Author List: Dawande, Milind; Johar, Monica S.; Kumar, Subodha; Mookerjee, Vijay S.;
Information Systems Research, 2008, Volume 19, Issue 1, Page 71-92.
This study compares the performances of pair development (an approach in which a pair of developers jointly work on the same piece of code), solo development, and mixed development under two separate objectives: effort minimization and time minimization. To this end, we develop analytical models to optimize module-developer assignments in each of these approaches. These models are shown to be strongly NP-hard and solved using a genetic algorithm. The solo and pair development approaches are compared for a variety of problem instances to highlight project characteristics that favor one of the two practices. We also propose a simple criterion that can reliably recommend the appropriate approach for a given problem instance. Typically, for efficient knowledge sharing between developers or for highly connected systems, the pair programming approach is preferable. Also, the pair approach is better at leveraging expertise by pairing experts with less skilled partners. Solo programming is usually desirable if the system is large or the effort needed either to form a pair or to code efficiently in pairs is high. Solo programming is also appropriate for projects with a tight deadline, whereas the reverse is true for projects with a lenient deadline. The mixed approach (i.e., an approach where both the solo and pair practices are used in the same project) is only indicated when the system consists of groups of modules that are sufficiently different from one another.
Keywords: extreme programming; genetic algorithms; heuristics; integer programming; pair programming; software development methodology
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#297 0.304 programming program programmers pair programs pairs software development problem time language application productivity best nominal languages programmer generators working reduces
#97 0.290 set approach algorithm optimal used develop results use simulation experiments algorithms demonstrate proposed optimization present analytical distribution selection number existing
#261 0.090 software development maintenance case productivity application tools systems function tool engineering projects effort code developed applications analysis estimation methodology methods
#180 0.051 multiple elements process environments complex integrated interdependencies design different developing integration order approach dialogue framework capabilities settings building focus distinct