Software cost estimation

The paper gives an overview of the state of the art of software cost estimation (SCE). The main questions to be answered in the paper are: (1) What are the reasons for overruns of budgets and planned durations? (2) What are the prerequisites for estimating? (3) How can software development effort be estimated? (4) What can software project management expect from SCE models, how accurate are estimations which are made using these kind of models, and what are the pros and cons of cost estimation models

The consistency of empirical comparisons of regression and analogy-based software project cost prediction

OBJECTIVE - to determine the consistency within and between results in empirical studies of software engineering cost estimation. We focus on regression and analogy techniques as these are commonly used. METHOD – we conducted an exhaustive search using predefined inclusion and exclusion criteria and identified 67 journal papers and 104 conference papers. From this sample we identified 11 journal papers and 9 conference papers that used both methods. RESULTS – our analysis found that about 25% of studies were internally inconclusive. We also found that there is approximately equal evidence in favour of, and against analogy-based methods. CONCLUSIONS – we confirm the lack of consistency in the findings and argue that this inconsistent pattern from 20 different studies comparing regression and analogy is somewhat disturbing. It suggests that we need to ask more detailed questions than just: “What is the best prediction system?

Software project economics: A roadmap

The objective of this paper is to consider research progress in the field of software project economics with a view to identifying important challenges and promising research directions. I argue that this is an important sub-discipline since this will underpin any cost-benefit analysis used to justify the resourcing, or otherwise, of a software project. To accomplish this I conducted a bibliometric analysis of peer reviewed research articles to identify major areas of activity. My results indicate that the primary goal of more accurate cost prediction systems remains largely unachieved. However, there are a number of new and promising avenues of research including: how we can combine results from primary studies, integration of multiple predictions and applying greater emphasis upon the human aspects of prediction tasks. I conclude that the field is likely to remain very challenging due to the people-centric nature of software engineering, since it is in essence a design task. Nevertheless the need for good economic models will grow rather than diminish as software becomes increasingly ubiquitous

An Empirical Evaluation of Effort Prediction Models Based on Functional Size Measures

Software development effort estimation is among the most interesting issues for project managers, since reliable estimates are at the base of good planning and project control. Several different techniques have been proposed for effort estimation, and practitioners need evidence, based on which they can choose accurate estimation methods. The work reported here aims at evaluating the accuracy of software development effort estimates that can be obtained via popular techniques, such as those using regression models and those based on analogy. The functional size and the development effort of twenty software development projects were measured, and the resulting dataset was used to derive effort estimation models and evaluate their accuracy. Our data analysis shows that estimation based on the closest analogues provides better results for most models, but very bad estimates in a few cases. To mitigate this behavior, the correction of regression toward the mean proved effective. According to the results of our analysis, it is advisable that regression to the mean correction is used when the estimates are based on closest analogues. Once corrected, the accuracy of analogy-based estimation is not substantially different from the accuracy of regression based models

A comparison of case-based reasoning and regression analysis approaches for cost uncertainty modeling

This thesis presents case-based reasoning approach for estimating the cost and modeling cost uncertainty of a new product in the concept selection stage. Case-based reasoning (CBR) is an approach which uses old cases/experiences to understand and solve new problems. The CBR approach consists of creating a knowledge-base (or database) containing past cases (products), defining a new case (concept), retrieving cases similar to the new case, and adjusting the solution of the retrieved cases to the new case. The first paper compares case-based reasoning, in studying the effects of varying design attribute specifications on cost estimation accuracy and cost distribution reliability. Case-based reasoning with cost estimation is compared with three methods: analogy-based cost estimation, case-based reasoning without cost adjustment, and regression analysis. Four automobile concepts with similar performance attribute specifications but varying design attribute specifications are defined and the comparison is made using leave-one-out cross-validation technique to a knowledge-base of 345 automobiles. The second paper further establishes case-based reasoning with cost adjustment by studying the optimum number of design attributes for specifying a concept. The results show that case-based reasoning and with cost adjustment performed best for cost estimation accuracy and cost distribution reliability when one design attribute is specified for the concept in addition to performance attributes --Abstract, page iv