Organizing knowledge workforce for specified iterative software development tasks
Organizing knowledge workers for specific tasks in a software development process is critical for the success of software projects. Assigning workforce in software projects represents a dynamic and complex problem that concerns the utilization of cross-trained knowledge workers who possess different...
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| Veröffentlicht in: | Decision Support Systems 2014-03, Vol.59, p.15-27 |
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| creator | Shao, Benjamin B.M. Yin, Peng-Yeng Chen, Andrew N.K. |
| description | Organizing knowledge workers for specific tasks in a software development process is critical for the success of software projects. Assigning workforce in software projects represents a dynamic and complex problem that concerns the utilization of cross-trained knowledge workers who possess different productivities and error tendencies in coding and defect correction. This complexity is further compounded when the development process follows a software release life cycle and involves major releases of alpha, beta, and final versions in the context of iterative software development. We study this knowledge workforce problem from three essential project management perspectives: (1) timeliness — obtaining shortest development time; (2) effectiveness — satisfying budget constraint; and (3) efficiency — achieving high workforce utilization. We explore ideal workforce composites with two strategic focuses on productivity and quality and with different scenarios of workload ratios. An analytical model is formulated and a meta-heuristic approach based on particle swarm optimization is used to derive solutions in a simulation experiment. Our findings suggest that forming an ideal workforce composite is a non-trivial task and task assignments with divergent focuses for software projects under different workload scenarios require different planning strategies. Practical implications are drawn from our findings to provide insight on effectively planning workforce for software projects with specific goals and considerations.
•Knowledge workforce issue in the context of iterative software development•Three project management perspectives: timeliness, effectiveness, and efficiency•Ideal workforce composite with two strategic focuses on productivity and quality•An analytical model with a meta-heuristic approach of particle swarm optimization•Simulation findings shed light on identifying specific workforce composites. |
| doi_str_mv | 10.1016/j.dss.2013.10.002 |
| format | Article |
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•Knowledge workforce issue in the context of iterative software development•Three project management perspectives: timeliness, effectiveness, and efficiency•Ideal workforce composite with two strategic focuses on productivity and quality•An analytical model with a meta-heuristic approach of particle swarm optimization•Simulation findings shed light on identifying specific workforce composites.</description><identifier>ISSN: 0167-9236</identifier><identifier>EISSN: 1873-5797</identifier><identifier>DOI: 10.1016/j.dss.2013.10.002</identifier><identifier>CODEN: DSSYDK</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Algorithmics. Computability. Computer arithmetics ; Applied sciences ; Computer programs ; Computer science; control theory; systems ; Data processing. List processing. Character string processing ; Exact sciences and technology ; Information professionals ; Inventory control, production control. Distribution ; Iterative methods ; Iterative software development ; Knowledge management ; Memory organisation. Data processing ; Operational research and scientific management ; Operational research. Management science ; Organizing ; Particle swarm optimization ; Product life cycle ; Productivity ; Project management ; Simulations ; Software ; Software development ; Software engineering ; Studies ; Task assignment ; Tasks ; Theoretical computing ; Utilization ; Workforce ; Workforce management ; Workload ; Workloads</subject><ispartof>Decision Support Systems, 2014-03, Vol.59, p.15-27</ispartof><rights>2013 Elsevier B.V.</rights><rights>2015 INIST-CNRS</rights><rights>Copyright Elsevier Sequoia S.A. Mar 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c388t-3b43297dd66d23c42b8c98519987db565323c4f8a48cd5225b13c9db6bbc27ca3</citedby><cites>FETCH-LOGICAL-c388t-3b43297dd66d23c42b8c98519987db565323c4f8a48cd5225b13c9db6bbc27ca3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.dss.2013.10.002$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3548,27923,27924,45994</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28306972$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Shao, Benjamin B.M.</creatorcontrib><creatorcontrib>Yin, Peng-Yeng</creatorcontrib><creatorcontrib>Chen, Andrew N.K.</creatorcontrib><title>Organizing knowledge workforce for specified iterative software development tasks</title><title>Decision Support Systems</title><description>Organizing knowledge workers for specific tasks in a software development process is critical for the success of software projects. Assigning workforce in software projects represents a dynamic and complex problem that concerns the utilization of cross-trained knowledge workers who possess different productivities and error tendencies in coding and defect correction. This complexity is further compounded when the development process follows a software release life cycle and involves major releases of alpha, beta, and final versions in the context of iterative software development. We study this knowledge workforce problem from three essential project management perspectives: (1) timeliness — obtaining shortest development time; (2) effectiveness — satisfying budget constraint; and (3) efficiency — achieving high workforce utilization. We explore ideal workforce composites with two strategic focuses on productivity and quality and with different scenarios of workload ratios. An analytical model is formulated and a meta-heuristic approach based on particle swarm optimization is used to derive solutions in a simulation experiment. Our findings suggest that forming an ideal workforce composite is a non-trivial task and task assignments with divergent focuses for software projects under different workload scenarios require different planning strategies. Practical implications are drawn from our findings to provide insight on effectively planning workforce for software projects with specific goals and considerations.
