Bayesian analysis of empirical software engineering cost models

Many parametric software estimation models have evolved in the last two decades (L.H. Putnam and W. Myers, 1992; C. Jones, 1997; R.M. Park et al., 1992). Almost all of these parametric models have been empirically calibrated to actual data from completed software projects. The most commonly used technique for empirical calibration has been the popular classical multiple regression approach. As discussed in the paper, the multiple regression approach imposes a few assumptions frequently violated by software engineering datasets. The paper illustrates the problems faced by the multiple regression approach during the calibration of one of the popular software engineering cost models, COCOMO II. It describes the use of a pragmatic 10 percent weighted average approach that was used for the first publicly available calibrated version (S. Chulani et al., 1998). It then moves on to show how a more sophisticated Bayesian approach can be used to alleviate some of the problems faced by multiple regression. It compares and contrasts the two empirical approaches, and concludes that the Bayesian approach was better and more robust than the multiple regression approach     

Metrics-Based Management of Software Product Portfolios

Commmercial software product vendors such as Microsoft, IBM, and Oracle develop and manage a large portfolio of software products, which might include operating systems, middleware, firmware, and applications. Many institutions (such as banks, universities, and hospitals) also create and manage their own custom applications. Managers at these companies face an important problem: How can you manage investment, revenue, quality, and customer expectations across such a large portfolio? A heuristics-based product maturity framework can help companies effectively manage the development and maintenance of a portfolio of software products     

Software development cost estimation approaches — A survey

This paper summarizes several classes of software cost estimation models and techniques: parametric models, expertise‐based techniques, learning‐oriented techniques, dynamics‐based models, regression‐based models, and composite‐Bayesian techniques for integrating expertise‐based and regression‐based models. Experience to date indicates that neural‐net and dynamics‐based techniques are less mature than the other classes of techniques, but that all classes of techniques are challenged by the rapid pace of change in software technology. The primary conclusion is that no single technique is best for all situations, and that a careful comparison of the results of several approaches is most likely to produce realistic estimates. This revised version was published online in June 2006 with corrections to the Cover Date.