Software Cost Estimation

Paper presented and discussed at the INSEAD Workshop, Fontainebleau, September 24, 2001
Contents:
Estimation of IT Projects
object,
timing,
accuracy,
effort,
methods,
monitoring & tracking,
exprience,
installing,
culture.
Function Point Analysis
Checkpoint for Windows
Function Point Prognosis

Analogy Based Software Estimation is based on the principle that actual values achieved within the organization in an earlier and similar project are better indicators and predict the future project performance much better than an estimate developed afresh from scratch. It also facilitates bringing the organizational experience to bear on the new projects.

The accurate prediction of software development costs may have a large economic impact. As a consequence, considerable research attention is now directed to understand better the software development process. The objective of this paper is to provide an experimental evaluation of the applicability, universality and accuracy of some algorithmic software cost estimating models (COCOMO, TUCOMO, PUTNAM, COPMO, ESSE and Function Points). Data on nine Italian MIS (Management Information Systems) projects were collected and used to evaluate the performance of the models. The evaluation of the estimates was based on the Mean Magnitude Relative Error (MRE) and Prediction at level 25% (PRED(25%)) criteria. Results indicated that the models provided interesting performances, better ˝if recalibrated with local data.

This work addresses the issue of software effort prediction via fuzzy decision trees generated using historical project data samples. Moreover, the effect that various numerical and nominal project characteristics used as predictors have on software development effort is investigated utilizing the classification rules extracted. The approach attempts to classify successfully past project data into homogeneous clusters to provide accurate and reliable cost estimates within each cluster. CHAID and CART algorithms are applied on approximately 1000 project cost data records which were analyzed, preprocessed and used for generating fuzzy decision tree instances, followed by an evaluation method assessing prediction accuracy achieved by the classification rules produced. Even though the experimentation follows a heuristic approach, the trees built were found to fit the data properly, while the predicted effort values approximate well the actual effort.

Software estimation is imperative in software engineering. Many projects fail because of inaccurate size or effort estimation. The most critical and crucial part of software estimation is when estimation is required in the early stages of the software life cycle where the problem to be solved has not yet been completely revealed. Early software size estimation helps projects to be managed efficiently and helps managers estimate the effort, schedule and cost of the project. This in turn, allows the managers to bid effectively on software projects. This paper demonstrates the function points model and presents some methods used to measure the size of software in the early stages. A comparison between these methods highlights the strengths and weaknesses of each method. After displaying software size estimation methods, some methods will be presented to demonstrate how the effort of software can be estimated. Some techniques based on Artificial Intelligence are presented to illustrate how the accuracy of estimation can be improved.

More resources are spent on maintaining software than for its development. Maintenance costs for large scale software systems can amount to somewhere between 40 and 67% of the total system life cycle cost. It is therefore important to manage maintenance costs, and to balance costs with benefits. Frequently this task is approached, at least in the literature, merely as a software cost estimation problem. Unfortunately, the creation of effort estimation models for maintenance -a primary requisite for cost calculation -has not yet been satisfactorily addressed. At the same time, project managers do not estimate costs first, but instead prioritize maintenance projects, trying to determine which projects to carry out (first) within their fixed budgets and resource capabilities. This essentially means that "cost estimation" is done qualitatively first before formal cost estimation techniques are employed. Recognizing the problems associated with standard, regression based estimation models, and focusing on the needs of software project managers, this research studied the process of project prioritization as an expert problem solving and decision making task, through concurrently taken ("think aloud") protocols. Analysis of these protocols revealed that experts rarely make use of formal mathematical models to determine project priorities or resource needs, such as COCOMO or FPA, although project size is a key determinant of a project's priority. Instead, estimators qualitatively consider cost or value, urgency, and difficulty of a maintenance task, then prioritize projects accordingly, followed by a decision concerning further treatment of the problem. The process employs case based reasoning and the use of heuristics. While different experts may use different strategies, there exists great overlap in their overall prioritization procedure.