•Knowledge workforce issue in the context of iterative software development•Three project management perspectives: timeliness, effectiveness, and efficiency•Ideal workforce composite with two strategic focuses on productivity and quality•An analytical model with a meta-heuristic approach of particle swarm optimization•Simulation findings shed light on identifying specific workforce composites.</description><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Applied sciences</subject><subject>Computer programs</subject><subject>Computer science; control theory; systems</subject><subject>Data processing. List processing. Character string processing</subject><subject>Exact sciences and technology</subject><subject>Information professionals</subject><subject>Inventory control, production control. Distribution</subject><subject>Iterative methods</subject><subject>Iterative software development</subject><subject>Knowledge management</subject><subject>Memory organisation. Data processing</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Organizing</subject><subject>Particle swarm optimization</subject><subject>Product life cycle</subject><subject>Productivity</subject><subject>Project management</subject><subject>Simulations</subject><subject>Software</subject><subject>Software development</subject><subject>Software engineering</subject><subject>Studies</subject><subject>Task assignment</subject><subject>Tasks</subject><subject>Theoretical computing</subject><subject>Utilization</subject><subject>Workforce</subject><subject>Workforce management</subject><subject>Workload</subject><subject>Workloads</subject><issn>0167-9236</issn><issn>1873-5797</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp9kEtrGzEQgEVpoW6aH5DbQij0so4eXj3oqZi8IGAC7VlopVkje71yNGub9tdHi0MPPeSiQcM3r4-QK0bnjDJ5s5kHxDmnTJT_nFL-gcyYVqJulFEfyawwqjZcyM_kC-KGUimUljPyvMprN8S_cVhX2yGdeghrqE4pb7uUPVTlrXAPPnYRQhVHyG6MR6gwdePJZagCHKFP-x0MYzU63OJX8qlzPcLlW7wgv-9ufy0f6qfV_ePy51PthdZjLdqF4EaFIGXgwi94q73RDTNGq9A2shFTttNuoX1oOG9aJrwJrWxbz5V34oJ8P_fd5_RyABztLqKHvncDpAPacjBrDDdcF_T6P3STDnko21nWUCUpVZoXip0pnxNihs7uc9y5_McyaifJdmOLZDtJnlJFcqn59tbZoXd9l93gI_4rLLOpNGrifpw5KEaOEbJFH2HwEGIGP9qQ4jtTXgFndpH4</recordid><startdate>20140301</startdate><enddate>20140301</enddate><creator>Shao, Benjamin B.M.</creator><creator>Yin, Peng-Yeng</creator><creator>Chen, Andrew N.K.</creator><general>Elsevier B.V</general><general>Elsevier</general><general>Elsevier Sequoia S.A</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20140301</creationdate><title>Organizing knowledge workforce for specified iterative software development tasks</title><author>Shao, Benjamin B.M. ; Yin, Peng-Yeng ; Chen, Andrew N.K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c388t-3b43297dd66d23c42b8c98519987db565323c4f8a48cd5225b13c9db6bbc27ca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Algorithmics. Computability. Computer arithmetics</topic><topic>Applied sciences</topic><topic>Computer programs</topic><topic>Computer science; control theory; systems</topic><topic>Data processing. List processing. Character string processing</topic><topic>Exact sciences and technology</topic><topic>Information professionals</topic><topic>Inventory control, production control. Distribution</topic><topic>Iterative methods</topic><topic>Iterative software development</topic><topic>Knowledge management</topic><topic>Memory organisation. Data processing</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Organizing</topic><topic>Particle swarm optimization</topic><topic>Product life cycle</topic><topic>Productivity</topic><topic>Project management</topic><topic>Simulations</topic><topic>Software</topic><topic>Software development</topic><topic>Software engineering</topic><topic>Studies</topic><topic>Task assignment</topic><topic>Tasks</topic><topic>Theoretical computing</topic><topic>Utilization</topic><topic>Workforce</topic><topic>Workforce management</topic><topic>Workload</topic><topic>Workloads</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shao, Benjamin B.M.