Karl Meinke – författare

This book constitutes the proceedings of the 16th International Conference on Tests and Proofs, TAP 2022, which was held as part of Software Technologies: Applications and Foundations, STAF 2022, and took place in Nantes, France in July 2022.The 6 full papers together with 1 invited extended abstract included in this volume were carefully reviewed and selected from 11 submissions. They were organized in topical sections on formal analysis, and proofs, and effective testing.

This book constitutes the proceedings of the 16th International Conference on Tests and Proofs, TAP 2022, which was held as part of Software Technologies: Applications and Foundations, STAF 2022, and took place in Nantes, France in July 2022.

The 6 full papers together with 1 invited extended abstract included in this volume were carefully reviewed and selected from 11 submissions. They were organized in topical sections on formal analysis, and proofs, and effective testing.

Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities.  Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems.  These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Machine learning is a powerful paradigm that provides novel approaches to automating the generation of models and other essential software artifacts.  This volume originates from a Dagstuhl Seminar entitled "Machine Learning for Dynamic Software Analysis: Potentials and Limits” held in April 2016. The seminar focused on fostering a spirit of collaboration in order to share insights and to expand and strengthen the cross-fertilisation between the machine learning and software analysis communities.  The book provides an overview of the machine learning techniques that can be used for software analysis and presents example applications of their use. Besides an introductory chapter, the book is structured into three parts: testing and learning, extension of automata learning, and integrative approaches.

Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities.  Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems.  These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Machine learning is a powerful paradigm that provides novel approaches to automating the generation of models and other essential software artifacts.  This volume originates from a Dagstuhl Seminar entitled "Machine Learning for Dynamic Software Analysis: Potentials and Limits” held in April 2016. The seminar focused on fostering a spirit of collaboration in order to share insights and to expand and strengthen the cross-fertilisation between the machine learning and software analysis communities.  The book provides an overview of the machine learning techniques that can be used for software analysis and presents example applications of their use. Besides an introductory chapter, the book is structured into three parts: testing and learning, extension of automata learning, and integrative approaches.

This volume contains the final revised versions of the best papers presented at the First International Workshop on Higher-Order Algebra, Logic, and Term Rewriting (HOA '93), held in Amsterdam in September 1993. Higher-Order methods are increasingly applied in functional and logic programming languages, as well as in specification and verification of programs and hardware. The 15 full papers in this volume are devoted to the algebra and model theory of higher-order languages, computational logic techniques including resolution and term rewriting, and specification and verification case studies; in total they provide a competently written overview of current research and suggest new research directions in this vigourous area.

This volume contains the final versions of a collection of papers presented at the Annual Conference of the European Association for Computer Science Logic, CSL '93, held at Swansea, UK in September 1993.The 21 full papers included were selected from a total of 62 submissions and essentially contribute to the whole area of computer science logic research. They are devoted to such topics as set constraints, lambda calculi, process algebras, program semantics, intuitionistic logics, fixed-point logics, the equivalence problem, Horn clauses, quantifiers, and proof tranformations.

This book presents a collection of revised refereed papers selected from the presentations accepted for the Second International Workshop on Higher-Order Algebra, Logic, and Term Rewriting, HOA '95, held in Paderborn, Germany, in September 1995.The 14 research papers included, together with an invited paper by Jan Willem Klop, report state-of-the-art results; the relevant theoretical aspects are addressed, and in addition existing proof systems and term rewriting systems are discussed.

This book constitutes the refereed proceedings of the 6th International Conference on Algebraic and Logic Programming, ALP '97 and the 3rd International Workshop on Higher-Order Algebra, Logic and Term Rewriting, HOA '97, held jointly in Southampton, UK, in September 1997.The 18 revised full papers presented in the book were selected from 31 submissions. The volume is divided in sections on functional and logic programming, higher-order methods, term rewriting, types, lambda-calculus, and theorem proving methods.