- author = "Maria Kechagia and Sergey Mechtaev and Federica Sarro and Mark Harman",
- title = "Evaluating Automatic Program Repair Capabilities to Repair {API} Misuses",
- journal = "IEEE Transactions on Software Engineering",
- year = "2022",
- volume = "48",
- number = "7",
- pages = "2658--2679",
- keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, APR, Automatic Program Repair, Application Programming Interfaces, API Misuses, Bug Benchmarks",
- ISSN = "0098-5589",
-
URL = "
http://www.cs.ucl.ac.uk/staff/F.Sarro/resource/papers/TSE2021_API_repair.pdf",
-
URL = "
https://ieeexplore.ieee.org/document/9381596",
-
URL = "https://mkechagia.github.io/",
-
DOI = "
doi:10.1109/TSE.2021.3067156",
- size = "22 pages",
-
abstract = "API misuses are well-known causes of software crashes
and security vulnerabilities. However, their detection
and repair is challenging given that the correct usages
of (third-party) APIs might be obscure to the
developers of client programs.This paper presents the
first empirical study to assess the ability of existing
automated bug repair tools to repair API misuses, which
is a class of bugs previously unexplored. Our study
examines and compares 14 Java test-suite-based repair
tools (11 proposed before 2018, and three afterwards)
on a manually curated benchmark (APIREPBENCH)
consisting of 101 API misuses. We develop an extensible
execution framework (APIARTY) to automatically execute
multiple repair tools.
Our results show that the repair tools are able to generate patches for 28percent of the API misuses considered. While the 11 less recent tools are generally fast (the median execution time of the repair attempts is 3.87 minutes and the mean execution time is 30.79 minutes), the three most recent are less efficient (i.e., 98percent slower) than their predecessors. The tools generate patches for API misuses that mostly belong to the categories of missing null check, missing value, missing exception, and missing call. Most of the patches generated by all tools are plausible (65percent), but only few of these patches are semantically correct to human patches (25percent).
Our findings suggest that the design of future repair tools should support the localisation of complex bugs, including different categories of API misuses, handling of timeout issues, and ability to configure large software projects. Both APIREPBENCH and APIARTY have been made publicly available for other researchers to evaluate the capabilities of repair tools on detecting and fixing API misuses.",
-
notes = "Computer Science, University College London, 4919
London, London, United Kingdom of Great Britain and
Northern Ireland
Many of the tools identified are based on GP.
also known as \cite{9381596}",
