451 Cost and Effort Estimation • • • • • • • • Software project managers are responsible for controlling project budgets so, they must be able to make estimates of how much a software development is going to cost. The principal components of project costs are: • Hardware costs.

• Travel and training costs. • Effort costs (the costs of paying software engineers).The dominant cost is the effort cost. This is the most difficult to estimate and control, and has the most significant effect on overall costs. Software costing should be carried out objectively with the aim of accurately predicting the cost to the contractor of developing the software. Software cost estimation is a continuing activity which starts at the proposal stage and continues throughout the lifetime of a project.

IJETI International Journal of Engineering & Technology Innovations, Vol. Software engineering cost (and schedule) models and estimation. 2.0, the newest version of their software solution for forecasting time, effort and costs for commercial and nonmilitary government software projects.

Projects normally have a budget, and continual cost estimation is necessary to ensure that spending is in line with the budget. Effort can be measured in staff-hours or staff-months (Used to be known as man-hours or man-months).

Boehm (1981) discusses seven techniques of software cost estimation: (1) Algorithmic cost modeling A model is developed using historical cost information which relates some software metric (usually its size) to the project cost. An estimate is made of that metric and the model predicts the effort required. (2) Expert judgement One or more experts on the software development techniques to be used and on the application domain are consulted.

They each estimate the project cost and the final cost estimate is arrived at by consensus. (3) Estimation by analogy This technique is applicable when other projects in the same application domain have been completed. The cost of a new project is estimated by analogy with these completed projects. Twinkle Brothers Discography Download there. (4) Parkinson's Law Parkinson's Law states that work expands to fill the time available.

In software costing, this means that the cost is determined by available resources rather than by objective assessment. If the software has to be delivered in 12 months and 5 people are available, the effort required is estimated to be 60 person-months. (5) Pricing to win The software cost is estimated to be whatever the customer has available to spend on the project. The estimated effort depends on the customer's budget and not on the software functionality.

(6) Top- down estimation A cost estimate is established by considering the overall functionality of the product and how that functionality is provided by interacting sub-functions. Cost estimates are made on the basis of the logical function rather than the components implementing that function. (7) Bottom- up estimation The cost of each component is estimated. All these costs are added to produce a final cost estimate. Each technique has advantages and disadvantages.

For large projects, several cost estimation techniques should be used in parallel and their results compared. If these predict radically different costs, more information should be sought and the costing process repeated. The process should continue until the estimates converge. Cost models are based on the fact that a firm set of requirements has been drawn up and costing is carried out using these requirements as a basis. However, sometimes the requirements may be changed so that a fixed cost is not exceeded. Costs are analyzed using mathematical formulae linking costs with metrics. The most commonly used metric for cost estimation is the number of lines of source code in the finished system (which of course is not known).

Size estimation may involve estimation by analogy with other projects, estimation by ranking the sizes of system components and using a known reference component to estimate the component size or may simply be a question of engineering judgement. Code size estimates are uncertain because they depend on hardware and software choices, use of a commercial database management system etc. An alternative to using code size as the estimated product attribute is the use of `function- points', which are related to the functionality of the software rather than to its size. Function points are computed by counting the following software characteristics: • External inputs and outputs. • User interactions.

• External interfaces. • Files used by the system.Each of these is then individually assessed for complexity and given a weighting value which varies from 3 (for simple external inputs) to 15 (for complex internal files). The function point count is computed by multiplying each raw count by the estimated weight and summing all values, then multiplied by the project complexity factors which consider the overall complexity of the project according to a range of factors such as the degree of distributed processing, the amount of reuse, the performance, and so on. Function point counts can be used in conjunction with lines of code estimation techniques. The number of function points is used to estimate the final code size.

Based on historical data analysis, the average number of lines of code in a particular language required to implement a function point can be estimated (AVC). The estimated code size for a new application is computed as follows: Code size = AVC x Number of function points The advantage of this approach is that the number of function points can often be estimated from the requirements specification so an early code size prediction can be made. The Rayleigh-Putnam Curve Halstead's Effort Model Uses a negative exponential curve as an indicator of cumulative staff-power distribution over time during a project. Technology constant, C, combines the effect of using tools, languages, methodology, quality assurance procedures. Standards etc. It is determined on the basis of historical data (past projects).

C is determined from project size, area under effort curve, and project duration. Rating: C = 2000 -- poor, C = 8000 -- good, C = 11000 it is excellent. Effort and productivity change when development time varies between 2 and 3 years: Most widely used model for effort and cost estimation. Considers a wide variety of factors. Projects fall into three categories: organic, semidetached, and embedded, characterized by their size. In the basic model which uses only source size: There is also an intermediate model which, as well as size, uses 15 other cost drivers. Cost Drivers for the COCOMO Model.

• Software reliability • Size of application database • Complexity • Analyst capability • Software engineering capability • Applications experience • Virtual machine experience • Programming language expertise • Performance requirements • Memory constraints • Volatility of virtual machine • Environment • Turnaround time • Use of software tools • Application of software engineering methods • Required development schedule • Values are assigned by the manager. The intermediate model is more accurate than the basic model.

