How much will it cost to support your software project based on current estimations? Discover the answer to this question by using statistical estimation methods-including the S-curve and the Rayleigh curve-to help you determine where your projects are in relation to required quality and trendings to meet your post-project cost goals. Learn how to use metrics to predict post-project costs and make better release decisions based on these predictions.
This paper introduces a fault model that predicts the number of errors and defects throughout the development cycle. Project managers can use this information to quantitatively determine if the development process is in control, may be going out of control, or is clearly out of control. This model is able to adjust estimates based on the most current data available.
Prediction becomes more accurate when there are measured trends to show the way. Knowing what to collect and review is only half of the process of predicting change. The rest of the methodology is understanding the data and being able to predict changes so that the project team can proactively respond to change events. Learn how organizations within EDS have begun to accurately predict changes. Explore the methods, decisions, and the necessary steps taken by EDS to develop and use metrics and measures that support key management decisions.
The best project managers know to superbly manage the subtleties of risks, employee turnover, personality clashes, shifting priorities, and other unexpected events. And they know how to motivate even mediocre employees to produce exceptional results. The biggest challenge is facing the fact that no project proceeds predictably and according to plan. Learn practical day-to-day techniques you can use to achieve extraordinary project success in spite of seemingly insurmountable setbacks.
Estimating productivity (e.g., lines of source code developed per hour) and quality (e.g., code defect rates) are difficult on large software projects that involve several companies or sites, emphasize reuse of Commercial-Off-The-Shelf (COTS) components or adaptation of legacy code, and require open architectures. Using actual metrics from such software development projects, this paper illustrates problems encountered and lessons learned when measuring productivity and quality. These include: how to count different types of code; effects of lengthy development times on productivity/quality; variability
between estimates obtained from different models; and tracking and reporting metrics on productivity/quality for projects based on incremental or evolutionary development.
Discover the techniques used by estimators to overcome the challenges they are confronted with in attempting to estimate totally new development environments in the Web/e-commerce world. Typical challenges include how to scope functionality, assess realistic developer efficiency, and tailor the lifecycle processes. Learn how to use these techniques to estimate new project environments and effectively communicate the results of your analysis. Case studies will be provided to illustrate the techniques and their practical application.