Optimal Project Feature Weights in Analogy-Based Cost Estimation: Improvement and Limitations
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The objective of this paper is to investigate thepertinent question whether multi-organizational data isvaluable for software project cost estimation. Local,company-specific data is widely believed to provide abetter basis for accurate estimates. On the other hand,multi-organizational databases provide an opportunityfor fast data accumulation and shared informationbenefits. Therefore, this paper trades off the potentialadvantages and drawbacks of using local data ascompared to multi-organizational data.Motivated by the results from previous investigations,we further analyzed a large cost database from Finlandthat collects standard cost factors and includesinformation on six individual companies. Each of thesecompanies provided data for more than ten projects. Thisinformation was used to compare the accuracy betweencompany-specific (local) and company-external (global)cost models. Consistent with some of the previousinvestigations in this area, the general trends in theresults are surprising. They show that company-specificmodels seem not to yield better results than the company-externalmodels. Our results are based on applying twostandard statistical estimation methods (OLS-regression,Analysis of Variance) and Analogy-based estimation.