東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Software Management." ]

切換: 標籤 | MARC模式 | ISBD

Intelligent crowdsourced testing

Wang, Qing.

FindBook

Google Book

Amazon

博客來

Intelligent crowdsourced testing

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Intelligent crowdsourced testing/ by Qing Wang ... [et al.].
其他作者:	Wang, Qing.
出版者:	Singapore :Springer Nature Singapore : : 2022.,
面頁冊數:	xiv, 251 p. :ill., digital ;24 cm.
內容註:	Part I Preliminary of Crowdsourced Testing -- 1 Introduction -- 2 Preliminaries -- 3 Book Structure -- Part II Supporting Technology for Crowdsourced Testing Workers -- 4 Characterization of Crowd Worker -- 5 Task Recommendation for Crowd Worker -- Part III Supporting Technology for Crowdsourced Testing Tasks -- 6 Crowd Worker Recommendation for Testing Task -- 7 Crowdsourced Testing Task Management -- Part IV Supporting Technology for Crowdsourced Testing Results -- 8 Classification of Crowdsourced Testing Reports -- 9 Duplicate Detection of Crowdsourced Testing Reports -- 10 Prioritization of Crowdsourced Testing Reports -- 11 Summarization of Crowdsourced Testing Reports -- 12 Quality Assessment of Crowdsourced Testing Cases -- Part V Conclusions and Future Perspectives -- 13 Conclusions -- 14 Perspectives.
Contained By:	Springer Nature eBook
標題:	Computer software - Testing. -
電子資源:	https://doi.org/10.1007/978-981-16-9643-5
ISBN:	9789811696435

Intelligent crowdsourced testing
Intelligent crowdsourced testing[electronic resource] /by Qing Wang ... [et al.]. - Singapore :Springer Nature Singapore :2022. - xiv, 251 p. :ill., digital ;24 cm.

Part I Preliminary of Crowdsourced Testing -- 1 Introduction -- 2 Preliminaries -- 3 Book Structure -- Part II Supporting Technology for Crowdsourced Testing Workers -- 4 Characterization of Crowd Worker -- 5 Task Recommendation for Crowd Worker -- Part III Supporting Technology for Crowdsourced Testing Tasks -- 6 Crowd Worker Recommendation for Testing Task -- 7 Crowdsourced Testing Task Management -- Part IV Supporting Technology for Crowdsourced Testing Results -- 8 Classification of Crowdsourced Testing Reports -- 9 Duplicate Detection of Crowdsourced Testing Reports -- 10 Prioritization of Crowdsourced Testing Reports -- 11 Summarization of Crowdsourced Testing Reports -- 12 Quality Assessment of Crowdsourced Testing Cases -- Part V Conclusions and Future Perspectives -- 13 Conclusions -- 14 Perspectives.

In an article for Wired Magazine in 2006, Jeff Howe defined crowdsourcing as an idea for outsourcing a task that is traditionally performed by a single employee to a large group of people in the form of an open call. Since then, by modifying crowdsourcing into different forms, some of the most successful new companies on the market have used this idea to make people's lives easier and better. On the other hand, software testing has long been recognized as a time-consuming and expensive activity. Mobile application testing is especially difficult, largely due to compatibility issues: a mobile application must work on devices with different operating systems (e.g. iOS, Android), manufacturers (e.g. Huawei, Samsung) and keypad types (e.g. virtual keypad, hard keypad) One cannot be 100% sure that, just because a tested application works well on one device, it will run smoothly on all others. Crowdsourced testing is an emerging paradigm that can improve the cost-effectiveness of software testing and accelerate the process, especially for mobile applications. It entrusts testing tasks to online crowdworkers whose diverse testing devices/contexts, experience, and skill sets can significantly contribute to more reliable, cost-effective and efficient testing results. It has already been adopted by many software organizations, including Google, Facebook, Amazon and Microsoft. This book provides an intelligent overview of crowdsourced testing research and practice. It employs machine learning, data mining, and deep learning techniques to process the data generated during the crowdsourced testing process, to facilitate the management of crowdsourced testing, and to improve the quality of crowdsourced testing.

