Mining Preconditions of APIs in Large-scale Code Corpus

By: Hoan Anh Nguyen, Robert Dyer, Tien N. Nguyen, and Hridesh Rajan

Abstract

Modern software relies on existing application programming in- terfaces (APIs) from libraries. Formal specifications for the APIs enable many software engineering tasks as well as help developers correctly use them. In this work, we mine large-scale repositories of existing open-source software to derive potential preconditions for API methods. Our key idea is that APIs’ preconditions would appear frequently in an ultra-large code corpus with a large num- ber of API usages, while project-specific conditions will occur less frequently. First, we find all client methods invoking APIs. We then compute a control dependence relation from each call site and mine the potential conditions used to reach those call sites. We use these guard conditions as a starting point to automatically infer the preconditions for each API. We analyzed almost 120 million lines of code from SourceForge and Apache projects to infer precondi- tions for the standard Java Development Kit (JDK) library. The results show that our technique can achieve high accuracy with recall from 75–80% and precision from 82–84%. We also found 5 preconditions missing from human written specifications. They were all confirmed by a specification expert. In a user study, par- ticipants found 82% of the mined preconditions as a good starting point for writing specifications. Using our mining result, we also built a benchmark of more than 4,000 precondition-related bugs.

ACM Reference

Nguyen, H.A. et al. 2014. Mining Preconditions of APIs in Large-scale Code Corpus. Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, (FSE-22), Hong Kong, China (2014), 166–177.

BibTeX Reference

@inproceedings{NguyenDyerNguyenRajan2014,
  author = {Hoan Anh Nguyen and Robert Dyer and Tien N. Nguyen and Hridesh Rajan},
  title = {Mining Preconditions of {API}s in Large-scale Code Corpus},
  booktitle = {Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, (FSE-22), Hong Kong, China},
  pages = {166--177},
  year = {2014},
  publisher = {{ACM}},
  editor = {Shing{-}Chi Cheung and Alessandro Orso and Margaret{-}Anne D. Storey},
  doi = {10.1145/2635868.2635924},
  abstract = {
  Modern software relies on existing application programming in- terfaces (APIs)
  from libraries. Formal specifications for the APIs enable many software
  engineering tasks as well as help developers correctly use them. In this work,
  we mine large-scale repositories of existing open-source software to derive
  potential preconditions for API methods. Our key idea is that APIs’
  preconditions would appear frequently in an ultra-large code corpus with a large
  num- ber of API usages, while project-specific conditions will occur less
  frequently. First, we find all client methods invoking APIs. We then compute a
  control dependence relation from each call site and mine the potential
  conditions used to reach those call sites. We use these guard conditions as a
  starting point to automatically infer the preconditions for each API. We
  analyzed almost 120 million lines of code from SourceForge and Apache projects
  to infer precondi- tions for the standard Java Development Kit (JDK) library.
  The results show that our technique can achieve high accuracy with recall from
  75–80% and precision from 82–84%. We also found 5 preconditions missing from
  human written specifications. They were all confirmed by a specification expert.
  In a user study, par- ticipants found 82% of the mined preconditions as a good
  starting point for writing specifications. Using our mining result, we also
  built a benchmark of more than 4,000 precondition-related bugs.},
}