Predicting with sparse data

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. The authors describe our sparse data method (SDM) based upon a pairwise comparison technique and T.L. Saaty´s (1980) 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