This content originally appeared on HackerNoon and was authored by Pair Programming AI Agent
Table of Links
3. Experiment Design and Conduct
3.2 Subjects, Tasks and Objects
4.2 Analysis of Variance (ANOVA)
4.4 Effect Size and Power Analysis
5. Experiment Limitations and 5.1 Threats to the Conclusion Validity
5.2 Threats to Internal Validity
5.3 Threats to Construct Validity
5.4 Threats to External Validity
6. Discussion and 6.1 Duration
7. Conclusions and Further Work, and References
6.2 Effort
This measure is not present in all of the experiments previously discussed, so we compute it (doubling the time duration of pairs) only in the cases where data is available.
\ According to Nosek data [24] we observe a decrease in effort of 29% in favor of solo programming. Conversely, data of Lui and Chan [19] indicate a decrease of 4% in favor of pairs. Finally, Arisholm et al. [1] Report an increase in effort of 84% (against of pairs).
\ In contrast, the results reported in this paper infer a significant (at a=0.1) 30% decrease in effort (in favor of solos), and an effect size d=0.64. Our results, again, reinforce those calculated in [24].
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:::info Authors:
(1) Omar S. Gómez, full time professor of Software Engineering at Mathematics Faculty of the Autonomous University of Yucatan (UADY);
(2) José L. Batún, full time professor of Statistics at Mathematics Faculty of the Autonomous University of Yucatan (UADY);
(3) Raúl A. Aguilar, Faculty of Mathematics, Autonomous University of Yucatan Merida, Yucatan 97119, Mexico.
:::
:::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
:::
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This content originally appeared on HackerNoon and was authored by Pair Programming AI Agent
Pair Programming AI Agent | Sciencx (2025-08-23T02:00:05+00:00) Pair Programming Research: Contrasting Results on Effort. Retrieved from https://www.scien.cx/2025/08/23/pair-programming-research-contrasting-results-on-effort/
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