ISBN: 9789811696435

Standard No.: 10.1007/978-981-16-9643-5doiSubjects--Topical Terms:

570748
Computer software
--Testing.

LC Class. No.: QA76.76.T48

Dewey Class. No.: 005.14

Intelligent crowdsourced testing
LDR:03556nmm a2200337 a 4500 001 2301810
003 DE-He213
005 20220616151016.0
006 m d
007 cr nn 008maaau
008 230409s2022 si s 0 eng d
020 $a 9789811696435 $q (electronic bk.)
020 $a 9789811696428 $q (paper)
024 7 $a 10.1007/978-981-16-9643-5 $2 doi
035 $a 978-981-16-9643-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.76.T48
072 7 $a UMZT $2 bicssc
072 7 $a COM051230 $2 bisacsh
072 7 $a UMZT $2 thema
082 0 4 $a 005.14 $2 23
090 $a QA76.76.T48 $b I61 2022
245 0 0 $a Intelligent crowdsourced testing $h [electronic resource] / $c by Qing Wang ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a xiv, 251 p. : $b ill., digital ; $c 24 cm.
338 $a online resource $b cr $2 rdacarrier
505 0 $a Part I Preliminary of Crowdsourced Testing -- 1 Introduction -- 2 Preliminaries -- 3 Book Structure -- Part II Supporting Technology for Crowdsourced Testing Workers -- 4 Characterization of Crowd Worker -- 5 Task Recommendation for Crowd Worker -- Part III Supporting Technology for Crowdsourced Testing Tasks -- 6 Crowd Worker Recommendation for Testing Task -- 7 Crowdsourced Testing Task Management -- Part IV Supporting Technology for Crowdsourced Testing Results -- 8 Classification of Crowdsourced Testing Reports -- 9 Duplicate Detection of Crowdsourced Testing Reports -- 10 Prioritization of Crowdsourced Testing Reports -- 11 Summarization of Crowdsourced Testing Reports -- 12 Quality Assessment of Crowdsourced Testing Cases -- Part V Conclusions and Future Perspectives -- 13 Conclusions -- 14 Perspectives.
520 $a In an article for Wired Magazine in 2006, Jeff Howe defined crowdsourcing as an idea for outsourcing a task that is traditionally performed by a single employee to a large group of people in the form of an open call. Since then, by modifying crowdsourcing into different forms, some of the most successful new companies on the market have used this idea to make people's lives easier and better. On the other hand, software testing has long been recognized as a time-consuming and expensive activity. Mobile application testing is especially difficult, largely due to compatibility issues: a mobile application must work on devices with different operating systems (e.g. iOS, Android), manufacturers (e.g. Huawei, Samsung) and keypad types (e.g. virtual keypad, hard keypad) One cannot be 100% sure that, just because a tested application works well on one device, it will run smoothly on all others. Crowdsourced testing is an emerging paradigm that can improve the cost-effectiveness of software testing and accelerate the process, especially for mobile applications. It entrusts testing tasks to online crowdworkers whose diverse testing devices/contexts, experience, and skill sets can significantly contribute to more reliable, cost-effective and efficient testing results. It has already been adopted by many software organizations, including Google, Facebook, Amazon and Microsoft. This book provides an intelligent overview of crowdsourced testing research and practice. It employs machine learning, data mining, and deep learning techniques to process the data generated during the crowdsourced testing process, to facilitate the management of crowdsourced testing, and to improve the quality of crowdsourced testing.
650 0 $a Computer software $x Testing. $3 570748
650 0 $a Crowdsourcing. $3 3377825
650 1 4 $a Software Testing. $3 3596835
650 2 4 $a Software Management. $3 2139173
700 1 $a Wang, Qing. $3 898346
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-16-9643-5
950 $a Computer Science (SpringerNature-11645)