Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Automated and Algorithmic Debugging PDF full book. Access full book title Automated and Algorithmic Debugging by Peter A. Fritzson. Download full books in PDF and EPUB format.
Author: Peter A. Fritzson Publisher: Springer Science & Business Media ISBN: 9783540574170 Category : Computers Languages : en Pages : 392
Book Description
Debugging has always been a costly part of software development, and many attempts have been made to provide automatic computer support for this task.Automated debugging has seen major develoments over the last decade. Onesuccessful development is algorithmic debugging, which originated in logic programming but was later generalized to concurrent, imperative, and lazy functional languages. Important advances have also been made in knowledge-based program debugging, and in approaches to automated debugging based on static and dynamic program slicing based on dataflow and dependence analysis technology. This is the first collected volume of papers on automated debugging and presents latest developments, tutorial papers, and surveys.
Author: Peter A. Fritzson Publisher: Springer Science & Business Media ISBN: 9783540574170 Category : Computers Languages : en Pages : 392
Book Description
Debugging has always been a costly part of software development, and many attempts have been made to provide automatic computer support for this task.Automated debugging has seen major develoments over the last decade. Onesuccessful development is algorithmic debugging, which originated in logic programming but was later generalized to concurrent, imperative, and lazy functional languages. Important advances have also been made in knowledge-based program debugging, and in approaches to automated debugging based on static and dynamic program slicing based on dataflow and dependence analysis technology. This is the first collected volume of papers on automated debugging and presents latest developments, tutorial papers, and surveys.
Author: Ali Arya Publisher: Ali Arya ISBN: Category : Computers Languages : en Pages : 288
Book Description
As the second book in the Anyone Can Code series, Algorithmic Thinking focuses on the logic behind computer programming and software design. With a data-centred approach, it starts with simple algorithms that work on simple data items and advances to more complex ones covering data structures and classes. Examples are given in C/C++ and Python and use both plain text and graphics applications to illustrate the concepts in different languages and forms. With the advances in artificial intelligence and automated code generators, it is essential to learn about the logic of what a code needs to do, not just how to write the code. Anyone Can Code: Algorithmic Thinking is suitable for anyone who aims to improve their programming skills and go beyond the simple craft of programming, stepping into the world of algorithm design.
Author: Ehud Y. Shapiro Publisher: MIT Press (MA) ISBN: 9780262693073 Category : Computers Languages : en Pages : 231
Book Description
Shapiro productively combines elements of programming languages, environments, logic, and inductive inference to produce effective debugging aids. The author's use of the PROLOG language provides an efficient implementation of the debugging algorithms.
Author: George Pruitt Publisher: John Wiley & Sons ISBN: 1119262976 Category : Business & Economics Languages : en Pages : 363
Book Description
The accessible, beneficial guide to developing algorithmic trading solutions The Ultimate Algorithmic Trading System Toolbox is the complete package savvy investors have been looking for. An integration of explanation and tutorial, this guide takes you from utter novice to out-the-door trading solution as you learn the tools and techniques of the trade. You'll explore the broad spectrum of today's technological offerings, and use several to develop trading ideas using the provided source code and the author's own library, and get practical advice on popular software packages including TradeStation, TradersStudio, MultiCharts, Excel, and more. You'll stop making repetitive mistakes as you learn to recognize which paths you should not go down, and you'll discover that you don't need to be a programmer to take advantage of the latest technology. The companion website provides up-to-date TradeStation code, Excel spreadsheets, and instructional video, and gives you access to the author himself to help you interpret and implement the included algorithms. Algorithmic system trading isn't really all that new, but the technology that lets you program, evaluate, and implement trading ideas is rapidly evolving. This book helps you take advantage of these new capabilities to develop the trading solution you've been looking for. Exploit trading technology without a computer science degree Evaluate different trading systems' strengths and weaknesses Stop making the same trading mistakes over and over again Develop a complete trading solution using provided source code and libraries New technology has enabled the average trader to easily implement their ideas at very low cost, breathing new life into systems that were once not viable. If you're ready to take advantage of the new trading environment but don't know where to start, The Ultimate Algorithmic Trading System Toolbox will help you get on board quickly and easily.
Author: Oswaal Editorial Board Publisher: Oswaal Books ISBN: 9357288589 Category : Study Aids Languages : en Pages : 107
Book Description
Description of the product: •Fresh & Relevant with Latest Typologies of the Questions •Score Boosting Insights with 500+ Questions & 1000 Concepts •Insider Tips & Techniques with On-Tips Notes, Mind Maps & Mnemonics •Exam Ready Practice with 10 Highly Probable SQPs
Author: Marco Brambilla Publisher: Springer Nature ISBN: 3030742962 Category : Computers Languages : en Pages : 561
Book Description
This book constitutes the proceedings of the 21st International Conference on Web Engineering, ICWE 2021, which was supposed to be held in Biarritz, France, in May 2021. Due to the corona pandemic the conference changed to a virtual format. The total of 22 full and 13 short contributions presented in this volume were carefully reviewed and selected from 128 submissions. The book also contains 6 demonstration, 1 poster, 3 PhD, and 3 tutorial papers. The papers were organized in topical sections named: Semantic Web; social Web; Web modeling and engineering; Web big data and data analytics; Web mining and knowledge extraction; Web of Things; Web programming; Web user interfaces; PhD symposium; posters and demonstrations; and tutorials. Chapter “A Web-Based Co-Creation and User Engagement Method and Platform” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Author: Shai Ben David Publisher: Springer Science & Business Media ISBN: 3540233563 Category : Computers Languages : en Pages : 519
Book Description
Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical ?elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the ?eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re?ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of activities that can be viewed as learning. A ?rst dichotomy is between viewing learning as an inde?nite process and viewing it as a ?nite activity with a de?ned termination. Inductive Inference models focus on inde?nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion.
Author: Peter A. Fritzson
Author: Peter A. Fritzson
Author: Ali Arya
Author: 
Author: Conference on Software Maintenance
Author: Ehud Y. Shapiro
Author: George Pruitt
Author: Oswaal Editorial Board
Author: Marco Brambilla
Author: 
Author: Shai Ben David