Skip to main content
SpringerLink
Log in

Program Analysis and Machine Learning: A Win-Win Deal

  • Conference paper
Static Analysis (SAS 2011)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6887))

Included in the following conference series:

Abstract

We give an account of our experiences working at the intersection of two fields: program analysis and machine learning. In particular, we show that machine learning can be used to infer annotations for program analysis tools, and that program analysis techniques can be used to improve the efficiency of machine learning tools.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Beckman, N., Nori, A.V.: Probabilistic, modular and scalable inference of typestate specifications. In: Programming Languages Design and Implementation (PLDI), pp. 211–221 (2011)

    Google Scholar 

  2. Chaganty, A., Lal, A., Nori, A.V., Rajamani, S.K.: Statistical inference modulo theories. Technical report, Microsoft Research (2011)

    Google Scholar 

  3. Kok, S., Sumner, M., Richardson, M., Singla, P., Poon, H., Lowd, D., Domingos, P.: The Alchemy system for statistical relational AI. Technical report, University of Washington, Seattle (2007), http://alchemy.cs.washington.edu

  4. Koller, D., McAllester, D.A., Pfeffer, A.: Effective Bayesian inference for stochastic programs. In: Fifteenth National Conference on Artificial Intelligence (AAAI), pp. 740–747 (1997)

    Google Scholar 

  5. Livshits, V.B., Nori, A.V., Rajamani, S.K., Banerjee, A.: Merlin: Specification inference for explicit information flow problems. In: Programming Languages Design and Implementation (PLDI), pp. 75–86 (2009)

    Google Scholar 

  6. Niu, F., Re, C., Doan, A., Shavlik, J.: Tuffy: Scaling up statistical inference in Markov logic networks using an RDBMS. In: International Conference on Very Large Data Bases, VLDB (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Microsoft Research, India

    Aditya V. Nori & Sriram K. Rajamani

Authors
  1. Aditya V. Nori
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sriram K. Rajamani
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computer Science Deptartement, Technion, 32000, Technion City, Haifa, Israel

    Eran Yahav

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nori, A.V., Rajamani, S.K. (2011). Program Analysis and Machine Learning: A Win-Win Deal. In: Yahav, E. (eds) Static Analysis. SAS 2011. Lecture Notes in Computer Science, vol 6887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23702-7_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23702-7_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23701-0

  • Online ISBN: 978-3-642-23702-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation