Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/598461
Title: Optimal Group Size for Software Change Tasks: A Social Information Foraging Perspective
Authors: Tanmay Bhowmik;Nan Niu;Wentao Wang;Jing-Ru C. Cheng;Ling Li;Xiongfei Cao
subject: task assignment;social information foraging theory;group size;productivity;Cybernetic application
Year: 2016
Publisher: IEEE
Abstract: Group size is a key factor in collaborative software development and many other cybernetic applications where task assignments are important. While methods exist to estimate its value for proprietary projects, little is known about how group size affects distributed and decentralized cybernetic applications and in particular open source software (OSS) development. This paper presents a novel approach in which we frame developers' collective resolution of OSS change tasks as a social information foraging problem. This new perspective enables us to predict the optimal group size and quantify group size's effect on individual performance. We test the theory with data mined from two projects: 1) Firefox and 2) Mylyn. This paper not only uncovers the mismatch of optimal and actual group sizes, but also reveals the association of optimality with improved productivity. In addition, the social-level productivity gain is observed as project evolves. We show this paper's impact by extending the frontiers of knowledge in two areas: 1) social coding and 2) recommendation systems.
URI: http://localhost/handle/Hannan/154410
http://localhost/handle/Hannan/598461
ISSN: 2168-2267
2168-2275
volume: 46
issue: 8
Appears in Collections:2016

Files in This Item:
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Title: Optimal Group Size for Software Change Tasks: A Social Information Foraging Perspective
Authors: Tanmay Bhowmik;Nan Niu;Wentao Wang;Jing-Ru C. Cheng;Ling Li;Xiongfei Cao
subject: task assignment;social information foraging theory;group size;productivity;Cybernetic application
Year: 2016
Publisher: IEEE
Abstract: Group size is a key factor in collaborative software development and many other cybernetic applications where task assignments are important. While methods exist to estimate its value for proprietary projects, little is known about how group size affects distributed and decentralized cybernetic applications and in particular open source software (OSS) development. This paper presents a novel approach in which we frame developers' collective resolution of OSS change tasks as a social information foraging problem. This new perspective enables us to predict the optimal group size and quantify group size's effect on individual performance. We test the theory with data mined from two projects: 1) Firefox and 2) Mylyn. This paper not only uncovers the mismatch of optimal and actual group sizes, but also reveals the association of optimality with improved productivity. In addition, the social-level productivity gain is observed as project evolves. We show this paper's impact by extending the frontiers of knowledge in two areas: 1) social coding and 2) recommendation systems.
URI: http://localhost/handle/Hannan/154410
http://localhost/handle/Hannan/598461
ISSN: 2168-2267
2168-2275
volume: 46
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7091909.pdf1.4 MBAdobe PDFThumbnail
Preview File
Title: Optimal Group Size for Software Change Tasks: A Social Information Foraging Perspective
Authors: Tanmay Bhowmik;Nan Niu;Wentao Wang;Jing-Ru C. Cheng;Ling Li;Xiongfei Cao
subject: task assignment;social information foraging theory;group size;productivity;Cybernetic application
Year: 2016
Publisher: IEEE
Abstract: Group size is a key factor in collaborative software development and many other cybernetic applications where task assignments are important. While methods exist to estimate its value for proprietary projects, little is known about how group size affects distributed and decentralized cybernetic applications and in particular open source software (OSS) development. This paper presents a novel approach in which we frame developers' collective resolution of OSS change tasks as a social information foraging problem. This new perspective enables us to predict the optimal group size and quantify group size's effect on individual performance. We test the theory with data mined from two projects: 1) Firefox and 2) Mylyn. This paper not only uncovers the mismatch of optimal and actual group sizes, but also reveals the association of optimality with improved productivity. In addition, the social-level productivity gain is observed as project evolves. We show this paper's impact by extending the frontiers of knowledge in two areas: 1) social coding and 2) recommendation systems.
URI: http://localhost/handle/Hannan/154410
http://localhost/handle/Hannan/598461
ISSN: 2168-2267
2168-2275
volume: 46
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7091909.pdf1.4 MBAdobe PDFThumbnail
Preview File