M3: Semantic API Migrations

Bruce Collie, Philip Ginsbach, Jackson Woodruff, Ajitha Rajan, Michael F P O'Boyle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Library migration is a challenging problem, where most existing approaches rely on prior knowledge. This can be, for example, information derived from changelogs or statistical models of API usage.

This paper addresses a different API migration scenario where there is no prior knowledge of the target library.We have no historical changelogs and no access to its internal representation. To tackle this problem, this paper proposes a novel approach (M3), where probabilistic program synthesis is used to semantically model the behavior of library functions. Then, we use an SMT-based code search engine to discover similar code in user applications. These discovered instances provide potential locations for API migrations.

We evaluate our approach against 7 well-known libraries from varied application domains, learning correct implementations for 94 functions. Our approach is integrated with standard compiler tooling, and we use this integration to evaluate migration opportunities in 9 existing C/C++ applications with over 1MLoC. We discover over 7,000 instances of these functions, of which more than 2,000 represent migration opportunities.
Original languageEnglish
Title of host publicationProceedings of 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) 2020
PublisherACM Association for Computing Machinery
Pages90 - 102
Number of pages13
ISBN (Print)9781450367684
Publication statusPublished - 21 Dec 2020
Event35th IEEE/ACM International Conference on Automated Software Engineering - Melbourne, Australia
Duration: 21 Sept 202025 Sept 2020


Conference35th IEEE/ACM International Conference on Automated Software Engineering
Abbreviated titleASE 2020
Internet address


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