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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beckman, N., Nori, A.V.: Probabilistic, modular and scalable inference of typestate specifications. In: Programming Languages Design and Implementation (PLDI), pp. 211–221 (2011)
Chaganty, A., Lal, A., Nori, A.V., Rajamani, S.K.: Relational learning modulo axioms. Technical report, Microsoft Research (2011)
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
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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: Yang, H. (eds) Programming Languages and Systems. APLAS 2011. Lecture Notes in Computer Science, vol 7078. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25318-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-25318-8_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25317-1
Online ISBN: 978-3-642-25318-8
eBook Packages: Computer ScienceComputer Science (R0)