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2015 | OriginalPaper | Chapter

Programming with “Big Code”

Author : Eran Yahav

Published in: Programming Languages and Systems

Publisher: Springer International Publishing

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Abstract

The vast amount of code available on the web is increasing on a daily basis. Open-source hosting sites such as GitHub contain billions of lines of code. Community question-answering sites provide millions of code snippets with corresponding text and metadata. The amount of code available in executable binaries is even greater. In this talk, I will cover recent research trends on leveraging such “big code” for program analysis, program synthesis and reverse engineering. We will consider a range of semantic representations based on symbolic automata [11, 15], tracelets [3], numerical abstractions [13, 14], and textual descriptions [1, 22], as well as different notions of code similarity based on these representations.
To leverage these semantic representations, we will consider a number of prediction techniques, including statistical language models [19, 20], variable order Markov models [2], and other distance-based and model-based sequence classification techniques.
Finally, I will show applications of these techniques including semantic code search in both source code and stripped binaries, code completion and reverse engineering.

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next chapter Memory-Efficient Tail Calls in the JVM with Imperative Functional Objects
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2.
Begleiter, R., El-Yaniv, R., Yona, G.: On prediction using variable order Markov models. J. Artif. Intell. Res. 22, 385–421 (2004)MathSciNetMATH
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David, Y., Yahav, E.: Tracelet-based code search in executables. In: Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI ’14, pp. 349–360 (2014)
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Peleg, H., Shoham, S., Yahav, E., Yang, H.: Symbolic automata for represnting big code. In: International journal on Software Tools for Technology Transfer, STTT’15 (2015)
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Raychev, V., Vechev, M., Krause, A.: Predicting program properties from “big code”. In: Proceedings of the 42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL ’15, pp. 111–124 (2015)
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Raychev, V., Vechev, M., Yahav, E.: Code completion with statistical language models. In: Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI’14, p. 44 (2014)
20.
Rosenfeld, R.: Two decades of statistical language modeling: where do we go from here? Proc. IEEE 88, 1270–1278 (2000)CrossRef
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Sinai, M.B., Yahav, E.: Code similarity via natural language descriptions. In: POPL Off the Beaten Track, OBT’15 (2014)
Metadata
Title
Programming with “Big Code”
Author
Eran Yahav
Copyright Year
2015
Book
Programming Languages and Systems

Print ISBN: 978-3-319-26528-5

Electronic ISBN: 978-3-319-26529-2

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26529-2_1

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