In this paper, we investigate the application of the COBRA/spl trade/ method (Cost Estimation, Benchmarking, and Risk Assessment) in a new application domain, the area of web development. COBRA combines expert knowledge with data on a small number of projects to develop cost estimation models, which can also be used for risk analysis and benchmarking purposes. We modified and applied the method to the web applications of a small Australian company, specializing in web development. In this paper we present the modifications made to the COBRA method and results of applying the method In our study, using data on twelve web applications, the estimates derived from our Web-COBRA model showed a Mean Magnitude of Relative Error (MMRE) of 0.17. This result significantly outperformed expert estimates from Allette Systems (MMRE 0.37). A result comparable to Web-COBRA was obtained when applying ordinary least squares regression with size in terms of Web Objects as an independent variable (MMRE 0.23).

Delivering a software product on time, within budget, and to an agreed level of quality is a critical concern for many software organizations. Underestimating software costs can have detrimental effects on the quality of the delivered software and thus on a company's business reputation and competitiveness. On the other hand, overestimation of software cost can result in missed opportunities to funds in other projects. In response to industry demand, a myriad of estimation techniques has been proposed during the last three decades. In order to assess the suitability of a technique from a diverse selection, its performance and relative merits must be compared.

This paper investigates two essential data-driven, software cost modeling: questions related to (1) What modeling _ _ techniques are likely to yield more accurate results when using typical software development cost data?and (2) What are the benefits and drawbacks of using organizationspecific data as compared to multi-organization databases?The former question is important in guiding software cost analysts in their choice of the right type of modeling technique, if at all possible.In order to address this issue, we assess and compare a selection of common cost modeling techniques fulfilling a number of important criteria using a large multi-organizational database in the business application domain.Namely, these are: ordinary least squares regression, stepwise ANOVA, CART, and analogy.The latter question is important in order to assess the feasibility of using multi-organization cost databases to build cost models and the benefits gained from local, company-specific data collection and modeling.As a large subset of the data in the multi-company database came from one organization, we were able to investigate this issue by comparing organization-specific models with models based on multi-organization data.Results show that the performances of the modeling techniques considered were not significantly different, with the exception of the analogy-based models which appear to be less accurate.Surprisingly, when using standard cost factors (e.g., COCOMO-like factors, Function Points), organization specific models did not yield better results than generic, multi-organization models.Permission to make digital or hard topics c~fall or part of this work fbl personal or classroom USC is granted without fee provided that topics are not made or distributed for profit or commercial advnntage and that copies hear this notice and the full citation cm the tkt page.To CojJy othcrwiso, to republish, to post on servers or to redistribute to lists.requires prior specific permission and/or a fee.

There has been much controversy in the literature on several issues underlying the construction of parametric software development cost models.For example, it has been argued whether (dis)economies of scale exist in software production, what functional form should be assumed between effort and product size, whether COCOMO factors were useful, and whether the COCOMO factors are independent.Answers to such questions should help software organizations define suitable data collection programs and well-specified cost models.The only way to address these issues and obtain a generalizable conclusion is to investigate them on a large number of consistent data sets.In this paper we use a data set collected by the European Space Agency to perform such an investigation.To ensure a certain degree of consistency in our data, we focus our analysis on a set of space and military projects that represent an important application domain and the largest subset in the database.These projects have been performed, however, by a variety of organizations.First, our results indicate that two functional forms are plausible between effort and product size: linear and log-linear.This also means that different project subpopulations are likely to follow different functional forms.Second, besides product size, the strongest factor influencing cost appears to be team size.Larger teams result in substantially lower productivity, which is interesting considering this attribute is rarely collected in software engineering cost data bases.Third, although some COCOMO factors appear to be useful and significant covariates, they play a minor role in explaining project effort.Overall, the most plausible model appears to be a log-linear model involving KLOC, team size, and a principal component influenced by three COCOMO factors: reliability requirements (RELY), Permission to make digital or hard copies of all or part of this work IhI pcrsond or classroom USC is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies hear this notice and the f'ull citation on the lirst page.TO COPY otherwise, to republish, to post on servers or to rcdistrihutc 10 lists.requires prior specific permission and/or a fee.

Counts of defects found during the various defect defection activities in software projects and their classification provide a basis for product quality evaluation and process improvement. However, since defect classifications are subjective, it is necessary to ensure that they are repeatable (i.e., that the classification is not dependent on the individual). We evaluate a slight adaptation of a commonly used defect classification scheme that has been applied in IBM's Orthogonal Defect Classification work, and in the SEI's Personal Software Process. The evaluation utilizes the Kappa statistic. We use defect data from code inspections conducted during a development project. Our results indicate that the classification scheme is in general repeatable. We further evaluate classes of defects to find out if confusion between some categories is more common, and suggest a potential improvement to the scheme.