski

Authors
  1. Vladimir Šor
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  2. Nikita Salnikov-Tarnovski
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  3. Satish Narayana Srirama
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Editor information

Editors and Affiliations

  1. STAR Lab, Vrije Universiteit Brussel (VUB), Bldg G/10, Pleinlaan 2, 1050, Brussel, Belgium

    Robert Meersman

  2. DEBII, Curtin University of Technology, Technology Park, De Laeter Way, 6102, Bentley, WA, Australia

    Tharam Dillon

  3. Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660, Boadilla del Monte, Madrid, Spain

    Pilar Herrero

  4. Smeal College of Business, Pennsylvania State University, University Park, 16802, P.O. Box, PA, U.S.A.

    Akhil Kumar

  5. Institute of Databases and Information Systems, Ulm University, Germany

    Manfred Reichert

  6. City University of Hong Kong, Hong Kong

    Li Qing

  7. National University of Singapore (NUS), Singapore

    Beng-Chin Ooi

  8. Dipartemento Tecnologie dell’Informazione, Universitá degli Studi di Milano, Via Bramante 65, 26013, Crema, Italy

    Ernesto Damiani

  9. Vanderbilt University, VU, Station B #1829, 2015 Terrace Place, 37203, Nashville, TN, USA

    Douglas C. Schmidt

  10. Virginia Tech, 24060, Blacksburg, VA

    Jules White

  11. Digital Enterprise Research Institute (DERI),, National University of Ireland, IDA Business Park, Lower Dangan, Galway, Ireland

    Manfred Hauswirth

  12. Kno.e.sis Center, Wright State University, Dayton,, Ohio

    Pascal Hitzler

  13. IBM India Research Lab, 4, Block C, Institutional Area, 110 070, Vasant Kunj, New Delhi, India

    Mukesh Mohania

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© 2011 Springer-Verlag Berlin Heidelberg

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Šor, V., Salnikov-Tarnovski, N., Srirama, S.N. (2011). Automated Statistical Approach for Memory Leak Detection: Case Studies. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2011. OTM 2011. Lecture Notes in Computer Science, vol 7045. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25106-1_16

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  • DOI: https://doi.org/10.1007/978-3-642-25106-1_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25105-4

  • Online ISBN: 978-3-642-25106-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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