Context: In addressing how best to estimate how much effort is required to develop software, a recent study found that using exemplary and recently completed projects [forming Bellwether moving windows (BMW)] in software effort prediction (SEP) models leads to relatively improved accuracy. More studies need to be conducted to determine whether the BMW yields improved accuracy in general, since different sizing and aging parameters of the BMW are known to affect accuracy. Objective: To investigate the existence of exemplary projects (Bellwethers) with defined window size and age parameters, and whether their use in SEP improves prediction accuracy. Method: We empirically investigate the moving window assumption based on the theory that the prediction outcome of a future event depends on the outcomes of prior events. Sampling of Bellwethers was undertaken using three introduced Bellwether methods (SSPM, SysSam, and RandSam). The ergodic Markov chain was used to determine the stationarity of the Bell-wethers. Results: Empirical results show that 1) Bellwethers exist in SEP and 2) the BMW has an approximate size of 50 to 80 exemplary projects that should not be more than 2 years old relative to the new projects to be estimated. Conclusion: The study's results add further weight to the recommended use of Bellwethers for improved prediction accuracy in SEP.
翻译:在讨论如何最好地估计开发软件需要付出多大努力的问题时,最近的一项研究发现,在软件工作预测模型(SEP)模型中使用模范和最近完成的项目[形成Bellwether移动窗口(BMW)],可以提高相对准确性;需要开展更多的研究,以确定BMW是否总体上提高了准确性,因为已知BMW的不同规模和老化参数会影响准确性;目标:调查是否有具有确定窗口大小和年龄参数的模范项目(Bellwethers),以及这些项目在SEP中的使用是否提高预测准确性。方法:我们根据未来事件的预测结果取决于以往事件结果的理论对移动窗口假设进行了实证性调查。Bellwethers进行了抽样调查,使用了三种引进的Bellwethers方法(SSPM、SysSam和RandSam),以确定总体准确性。ergodic Markov 链用于确定Bell-wethers的固定性。结果:EP和2中存在贝尔韦瑟斯的预测性。BMWER的预测值大约为50至80年的准确性;SBERWIL的精确性,建议在2年的预测中,而不是更精确性项目。