Swarm intelligence
The collaborative software process(sm)
The collaborative software process(sm)
NextGen eXtreme porting: structured by automation
Proceedings of the 2005 ACM symposium on Applied computing
Pair programming and the re-appropriation of individual tools for collaborative programming
GROUP '05 Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work
Evaluating performances of pair designing in industry
Journal of Systems and Software
Evaluating Pair Programming with Respect to System Complexity and Programmer Expertise
IEEE Transactions on Software Engineering
An experimental investigation of personality types impact on pair effectiveness in pair programming
Empirical Software Engineering
Pair programming and the re-appropriation of individual tools for collaborative software development
Proceedings of the 2006 conference on Cooperative Systems Design: Seamless Integration of Artifacts and Conversations -- Enhanced Concepts of Infrastructure for Communication
Pair programming vs. side-by-side programming
EuroSPI'05 Proceedings of the 12th European conference on Software Process Improvement
A framework for understanding the factors influencing pair programming success
XP'05 Proceedings of the 6th international conference on Extreme Programming and Agile Processes in Software Engineering
Software process fusion: uniting pair programming and solo programming processes
SPW/ProSim'06 Proceedings of the 2006 international conference on Software Process Simulation and Modeling
The collaborative nature of pair programming
XP'06 Proceedings of the 7th international conference on Extreme Programming and Agile Processes in Software Engineering
Proceedings of the 34th International Conference on Software Engineering
| Hi-index | 0.00 |
This paper reports experimental measurements of productivity and quality in pair programming. The work complements Laurie Williams' work on collaborative programming, in which Pair Programming and Solo Programming student groups wrote the same programs and then their activities were measured to investigate productivity, quality, etc. In this paper, Pair and Solo industrial programmer groups are requested to complete algorithm-style aptitude tests so as to observe the capability of solving algorithms in singles and in pairs. So doing is independent of the familiarity of a programming language. Besides, we also take another approach to examining pair programming. A single group of industrial programmers carries alternately out Pair Programming and Solo Programming. All these demonstrate that productivity in pair programming hinges upon algorithm design at all levels from understanding problems and implementing solutions. In addition, we reach similar conclusions to Williams. Our findings indicate that simple design, refactoring, and rapid feedback provide an excellent continuous-design environment for higher productivity in pair programming.