Enabling Efficient Assertion Inference

Assertion inference techniques aim at automatically inferring sets of program assertions that capture the exhibited software behavior, often by generating and filtering assertions through dynamic test executions and mutation testing. Although powerful, such techniques are computationally expensive d...

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Bibliographic Details
Published inProceedings - International Symposium on Software Reliability Engineering pp. 623 - 634
Main Authors Garg, Aayush, Degiovanni, Renzo, Molina, Facundo, Cordy, Maxime, Aguirre, Nazareno, Papadakis, Mike, Le Traon, Yves
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.10.2023
Subjects
assertion inference
Behavioral sciences
Computer architecture
Costs
encoder decoder
Filtering
Learning systems
mutant prediction
mutation testing
Software
Software reliability
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Summary:Assertion inference techniques aim at automatically inferring sets of program assertions that capture the exhibited software behavior, often by generating and filtering assertions through dynamic test executions and mutation testing. Although powerful, such techniques are computationally expensive due to the large number of mutants that require execution. In this study, we introduce the notion of Assertion Inferring Mutants, and demonstrate that these mutants are sufficient for assertion inference and correspond to a small subset (12.95%) of the entire mutant set. Moreover, these mutants are significantly different (71.59%) from Subsuming Mutants that are frequently cited by mutation testing literature. We also show that Assertion Inferring Mutants can be statically approximated via a learning-based method. Given the widespread adoption of encoder-decoder architecture for prediction tasks, we demonstrate that it predicts Assertion Inferring Mutants with 0.79 Precision and 0.49 Recall. Its evaluation on 46 projects showcases that it enables a comparable inference capability (missing only 12.49% assertions) with a complete mutation analysis, while significantly reducing the execution cost (achieving 46.29 times faster inference). Moreover, it enables assertion inference techniques to scale on subjects where complete mutation testing is prohibitively expensive and other mutant selection strategies do not lead to an acceptable assertion inference.
ISSN:2332-6549
DOI:10.1109/ISSRE59848.2023.00039