#pair-programming

#pair-programming

Pair programming and continuous integration can go hand-in-hand. Pushing to main multiple times a day is hard in isolation, leading to delays, large PRs, and merge issues, Ola Hast and Asgaut Mjølne Söderbom mentioned in their talk about continuous delivery with pair programming at QCon London. Pairing enables instant code review, easier refactoring, fewer bugs, and higher team resilience. In an earlier article, Hast and Mjølne Söderbom mentioned that their team uses pair and mob programming with TDD;

He said that all the interviews he did before Stripe involved a whiteboard and he found these tests impractical. "We believed and believe today that that's actually not a great way of simulating what it's like to see a real engineer do work," Singleton said. "At Stripe, we designed an interview process where folks would actually be on a laptop with all the tools that they were used to having and pair programmers with an interviewer."

Taking this alpha level (a=0.1) into account, we perform a treatment comparison test (also referred as contrast test) for each measure. Table 8 shows the treatment means, standard error and replications for duration measure whereas Table 9 shows the same information for effort. There are several tests for performing treatment comparisons. These tests help us to analyze pairs of means to assess possible differences between means. Using Scheffé test [21] for treatment comparisons, Table 10 shows the treatment comparison with respect to duration.

Once we have the measures, we are able to test the hypotheses through statistical inferences. The statistical model associated with a Latin square design is shown in equation (1). This design uses analysis of variance (ANOVA) to assess the components (overall mean, blocks, treatment and random error) of the model. ANOVA is based on looking at the total variability of the collected measures and the variability partition according to different components.

Just as one pair member may provide too little explanation, she may also Drown the Partner in too many explanations that go far beyond the task and are hence not expedient.

Interaction and Events: While creating code, the all-male pairs interacted more with the SCRATCH interface than the all-female pairs. The all-male pairs moved blocks around significantly more often than all-female pairs (p = 0.041). Similarly, boys changed the parameters of an existing block more than the girls (p = 0.060). Dragging a new block out of the toolbox is also done more often by the all-male pairs, but not with such a distinct difference (p = 0.093).

The team relies on pair and mob programming with Test-Driven Development (TDD), and all tasks require at least two team members to ensure shared responsibility.

The study demonstrated significant improvements in student performance and confidence levels when implementing pair programming as opposed to traditional individual assignments.