Software engineering (SE) is full of abstract concepts that are crucial for both researchers and practitioners, such as programming experience, team productivity, code comprehension, and system security. Secondary studies aimed at summarizing research on the influences and consequences of such concepts would therefore be of great value. However, the inability to measure abstract concepts directly poses a challenge for secondary studies: primary studies in SE can operationalize such concepts in many ways. Standardized measurement instruments are rarely available, and even if they are, many researchers do not use them or do not even provide a definition for the studied concept. SE researchers conducting secondary studies therefore have to decide a) which primary studies intended to measure the same construct, and b) how to compare and aggregate vastly different measurements for the same construct. In this experience report, we discuss the challenge of study selection in SE secondary research on latent variables. We report on two instances where we found it particularly challenging to decide which primary studies should be included for comparison and synthesis, so as not to end up comparing apples with oranges. Our report aims to spark a conversation about developing strategies to address this issue systematically and pave the way for more efficient and rigorous secondary studies in software engineering.

翻译：软件工程（SE）中充斥着对研究人员和从业者至关重要的抽象概念，如编程经验、团队生产力、代码理解与系统安全性。旨在总结这些概念的影响与后果的二次研究具有重要价值。然而，抽象概念的不可直接测量性给二次研究带来了挑战：SE中的原始研究可通过多种方式实现这些概念的操作化。标准化测量工具往往匮乏，即便存在，许多研究者也未必使用，甚至未对所研究的概念给出明确定义。因此，开展二次研究的SE研究者需决定：a) 哪些原始研究意在测量相同构念，b) 如何比较并聚合针对同一构念的差异极大的测量结果。在本经验报告中，我们探讨了SE二次研究中针对潜在变量的研究选择难题。我们报告了两个案例，在这些案例中，判断哪些原始研究应纳入比较与综合（以避免出现苹果与橙子相混淆的情况）尤为困难。本报告旨在引发讨论，探讨系统化应对该问题的策略，并为软件工程领域更高效、严谨的二次研究铺平道路。