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Predicting with sparse data
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DOI：10.1109/32.965339.png)
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Abstract
En 中文
It is well-known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper, we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach-based upon expert judgement-adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practicing project manager. From this empirical work, we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction.
Keywords:
prediction
software project effort
expert judgement
empirical data
sparse data
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