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Combining Tests and Proofs for Better Software Verification
Li Huang, Bertrand Meyer, Manuel Oriol
Test or prove? These two approaches to software verification have long been presented as opposites. One is dynamic, the other static: a test executes the program, a proof only anal…
Do AI models help produce verified bug fixes?
Li Huang, Ilgiz Mustafin, Marco Piccioni +3
Among areas of software engineering where AI techniques -- particularly, Large Language Models -- seem poised to yield dramatic improvements, an attractive candidate is Automatic P…
Loop unrolling: formal definition and application to testing
Li Huang, Bertrand Meyer, Reto Weber
Testing processes usually aim at high coverage, but loops severely limit coverage ambitions since the number of iterations is generally not predictable. Most testing teams address…
Execution-free Program Repair
Li Huang, Bertrand Meyer, Ilgiz Mustafin +1
Automatic program repair usually relies heavily on test cases for both bug identification and fix validation. The issue is that writing test cases is tedious, running them takes mu…
BUGFIX: towards a common language and framework for the AutomaticProgram Repair community
Bertrand Meyer, Viktoryia Kananchuk, Li Huang
Techniques of Automatic Program Repair (APR) have the potential of thoroughly facilitating the task of producing quality software. After a promising start, however, progress in mak…
Seeding Contradiction: a fast method for generating full-coverage test suites
Li Huang, Bertrand Meyer, Manuel Oriol
The regression test suite, a key resource for managing program evolution, needs to achieve 100% coverage, or very close, to be useful. Devising a test suite manually is unacceptabl…
A Failed Proof Can Yield a Useful Test
Li Huang, Bertrand Meyer
A successful automated program proof is, in software verification, the ultimate triumph. In practice, however, the road to such success is paved with many failed proof attempts. Un…