In 1995, Boehm predicted that by 2005, there would be “55 million performers ” of “end user programming ” in the United States. The original context and method which generated this number had two weaknesses, both of which we address. First, it relies on undocumented, judgment based factors to estimate the number of end user programmers based on the total number of end users; we address this weakness by identifying specific end user sub populations and then estimating their sizes. Second, Boehm's estimate relies on additional undocumented, judgment based factors to adjust for rising computer usage rates; we address this weakness by integrating fresh Bureau of Labor Statistics (BLS) data and projections as well as a richer estimation method. With these improvements to Boehm’s method, we estimate that in 2012 there will be 90 million end users in American workplaces. Of these, we anticipate that over 55 million will use spreadsheets or databases (and therefore may potentially program), while over 13 million will describe themselves as programmers, compared to BLS projections of fewer than 3 million professional programmers. We have validated our improved method by generating estimates for 2001 and 2003, then verifying that our estimates are consistent with existing estimates from other sources.

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. 1.

We set out to answer a question we were asked by software project management: how much effort remains to be spent on a specific software project and how will that effort be distributed over time? To answer this question we propose a model based on the concept that each modification to software may cause repairs at some later time and investigate its theoretical properties and application to several projects in Avaya to predict and plan development resource allocation. Our model presents a novel unified framework to investigate and predict effort, schedule, and defects of a software project. The results of applying the model confirm a fundamental relationship between the new feature and defect repair changes and demonstrate its predictive properties.

A software product is often behind schedule, over budget, non-conforming to requirements and of poor quality. Controlling and improving the processes used to develop software has been proposed as a primary remedy to these problems. The Software Engineering Institute at Carnegie Mellon University has published the Software Capability Maturity Model (SW-CMM) for use as a set of criteria to evaluate an organization's Process Maturity. The model is also used as a roadmap to improve a software development process 's maturity. The premise of the SW-CMM is that mature development processes deliver products on time, within budget, within requirements, and of high quality. This research examines the effects of Software Process Maturity, using the SWCMM, on software development effort. Effort is the primary determinant of software development cost and schedule. The technical challenge in this research is determining how much change in effort is due solely to changing Process Maturity when this change generally occurs concurrently with changes to other factors that also influence software development effort. The six mathematical models used in this research support the following conclusion: For the one hundred twelve projects in this sample, Software Process Maturity was a significant factor (95% confidence level) affecting software development effort. After normalizing for the effects of other effort influences, a one-increment change in the rating of Process Maturity resulted in a 15% to 21% reduction in effort. The modeling approach used in this analysis can be used in other areas of Software Engineering as well.

A dynamic simulation model of an inspection-based software lifecycle process has been developed to support quantitative process evaluation. The model serves to examine the effects of inspection practices on cost, schedule and quality throughout the lifecycle. It uses system dynamics to model the interrelated flows of tasks, errors and personnel throughout different development phases and is calibrated to industrial data. It extends previous software project dynamics research by examining an inspection-based process with an original model, integrating it with the knowledge-based method for risk assessment and cost estimation, and using an alternative modeling platform. While specific enough to investigate inspection practices, it is sufficiently general to incorporate changes for other phenomena. It demonstrates the effects of performing inspections or not, the effectiveness of varied inspection policies, and the effects of other managerial policies such as manpower allocation. The resu...

Defect-occurrence projection is necessary for the development of methods to mitigate the risks of software defect occurrences. In this paper, we examine user-reported software defectoccurrence patterns across twenty-two releases of four widelydeployed, business-critical, production, software systems: a commercial operating system, a commercial middleware system, an open source operating system (OpenBSD), and an open source middleware system (Tomcat). We evaluate the suitability of common defect-occurrence models by first assessing the match between characteristics of widely-deployed production software systems and model structures. We then evaluate how well the models fit real world data. We find that the Weibull model is flexible enough to capture defect-occurrence behavior across a wide range of systems. It provides the best model fit in 16 out of the 22 releases. We then evaluate the ability of the moving averages and the exponential smoothing methods to extrapolate Weibull model parameters using fitted model parameters from historical releases. Our results show that in 50 % of our forecasting experiments, these two naïve parameterextrapolation methods produce projections that are worse than the projection from using the same model parameters as the most recent release. These findings establish the need for further research on parameter-extrapolation methods that take into account variations in characteristics of widely-deployed, production, software systems across multiple releases.

Software cost and quality estimation has become an increasingly important field due to the increasingly pervasive role of software in today's world. In spite of the existence of about a dozen software estimation models, the field continues to remain nottoo -well-understood, causing growing concerns in the software-engineering community. In this dissertation, the existing techniques that are used for building software estimation models are discussed with a focus on the empirical calibration of the models. It is noted that traditional calibration approaches (especially the popular multipleregression approach) can have serious difficulties when used on software engineering data that is usually scarce, incomplete, and imprecisely collected. To alleviate these problems, a composite technique for building software models based on a mix of data and expert judgement is discussed. This technique is based on the well understood and widely accepted Bayes' theorem that has been successfully applie...

Abstract: Defining the project estimated cost, duration and maintenance effort early in the development life cycle is a valuable goal to be achieved for software projects. Many model structures evolved in the literature. These model structures consider modeling software effort as a function of the developed line of code (DLOC). Building such a function helps project managers to accurately allocate the available resources for the project. In this study, we present two new model structures to estimate the effort required for the development of software projects using Genetic Algorithms (GAs). A modified version of the famous COCOMO model provided to explore the effect of the software development adopted methodology in effort computation. The performance of the developed models were tested on NASA software project dataset [1].The developed models were able to provide a good estimation capabilities. Key words: COCOMO model, NASA software, genetic algorithms, genetic programming technique

In this paper we consider empirical evidence in support of a set of object-oriented software metrics. In particular, we look at the object oriented design metrics of Chidamber and Kemerer, and their applicability in different application domains. We briefly describe the metrics, and present our empirical findings, arising from our analysis of systems taken from a number of different application domains. Our investigations have led us to conclude that a subset of the metrics can be of great value to software developers, maintainers and project managers.

Since its publication, Lave and Wenger’s concept of legitimate peripheral participation (LPP) [18] has become an important concept for understanding situated learning. LPP states that learning only occurs when students perceive that what’s being taught is aligned with their goals (in LPP terms, with the students ’ perceived community of practice). This has implications for our traditional CS courses (e.g., are we teaching what the students perceive as being relevant for their future careers?), but even greater implications for courses for non-CS majors. When computer science educators are asked to teach non-CS majors, we are often placed in the position of teaching in alignment with a community of practice that does not, or does not yet, exist. In that sense, our teaching is inauthentic—not aligned with a community of practice. However, there is the possibility that we can generate a perception of authenticity or alignment. We use the example of two classes at Georgia Tech that seem successful by several measures, yet suffer this inauthenticity. We propose that a useful tool for understanding how these classes work is the Disney Corporation’s Imagineering—their process of storytelling in three-dimensions as used in their theme parks. However, in the end, we find that what students actually learn is not necessarily the story that we are telling them, which points toward future research.