</creatorcontrib><creatorcontrib>Yin, Peng-Yeng</creatorcontrib><creatorcontrib>Chen, Andrew N.K.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Decision Support Systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shao, Benjamin B.M.</au><au>Yin, Peng-Yeng</au><au>Chen, Andrew N.K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Organizing knowledge workforce for specified iterative software development tasks</atitle><jtitle>Decision Support Systems</jtitle><date>2014-03-01</date><risdate>2014</risdate><volume>59</volume><spage>15</spage><epage>27</epage><pages>15-27</pages><issn>0167-9236</issn><eissn>1873-5797</eissn><coden>DSSYDK</coden><abstract>Organizing knowledge workers for specific tasks in a software development process is critical for the success of software projects. Assigning workforce in software projects represents a dynamic and complex problem that concerns the utilization of cross-trained knowledge workers who possess different productivities and error tendencies in coding and defect correction. This complexity is further compounded when the development process follows a software release life cycle and involves major releases of alpha, beta, and final versions in the context of iterative software development. We study this knowledge workforce problem from three essential project management perspectives: (1) timeliness — obtaining shortest development time; (2) effectiveness — satisfying budget constraint; and (3) efficiency — achieving high workforce utilization. We explore ideal workforce composites with two strategic focuses on productivity and quality and with different scenarios of workload ratios. An analytical model is formulated and a meta-heuristic approach based on particle swarm optimization is used to derive solutions in a simulation experiment. Our findings suggest that forming an ideal workforce composite is a non-trivial task and task assignments with divergent focuses for software projects under different workload scenarios require different planning strategies. Practical implications are drawn from our findings to provide insight on effectively planning workforce for software projects with specific goals and considerations.
•Knowledge workforce issue in the context of iterative software development•Three project management perspectives: timeliness, effectiveness, and efficiency•Ideal workforce composite with two strategic focuses on productivity and quality•An analytical model with a meta-heuristic approach of particle swarm optimization•Simulation findings shed light on identifying specific workforce composites.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.dss.2013.10.002</doi><tpages>13</tpages></addata></record> |
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| subjects | Algorithmics. Computability. Computer arithmetics Applied sciences Computer programs Computer science control theory systems Data processing. List processing. Character string processing Exact sciences and technology Information professionals Inventory control, production control. Distribution Iterative methods Iterative software development Knowledge management Memory organisation. Data processing Operational research and scientific management Operational research. Management science Organizing Particle swarm optimization Product life cycle Productivity Project management Simulations Software Software development Software engineering Studies Task assignment Tasks Theoretical computing Utilization Workforce Workforce management Workload Workloads |
| title | Organizing knowledge workforce for specified iterative software development tasks |
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