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Different definitions of vacuity in temporal logic model checking have been suggested along the years. Examining them closely, however, reveals an interesting phenomenon. On the one hand, some of the definitions require high-complexity vacuity detection algorithms. On the other hand, studies in the literature report that not all vacuities detected in practical applications are considered a problem by the system verifier. This brings vacuity detection into an undesirable situation where the user of the model checking tool may find herself waiting a long time for results that are of no interest for her. In this paper we restrict our attention to practical usage of vacuity detection. We define Temporal Antecedent Failure, an extension of antecedent failure to temporal logic, which refines the notion of vacuity. According to our experience, this type of vacuity always indicates a problem in the model, environment or property. We show how vacuity information can be derived from the automaton built for the original property, and we introduce the notion of vacuity explanation. Our experiments demonstrate that this type of vacuity as well as its reasons can be computed with a negligible increase in the overall runtime.Shoham Ben-David is grateful to the Azrieli Foundation for the award of an Azrieli Fellowship.

This paper describes methods and techniques used to verify the POWER8i microprocessor. The base concepts for the functional verification are those that have been already used in POWER7 A processor verification. However, the POWER8 design point provided multiple new challenges that required innovative solutions. With approximately three times the number of transistors available, compared to the POWER7 processor chip, functionality was added by putting additional enhanced cores on-chip and by developing new features that intrinsically require more software interaction. The examples given in this paper demonstrate how new tools and the continuous improvement of existing methods addressed these verification challenges.

Many applications have security vulnerabilities that can be exploited. It is practically impossible to find all of them due to the NP-complete nature of the testing problem. Security solutions provide defenses against these attacks through continuous application testing, fast-patching of vulnerabilities, automatic deployment of patches, and virtual patching detection techniques deployed in network and endpoint security tools. These techniques are limited by the need to find vulnerabilities before the 'black hats'. We propose an innovative technique to virtually patch vulnerabilities before they are found. We leverage testing techniques for supervised-learning data generation, and show how artificial intelligence techniques can use this data to create predictive deep neural-network models that read an application's input and predict in real time whether it is a potential malicious input. We set up an ahead-of-threat experiment in which we generated data on old versions of an application, and then evaluated the predictive model accuracy on vulnerabilities found years later. Our experiments show ahead-of-threat detection on LibXML2 and LibTIFF vulnerabilities with 91.3% and 93.7% accuracy, respectively. We expect to continue work on this field of research and provide ahead-of-threat virtual patching for more libraries. Success in this research can change the current state of endless racing after application vulnerabilities and put the defenders one step ahead of the attackers.

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