An extensible, regular-expression-based tool for multi-language mutant generation.
Saved in:
| Title: | An extensible, regular-expression-based tool for multi-language mutant generation. |
|---|---|
| Authors: | Groce, Alex, Holmes, Josie, Marinov, Darko, Shi, August, Zhang, Lingming |
| Source: | ICSE: International Conference on Software Engineering. 5/27/2018, p25-28. 4p. |
| Subjects: | Mutation testing of computer software, Programming languages, Computer programmers, Computer programming, Formal languages |
| Abstract: | Mutation testing is widely used in research (even if not in practice). Mutation testing tools usually target only one programming language and rely on parsing a program to generate mutants, or operate not at the source level but on compiled bytecode. Unfortunately, developing a robust mutation testing tool for a new language in this paradigm is a difficult and time-consuming undertaking. Moreover, bytecode/intermediate language mutants are difficult for programmers to read and understand. This paper presents a simple tool, called universalmutator, based on regular-expression-defined transformations of source code. The primary drawback of such an approach is that our tool can generate invalid mutants that do not compile, and sometimes fails to generate mutants that a parser-based tool would have produced. Additionally, it is incompatible with some approaches to improving the efficiency of mutation testing. However, the regexp-based approach provides multiple compensating advantages. First, our tool is easy to adapt to new languages; e.g., we present here the first mutation tool for Apple's Swift programming language. Second, the method makes handling multi-language programs and systems simple, because the same tool can support every language. Finally, our approach makes it easy for users to add custom, project-specific mutations. [ABSTRACT FROM AUTHOR] |
| Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