Automated estimation tools allow the planner to estimate cost and effort and to perform 'what if' analyses for important project variables such as delivery date or staffing. All have the same general characteristics and require: • A quantitative estimate of project size (e.g., LOC) or functionality (function point data) • Qualitative project characteristics such as complexity, required reliability, or business criticality • Some description of the development staff and/or development environmentFrom these data, the model implemented by the automated estimation tool provides estimates of the effort required to complete the project, costs, staff loading, and, in some cases, development schedule and associated risk. BYL (Before You Leap) developed by the Gordon Group, WICOMO (Wang Institute Cost Model) developed at the Wang Institute, and DECPlan developed by Digital Equipment Corporation are automated estimation tools that are based on COCOMO.

Each of the tools requires the user to provide preliminary LOC estimates. These estimates are categorized by programming language and type (i.e., adapted code, reused code, new code). The user also specifies values for the cost driver attributes. Each of the tools produces estimated elapsed project duration (in months), effort in staff-months, average staffing per month, average productivity in LOC/pm, and cost per month. This data can be developed for each phase in the software engineering process individually or for the entire project. SLIM is an automated costing system based on the Rayleigh-Putnam Model.

SLIM applies the Putnam software model, linear programming, statistical simulation, and program evaluation and review technique, or PERT (a scheduling method) techniques to derive software project estimates. The system enables a software planner to perform the following functions in an interactive session: (1) calibrate the local software development environment by interpreting historical data supplied by the planner; (2) create an information model of the software to be developed by eliciting basic software characteristics, personal attributes, and environmental considerations; and (3) conduct software sizing--the approach used in SLIM is a more sophisticated, automated version of the LOC costing technique. Once software size (i.e., LOC for each software function) has been established, SLIM computes size deviation (an indication of estimation uncertainty), a sensitivity profile that indicates potential deviation of cost and effort, and a consistency check with data collected for software systems of similar size. The planner can invoke a linear programming analysis that considers development constraints on both cost and effort, and provides a month-by-month distribution of effort, and a consistency check with data collected for software systems of similar size. ESTIMACS is a 'macro- estimation model' that uses a function point estimation method enhanced to accommodate a variety of project and personnel factors.

The ESTIMACS tool contains a set of models that enable the planner to estimate • system development effort, • staff and cost, • hardware configuration, • risk, • the effects of 'development portfolio.' The system development effort model combines data about the user, the developer, the project geography (i.e., the proximity of developer and customer), and the number of 'major business functions' to be implemented with information domain data required for function point computation, the application complexity, performance, and reliability. Reshade Image Enlarger Serial Keygen - Torrent. ESTIMACS can develop staffing and costs using a life cycle data base to provide work distribution and deployment information.

The target hardware configuration is sized (i.e., processor power and storage capacity are estimated) using answers to a series of questions that help the planner evaluate transaction volume, windows of application, and other data. The level of risk associated with the successful implementation of the proposed system is determined based on responses to a questionnaire that examines project factors such as size, structure, and technology. SPQR/20, developed by Software Productivity Research, Inc.

In this research we reconcile two opposing views regarding the presence of economies or diseconomies of scale in new software development Our general approach hypothesizes a production function model of software development that allows for both increasing and decreasing returns to scale, and argues that local scale economies or diseconomies depend upon the size of projects. Using eight different data sets, including several reported in previous research on the subject.

We provide empirical evidence in support of our hypothesis. Through use of the nonparametric DBA technique we also show how to identify the most productive scale size that may vary across organizations. These results are extended to include the effects of nonparametric scale-related factors, such as project duration and the number of new staff on the project team. Prediction of software defects works well within projects as long as there is a sufficient amount of data available to train any models. However, this is rarely the case for new software projects and for many companies. So far, only a few have studies focused on transferring prediction models from one project to another. In this paper, we study cross-project defect prediction models on a large scale.

For 12 real-world applications, we ran 622 cross-project predictions. Our results indicate that cross-project prediction is a serious challenge, i.e., simply using models from projects in the same domain or with the same process does not lead to accurate predictions. To help software engineers choose models wisely, we identified factors that do influence the success of cross-project predictions.

We also derived decision trees that can provide early estimates for precision, recall, and accuracy before a prediction is attempted. Categories and Subject Descriptors. D.2.8 [Software Engineering]: Metrics—Performance measures, Process metrics, Product metrics. D.2.9 [Software Engineering]: Management—Software. 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. 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. Managers are often unable to explain objectively why or when effort was misplaced during the development process.

In this paper, we present a formal technique to depict the expended effort during the life-cycle of a software feature using feature development manifolds (FDMs). Using the FDMs we can compute the preferred development path for a given feature. This development path includes the versions of a software feature that contributed to the final version of the feature in a positive way. The preferred development path excludes versions of the software feature that should have been skipped.

Once the preferred development path is computed the amount of wasted effort can be quantified using the metric that we have developed. We demonstrate the effectiveness of our approach to compute wasted software feature development by applying our technique to two large open source software systems, Gaim and Firefox. This paper provides a historical account of the development of the field of parametrics through information obtained during interviews of twelve pioneers of the field. Cost model developers, users, and practitioners were interviewed with the intent to capture their views on the impact between cost estimation research and practice. The individuals interviewed represent a diverse range of perspectives including academia, government, and industry. Each perspective sheds light on the areas in which the field of parametrics has had an impact and which synergies have been influential in the development of the field.

The implications of the findings are discussed in light of the future challenges for the field of parametrics.