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Efficient techniques for partitionin...

Soothram, Samyukta.

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Efficient techniques for partitioning software development tasks.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Efficient techniques for partitioning software development tasks./
Author:	Soothram, Samyukta.
Description:	57 p.
Notes:	Source: Masters Abstracts International, Volume: 48-05, page: 3178.
Contained By:	Masters Abstracts International48-05.
Subject:	Information Technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1476351
ISBN:	9781109777277

Efficient techniques for partitioning software development tasks.
Soothram, Samyukta.

Efficient techniques for partitioning software development tasks. - 57 p.

Source: Masters Abstracts International, Volume: 48-05, page: 3178.

Thesis (M.S. and M.B.A.)--Iowa State University, 2010.

This research examines the problem of assigning software development tasks to teams. The goal of this study is to model the most efficient way of module assignments in order to reduce the communication and coordination delays among software teams that arise from the improper distribution of software modules. The study quantifies the module interactions using software coupling design measure and models these interactions using Linear Programming and Cluster Analysis techniques. The performance of the two techniques is evaluated to find the one that offers the most favorable set of module assignments that can be used by software practitioners in the real world. The results obtained from this research suggest that though Linear Programming is the most optimal technique for obtaining the solution, it cannot provide solutions for large problems. With an increase in the number of modules, the computational time required for Linear Programming model increased considerably. Cluster Analysis, on the other hand, provided solutions which were not as optimal as Linear Programming but generated module assignments for large module count problems. Two types of Cluster Analysis techniques, namely agglomerative clustering and partitional clustering were implemented in this research. Of the two, agglomerative cluster analysis technique offered the most efficient and practical solution for module assignments. This research is an attempt to improve the decision making capabilities of software practitioners who often make use of intuitions and their past experiences in the process of assigning modules to software development teams.

ISBN: 9781109777277Subjects--Topical Terms:

1030799
Information Technology.

Efficient techniques for partitioning software development tasks.
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020 $a 9781109777277
035 $a (UMI)AAI1476351
035 $a AAI1476351
040 $a UMI $c UMI
100 1 $a Soothram, Samyukta. $3 1675516
245 1 0 $a Efficient techniques for partitioning software development tasks.
300 $a 57 p.
500 $a Source: Masters Abstracts International, Volume: 48-05, page: 3178.
500 $a Adviser: Zhengrui Jiang.
502 $a Thesis (M.S. and M.B.A.)--Iowa State University, 2010.
520 $a This research examines the problem of assigning software development tasks to teams. The goal of this study is to model the most efficient way of module assignments in order to reduce the communication and coordination delays among software teams that arise from the improper distribution of software modules. The study quantifies the module interactions using software coupling design measure and models these interactions using Linear Programming and Cluster Analysis techniques. The performance of the two techniques is evaluated to find the one that offers the most favorable set of module assignments that can be used by software practitioners in the real world. The results obtained from this research suggest that though Linear Programming is the most optimal technique for obtaining the solution, it cannot provide solutions for large problems. With an increase in the number of modules, the computational time required for Linear Programming model increased considerably. Cluster Analysis, on the other hand, provided solutions which were not as optimal as Linear Programming but generated module assignments for large module count problems. Two types of Cluster Analysis techniques, namely agglomerative clustering and partitional clustering were implemented in this research. Of the two, agglomerative cluster analysis technique offered the most efficient and practical solution for module assignments. This research is an attempt to improve the decision making capabilities of software practitioners who often make use of intuitions and their past experiences in the process of assigning modules to software development teams.
590 $a School code: 0097.
650 4 $a Information Technology. $3 1030799
650 4 $a Computer Science. $3 626642
690 $a 0489
690 $a 0984
710 2 $a Iowa State University. $b Logistics, Operations, and Management Information Systems. $3 1064793
773 0 $t Masters Abstracts International $g 48-05.
790 1 0 $a Jiang, Zhengrui, $e advisor
790 1 0 $a Suzuki, Yoshinori $e committee member
790 1 0 $a Crum, Michael $e committee member
790 $a 0097
791 $a M.S. and M.B.A.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1476351