This allows comparing the replications’ results and hypothesizing on whether the replications’ threats to validity might have materialized or not. For example, replications’ designs can be tweaked to overcome the threats to validity of previous experiments. Replication of experiments may help to overcome such limitations (Shepperd 2016). 2014): (1) results may be imprecise (as the number of participants is typically low in SE experiments) (2) results might be artifactual (e.g., influenced by the programming environment rather than the treatments themselves) (3) results cannot be generalized to different contexts rather than those of the experiment (4) results may be impacted by the materialization of unforeseen threats to validity (e.g., rivalry threat, when the effectiveness of the less “desirable” treatment gets penalized by the participants’ disinterest). However, isolated experiments suffer from several shortcomings (Gómez et al. Isolated experiments are being run in software engineering (SE) to assess the performance of different treatments (i.e., tools, technologies, or processes).
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