- author = "Facundo Molina and Pablo Ponzio and Nazareno Aguirre and Marcelo Frias",
- title = "{EvoSpex}: An Evolutionary Algorithm for Learning Postconditions (artifact)",
- booktitle = "IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)",
- year = "2021",
- editor = "Silvia Abrahao and Daniel Mendez",
- pages = "186--186",
- month = "25-28 " # may,
- keywords = "genetic algorithms, genetic programming, Genetic Improvement",
- isbn13 = "978-1-6654-1219-3/21/",
-
code_url = "
http://doi.org/10.5281/zenodo.4458256",
-
code_url = "https://github.com/facumolina/evospex-ae",
-
DOI = "
doi:10.1109/ICSE-Companion52605.2021.00080",
- size = "2 pages",
- abstract = "Having the expected behaviour of software specified in a formal language can greatly improve the automation of software verification activities, since these need to contrast the intended behavior with the actual software implementation. Unfortunately, software many times lacks such specifications, and thus providing tools and techniques that can assist developers in the construction of software specifications are relevant in software engineering. As an aid in this context, we present EvoSpex, a tool that given a Java method, automatically produces a specification of the method current behaviour, in the form of post condition assertions. EvoSpex is based on generating software runs from the implementation (valid runs), making modifications to the runs to build divergent behaviors (invalid runs), and executing a genetic algorithm that tries to evolve a specification to satisfy the valid runs, and leave out the invalid ones. Our tool supports a rich JML-like assertion language, that can capture complex specifications, including sophisticated object structural properties.",
-
notes = "is this GP?
Department of Computer Science, FCEFQyN, University of Rio Cuarto, Argentina",
