The COTECOMO: COnstractive Test Effort COst MOdel

Abstract

The primary purpose of Software Testing Process and Evaluation (STP&E) is to reduce risk. While there exists extensive literature on software cost estimation techniques, industry practice continues to rely upon standard regression-based algorithms. These software effort models are typically calibrated or tuned to local conditions using local data. This paper cautions that current approaches to model calibration often produce sub-optimal models because of the large variance problem which is inherent in cost data and by including far more effort multipliers than the data supports. Building optimal models requires that a wider range of models be considered while correctly calibrating these models requires rejection rules that prune variables and records and use multiple criteria for evaluating model performance. This article compares the approaches taken by three (COCOMO II, FP, UCP) widely used models for software cost and schedule estimation to develop COTECOMO (COnstractive Test Effort COst MOdel). It also documents what we call the large variance problem that is a leading cause of cost model brittleness or instability. This paper proposes Software/System Test Point (STP), a new metric for estimating overall software testing process. STP covers so-called black-box testing; an estimate for the test activities, which precede scenarios (threads) testing (white-box testing included), will already have been included in the estimate produced by function point analysis. Software test point is a useful metric for test managers interested in estimating software test effort, and the metric aids in the precise estimation of project effort and addresses the interests of metric group.