Regression test selection (RTS) approaches reduce the number of regression tests. Current RTS approaches are typically monoglot, i.e., their implementations target a specific language. However, many subjects under test (SUT) are polyglot, i.e., they use multiple languages. Running multiple monoglot RTS approaches separately on a polyglot SUT is unsafe because tests that involve inter-language dependencies can be missed. Moreover, a new language may require completely reimplementing an RTS approach, especially if the original implementation relies on language and runtime features that are not available in the new language. We propose a new static approach called BabelRTS, which is multilingual (supports multiple languages out of the box), polyglot (analyzes SUTs written in multiple languages), and extensible (allows adding support for new languages). A key contribution is the idea of encapsulating the language-specific aspects of RTS by using patterns and actions. A pattern specifies programming language constructs used in each file that indicate dependencies to other files written in the same or a different language. An action specifies how to identify these files in the codebase. Patterns and actions can be customized to support new languages without modifying the test selection algorithm. BabelRTS is not tied to a specific language run-time system or paradigm. BabelRTS currently supports 12 languages and 5 language combinations. We evaluated BabelRTS on 142 open-source monoglot and polyglot SUTs, analyzing a total of more than two billion LOC. The performance of BabelRTS was similar to the state-of-the-art monoglot approaches on monoglot SUTs. On polyglot SUTs, BabelRTS was safer in polyglot mode and selected more tests for 60% of the commits than in monoglot mode, which missed inter-language dependencies.
Software engineeringSoftware testingComputer languages
Gabriele Maurina、Walter Cazzola、Sudipto Ghosh
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Department of Computer Science, Colorado State University, Fort Collins, CO, USA
Department of Computer Science, Università degli Studi di Milano, Milan, Italy
NETL
NSTL
IEEE
