%0 Conference Proceedings %T Gamifying program analysis %+ University of California [Santa Cruz] (UC Santa Cruz) %+ Département Ingénierie Logiciels et Systèmes (DILS) %+ SRI International [Menlo Park] (SRI) %A Fava, D. %A Signoles, J. %A Lemerre, M. %A Schäf, M. %A Tiwari, A. %Z Conference of 20th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning, LPAR 2015 ; Conference Date: 24 November 2015 Through 28 November 2015; Conference Code:158539 %< avec comité de lecture %B Logic for Programming, Artificial Intelligence, and Reasoning. LPAR 2015. Lecture Notes in Computer Science %C Suva, Fiji %Y Voronkov A. %Y Fehnker A. %Y Davis M. %Y McIver A. %I Springer Verlag %V 9450 %P 591-605 %8 2015-11-24 %D 2015 %R 10.1007/978-3-662-48899-7_41 %K Abstracting %K Accident prevention %K Artificial intelligence %K Computer circuits %K Learning systems %K Reconfigurable hardware %K Abstract interpretations %K Industrial scale %K Likely invariants %K Number of false alarms %K Program analysis %K Program Verification %K Symbolic execution %K Verification engineers %K Model checking %Z Computer Science [cs]Conference papers %X Abstract interpretation is a powerful tool in program verification. Several commercial or industrial scale implementations of abstract interpretation have demonstrated that this approach can verify safety properties of real-world code. However, using abstract interpretation tools is not always simple. If no user-provided hints are available, the abstract interpretation engine may lose precision during widening and produce an overwhelming number of false alarms. However, manually providing these hints is time consuming and often frustrating when re-running the analysis takes a lot of time. We present an algorithm for program verification that combines abstract interpretation, symbolic execution and crowdsourcing. If verification fails, our procedure suggests likely invariants, or program patches, that provide helpful information to the verification engineer and makes it easier to find the correct specification. By complementing machine learning with well-designed games, we enable program analysis to incorporate human insights that help improve their scalability and usability. %G English %L cea-01834979 %U https://cea.hal.science/cea-01834979 %~ CEA %~ DRT %~ CEA-UPSAY %~ UNIV-PARIS-SACLAY %~ CEA-UPSAY-SACLAY %~ LIST %~ GS-COMPUTER-SCIENCE