Peter H. Schmitt – författare

Static analysis of software with deductive methods is a highly dynamic field of research on the verge of becoming a mainstream technology in software engineering. It consists of a large portfolio of - mostly fully automated - analyses: formal verification, test generation, security  analysis, visualization, and debugging. All of them are realized in the state-of-art deductive verification framework KeY.This book is the definitive guide to KeY that lets you explore the full potential of deductive software verification in practice. It contains the complete theory behind KeY for active researchers who want to understand it in depth or use it in their own work. But the book also features fully self-contained chapters on the Java Modeling Language and on Using KeY that require nothing else than familiarity with Java. All other chapters are accessible for graduate students (M.Sc. level and beyond). The KeY framework is free and open software, downloadable from the book companion website which contains also all code examples mentioned in this book.

This proceedings volume contains revised and reviewed papersbased on talks presented at the first International Workshopon Nonmonotonic and Inductive Logic held in Karlsruhe,December 1990. The workshop was supported by theVolkswagen-Stiftung, Hannover, and provided a forum forresearchers from the two fields to communicate and findareas of cooperation. The papersare organized into sectionson:- Nonmonotonicity in logic programs- Axiomatic approach to nonmonotonic reasoning- Inductive inference- Autoepistemic logic- Belief updatesThe bulk of the papers are devoted to nonmonotonic logic andprovide an up-to-date view of the current state of researchpresented by leading experts in the field. A novelty in thecontributions from the area of inductive logic is theanalysis of nonmonotonicity in the theory of inductivelearning.

Die Programmiersprache Prolog hat ihre Bewährungsprobe im praktischen Einsatz bestanden, und das logische Programmieren hat unter den Programmiertechniken seinen festen Platz gefunden. Worin liegen Attraktion und Nutzen des logischen Programmierens, was sind die charakteristischen Unterschiede zu anderen Programmiertechniken, und welche Rolle kommt der Theorie des logischen Programmierens zu? Dieses Buch macht den Leser mit den Grundlagen und Möglichkeiten der logischen Programmierung vertraut. Die ausführliche Darstellung mit Übungsaufgaben und ausgewählten Lösungen setzt keine speziellen Vorkenntnisse in formaler Logik voraus. In der Einleitung werden die wichtigsten Probleme und Resultate der Theorie anhand von Beispielen anschaulich vorgestellt. Es ist ein besonderes Anliegen des Buches, zu zeigen, wie höhere Konzepte der mathematischen Logik, z.B. vollständige Theorien, dreiwertige Logik oder saturierte Strukturen, in natürlicher und verständlicher Weise sinnvoll eingesetzt werden können. Auch dem neuen Thema der logischen Programmierung mit Randbedingungen (constraints) ist ein Kapitel gewidmet. In allen Teilen des Buches wird Wert gelegt auf klare Darstellung, mathematische Präzision und vollständige, nachvollziehbare Beweise. Auf übertriebene Systematik und Notation wird jedoch verzichtet, und Verallgemeinerungen werden erst dort eingeführt, wo sie gebraucht werden. Das Buch ist sowohl als vorbereitende und begleitende Lektüre zu Vorlesungen als auch zum Selbststudium geeignet.

This proceedings volume contains a selection of revised andextended papers presented at the Second InternationalWorkshop on Nonmonotonic and InductiveLogic, NIL '91, whichtook place at Reinhardsbrunn Castle, December 2-6, 1991.The volume opens with an extended version of a tutorial onnonmonotonic logic by G. Brewka, J. Dix, and K. Konolige.Fifteen selected papers follow, on a variety of topics. Themajority of papers belong either to the area of nonmonotonicreasoning or to the field of inductive inference, butsome papers integrate research from both areas.The first workshop in this series was held at the Universityof Karlsruhe in December 1990 and its proceedings werepublished as Lecture Notes in Artificial Intelligence Volume543. The series of workshops was made possible by financialsupport from Volkswagen Stiftung, Hannover. This workshopwas also supported by IBM Deutschland GmbH and Siemens AG.

Long gone are the days when program veri?cation was a task carried out merely by hand with paper and pen. For one, we are increasingly interested in proving actual program artifacts, not just abstractions thereof or core algorithms. The programs we want to verify today are thus longer, including whole classes and modules. As we consider larger programs, the number of cases to be considered in a proof increases. The creative and insightful parts of a proof can easily be lost in scores of mundane cases. Another problem with paper-and-pen proofs is that the features of the programming languages we employ in these programs are plentiful, including object-oriented organizations of data, facilities for specifying di?erent c- trol ?ow for rare situations, constructs for iterating over the elements of a collection, and the grouping together of operations into atomic transactions. These language features were designed to facilitate simpler and more natural encodings of programs, and ideally they are accompanied by simpler proof rules. But the variety and increased number of these features make it harder to remember all that needs to be proved about their uses. As a third problem, we have come to expect a higher degree of rigor from our proofs. A proof carried out or replayed by a machine somehow gets more credibility than one that requires human intellect to understand.

Long gone are the days when program veri?cation was a task carried out merely by hand with paper and pen. For one, we are increasingly interested in proving actual program artifacts, not just abstractions thereof or core algorithms. The programs we want to verify today are thus longer, including whole classes and modules. As we consider larger programs, the number of cases to be considered in a proof increases. The creative and insightful parts of a proof can easily be lost in scores of mundane cases. Another problem with paper-and-pen proofs is that the features of the programming languages we employ in these programs are plentiful, including object-oriented organizations of data, facilities for specifying di?erent c- trol ?ow for rare situations, constructs for iterating over the elements of a collection, and the grouping together of operations into atomic transactions. These language features were designed to facilitate simpler and more natural encodings of programs, and ideally they are accompanied by simpler proof rules. But the variety and increased number of these features make it harder to remember all that needs to be proved about their uses. As a third problem, we have come to expect a higher degree of rigor from our proofs. A proof carried out or replayed by a machine somehow gets more credibility than one that requires human intellect to understand.