Michael Hanus

Karl Meinke

Jan Heering

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.

6th International Joint Conference, ALP '97 - HOA '97, Southhampton, UK, September 3-5, 1997. Proceedings

Algebraic and Logic Programming

Egon Börger

Yuri Gurevich

This volume presents the proceedings of the Fourth International Workshop on Analogical and Inductive Inference (AII '94) and the Fifth International Workshop on Algorithmic Learning Theory (ALT '94), held jointly at Reinhardsbrunn Castle, Germany in October 1994. (In future the AII and ALT workshops will be amalgamated and held under the single title of Algorithmic Learning Theory.) The book contains revised versions of 45 papers on all current aspects of computational learning theory; in particular, algorithmic learning, machine learning, analogical inference, inductive logic, case-based reasoning, and formal language learning are addressed.

7th Workshop, CSL '93, Swansea, United Kingdom, September 13 - 17, 1993. Selected Papers

Computer Science Logic

Reiner Hähnle

Amel Bennaceur

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.

International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers

Machine Learning for Dynamic Software Analysis: Potentials and Limits

Karl Meinke

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