The language mutation problem: Leveraging language product lines for mutation testing of interpreters

Compilers translate programs from a high level of abstraction into a low level representation that can be understood and executed by the computer; interpreters directly execute instructions from source code to convey their semantics. Undoubtedly, the correctness of both compilers and interpreters is fundamental to reliably execute the semantics of any software developed by means of high-level languages. Testing is one of the most important methods to detect errors in any software, including compilers and interpreters. Among testing methods, mutation testing is an empirically effective technique often used to evaluate and improve the quality of test suites. However, mutation testing imposes severe demands in computing resources due to the large number of mutants that need to be generated, compiled and executed. In this work, we introduce a mutation approach for programming languages that mitigates this problem by leveraging the properties of language product lines, language workbenches and separate compilations. In this approach, the base language is taken as a black-box and mutated by means of mutation operators performed at language feature level to create a family of mutants of the base language. Each variant of the mutant family is created at runtime, without any access to the source code and without performing any additional compilation. We report results from a preliminary case study in which mutants of an ECMAScript interpreter are tested against the Sputnik conformance test suite for the ECMA-262 specification. The experimental data indicates that this approach can be used to create generally non-trivial mutants.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

The correctness of compilers and interpreters is crucial for the reliable execution of software. Testing, including mutation testing, is an important method to detect errors and improve test suite quality. This work presents a mutation approach for programming languages that mitigates computing resource demands.