Mining Software Specifications -

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Editeur : Crc Press

Langue : Anglais

Parution : 01/05/2011

Format : Moyen, de 350g à 1kg

Nombre de pages : 458

Expédition : 793

Dimensions : 23.5 x 15.6 x 28.0

ISBN : 9781439806265

Résumé :

Exploring existing and emerging work in the field, this volume shows how specification mining techniques can help find software bugs and improve program understanding. Top researchers in the software engineering community provide valuable insight on up-to-date case studies of various software systems, including open source programs and those used by Microsoft Research and IBM Research. The book focuses on mining both finite state machines and temporal rules/patterns of behavior. It presents approaches that use static analysis, dynamic analysis, and combinations of the two.

Biographie:

David Lo is an assistant professor in the School of Information Systems at Singapore Management University. His research interests include specification mining, dynamic program analysis, automated debugging, code search, and pattern mining.

Siau-Cheng Khoo is an associate professor in the Department of Computer Science at the National University of Singapore. His research interests include specification mining, program analysis, program transformation, functional programming, domain-specific languages, and aspect-oriented programming.

Jiawei Han is a professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. He is editor-in-chief of the ACM Transactions on Knowledge Discovery from Data and co-editor of Geographic Data Mining and Knowledge Discovery, Second Edition (CRC Press, 2009) and Next Generation of Data Mining (CRC Press, 2009). His research interests include information network analysis, knowledge discovery, pattern discovery, data streams, and multidimensional analysis.

Chao Liu is a researcher in the Internet Service Research Center at Microsoft Research. His research interests include data mining for software engineering, statistical debugging, and machine learning and its use in web applications.

Sommaire:

Specification Mining: A Concise Introduction. Mining Finite-State Automata with Annotations. Adapting Grammar Inference Techniques to Mine State Machines. Mining API Usage Protocols from Large Method Traces. Static API Specification Mining: Exploiting Source Code Model Checking. Static Specification Mining Using Automata-Based Abstractions. DynaMine: Finding Usage Patterns and Their Violations by Mining Software Repositories. Automatic Inference and Effective Application of Temporal Specifications. Path-Aware Static Program Analyses for Specification Mining. Mining API Usage Specifications via Searching Source Code from the Web. Merlin: Specification Inference for Explicit Information Flow Problems. Lightweight Mining of Object Usage.