论文标题
在软件工程实验中使用不同的财务引起化方案的实验室实验
A Laboratory Experiment on Using Different Financial-Incentivization Schemes in Software-Engineering Experimentation
论文作者
论文摘要
在软件工程研究中,许多经验研究都是由开源或行业开发人员进行的。但是,与经济学或心理学等其他研究社区相比,只有很少的实验使用经济激励措施(即支付钱)作为激励参与者行为并奖励其表现的策略。 Sigsoft经验标准的最新版本仅用于增加参与调查的支出,而不是模仿实验中的现实世界动机和行为。在本文中,我们报告了一个受控的实验,我们通过研究不同的财务激励计划如何影响开发人员来解决这一差距。为此,我们首先对现实世界中使用的财务激励措施进行了调查,基于该调查,我们设计了三种激励方案:(1)依赖于绩效的方案,员工更喜欢绩效,(2)一种与绩效无关的计划,以及(3)一种模拟开放源源开发的计划。然后,使用受试者间实验设计,我们探讨了这三个方案如何影响参与者的表现。我们的发现表明,不同的方案会影响参与者在软件工程实验中的表现。由于样本量较小,我们的结果在统计上不显着,但我们仍然可以观察到明显的趋势。我们的贡献有助于了解经济激励措施对实验和现实情况的参与者的影响,从而指导研究人员设计实验和组织来补偿开发人员。
In software-engineering research, many empirical studies are conducted with open-source or industry developers. However, in contrast to other research communities like economics or psychology, only few experiments use financial incentives (i.e., paying money) as a strategy to motivate participants' behavior and reward their performance. The most recent version of the SIGSOFT Empirical Standards mentions payouts only for increasing participation in surveys, but not for mimicking real-world motivations and behavior in experiments. Within this article, we report a controlled experiment in which we tackled this gap by studying how different financial incentivization schemes impact developers. For this purpose, we first conducted a survey on financial incentives used in the real-world, based on which we designed three incentivization schemes: (1) a performance-dependent scheme that employees prefer, (2) a scheme that is performance-independent, and (3) a scheme that mimics open-source development. Then, using a between-subject experimental design, we explored how these three schemes impact participants' performance. Our findings indicate that the different schemes can impact participants' performance in software-engineering experiments. Due to the small sample sizes, our results are not statistically significant, but we can still observe clear tendencies. Our contributions help understand the impact of financial incentives on participants in experiments as well as real-world scenarios, guiding researchers in designing experiments and organizations in compensating developers.