This paper is the second in a series of empirical studies about requirement error abstraction and classification as a quality improvement approach. The Requirement error abstraction and classification method supports the developers' effort in efficiently identifying the root cause of requirements faults. By uncovering the source of faults, the developers can locate and remove additional related faults that may have been overlooked, thereby improving the quality and reliability of the resulting system. This study is a replication of an earlier study that adds a control group to address a major validity threat. The approach studied includes a process for abstracting errors from faults and provides a requirement error taxonomy for organizing those errors. A unique aspect of this work is the use of research from human cognition to improve the process. The results of the replication are presented and compared with the results from the original study. Overall, the results from this study indicate that the error abstraction and classification approach improves the effectiveness and efficiency of inspectors. The requirement error taxonomy is viewed favorably and provides useful insights into the source of faults. In addition, human cognition research is shown to be an important factor that affects the performance of the inspectors. This study also provides additional evidence to motivate further research.