國立虎尾科技大學 |

Machine learning for dynamic software analysis = potentials and limits : International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016 : revised papers /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine learning for dynamic software analysis/ edited by Amel Bennaceur, Reiner Hahnle, Karl Meinke.
其他題名:	potentials and limits : International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016 : revised papers /
其他作者:	Bennaceur, Amel.
出版者:	Cham :Springer International Publishing : : 2018.,
面頁冊數:	ix, 257 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Machine learning - Congresses. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-96562-8
ISBN:	9783319965628

Machine learning for dynamic software analysis = potentials and limits : International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016 : revised papers /
Machine learning for dynamic software analysispotentials and limits : International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016 : revised papers /[electronic resource] :edited by Amel Bennaceur, Reiner Hahnle, Karl Meinke. - Cham :Springer International Publishing :2018. - ix, 257 p. :ill., digital ;24 cm. - Lecture notes in computer science,110260302-9743 ;. - Lecture notes in computer science ;6140..

Introduction -- Testing and Learning -- Extensions of Automata Learning -- 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.

ISBN: 9783319965628

Standard No.: 10.1007/978-3-319-96562-8doiSubjects--Topical Terms:

574552
Machine learning
--Congresses.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning for dynamic software analysis = potentials and limits : International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016 : revised papers /
LDR:02541nam a2200337 a 4500 001 927679
003 DE-He213
005 20180720151925.0
006 m d
007 cr nn 008maaau
008 190626s2018 gw s 0 eng d
020 $a 9783319965628 $q (electronic bk.)
020 $a 9783319965611 $q (paper)
024 7 $a 10.1007/978-3-319-96562-8 $2 doi
035 $a 978-3-319-96562-8
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UMZ $2 bicssc
072 7 $a UL $2 bicssc
072 7 $a COM051230 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .M149 2018
245 0 0 $a Machine learning for dynamic software analysis $h [electronic resource] : $b potentials and limits : International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016 : revised papers / $c edited by Amel Bennaceur, Reiner Hahnle, Karl Meinke.
260 $a Cham : $c 2018. $b Springer International Publishing : $b Imprint: Springer,
300 $a ix, 257 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 11026
505 0 $a Introduction -- Testing and Learning -- Extensions of Automata Learning -- Integrative Approaches.
520 $a 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.
650 0 $a Machine learning $v Congresses. $3 574552 $3 727808
650 1 4 $a Computer Science. $3 593922
650 2 4 $a Software Engineering/Programming and Operating Systems. $3 669780
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 593924
650 2 4 $a Theory of Computation. $3 669322
700 1 $a Bennaceur, Amel. $3 1207107
700 1 $a Hahnle, Reiner. $3 679916
700 1 $a Meinke, Karl. $3 1207108
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Lecture notes in computer science ; $v 6140. $3 744199
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-96562-8
950 $a Computer Science (Springer-11645)