As software cost estimation in software projects is a very difficult, confusing and challenging work for any software company and software cost estimation is the primary step to start any software project. It gives the overview of resources, efforts and time/schedule required for a software project in respect of cost to the software company. Software project success generally depends on software cost estimation as it provides us an initial idea of the track, challenges and risk involved in the software project development. The software cost estimation in software engineering is very challenging to match the actual cost of the software project with estimated cost. Effective software cost estimation can help software company make more consistent decisions in planning the software project risk. If the predicted estimates are wrong it may lead to negative results for a software company. Many software companies find, search and analyze software project performance by estimating software cost estimation accuracy. Unfortunately, regardless of the large body of experienced and skilled with estimation models, the accuracy of these models is not adequate. In this research paper observation on the performance of the software cost estimation methods and description of methodologies and technique used in to software project cost estimation included. This research paper give comparative comparison study of software cost estimation methods and reviews several classes of software cost estimation models and techniques. Also study the pros and cons of different software cost estimation modeling techniques.

Keynote lecture at the: 1st International Software Measuremen & Analysis (ISMA) Conference, Sponsored by IFPUG, September 10-15, 2006 San Diego, California, USA

7. Abstract
The presented Function Point Prognosis was performed three times: with 20, 39 and 78 application system counts, as number of counts increased during the years. It was possible to show from error calculations that the regression formulae delivered Function Points with 13 % error by only counting the EI’s and EO’s. Since we fortunately have an almost uniform development environment with all application systems being counted (both being prerequisites for this result) and thus knowing all our interfaces, we can do this Function Point Prognosis before or at project start with marginal effort.

This Function Points are used for effort estimation with an estimation tool (Capers Jones’ Checkpoint for Windows). Since this tool allowed only to estimate and view single projects or their comparison with other projects, templates or portfolios of projects within this tool database, there was a lack of overview. Thus the here presented enhancement was developed, showing the overview of the estimated projects for certain project categories like e.g. new development, enhancement, maintenance, project post mortem, and template, each for different platforms like e.g. host, PC, C/S, etc.

Content
THE CHALLENGE OF EARLY ESTIMATION 1
GETTING STARTED 2
DEVELOPMENT ENVIRONMENT 2
IT PROJECT ESTIMATION BASICS 3
EXPERT ESTIMATIONS 3
POLITICAL ESTIMATIONS 4
ESTIMATION CONFERENCES 4
CONTROLLING AND ESTIMATION 5
THE MOST IMPORTANT THEMES OF  ESTIMATION 5
FUNCTION POINT PROGNOSIS 6
FUNCTION POINT DATABASE 6
INDICATORS 7
Value adjustment factor 7
Function component proportions 7
REGRESSION ANALYSIS 8
PROGNOSIS FORMULAE 8
TOOL BASED ESTIMATION 8
ESTIMATION TOOL 8
ESTIMATION PORTFOLIO AND TEMPLATES 8
BENEFITS OF EARLY FUNCTION POINT PROGNOSIS FOR EARLY PROJECT ESTIMATION 8
BENEFITS OF THE FPM 8
LITERATURE 10

Process of estimating the effort required for the software development process is difficult and very complicated. There are many techniques to estimate the effort and cost of producing software, this article is based on a correct reading and mathematical analysis of the software problem, extract the variables needed as input and output for the required software, and applying the Software Science Metrics on the variables and their relationship to estimate unique and used operators and operators. Finally depending on these metric calculate the size and the time and effort ... etc. expected to produce software.

Der Nutzen der Function Point Methode für Aufwandschätzung und Softwareentwicklung –Praktische Erfahrungen
Artikel für die GPM Tagung 2005
Der Nutzen der Function Point Methode für
Aufwandschätzung und Softwareentwicklung –Praktische
Erfahrungen
MANFRED BUNDSCHUH
Der Nutzen der Function Point Methode für Aufwandschätzung und Softwareentwicklung –
Praktische Erfahrungen................................................................................................................1
Zusammenfassung.......................................................................................................................1
Herausforderungen der Aufwandschätzung.......................................................................................................................2
Die Function Point Methode als Voraussetzung für solide Aufwandschätzung.........................2
Function Points auf einen Blick..................................................................................................3
Der Schritt vom Messen zum Schätzen...................................................................................3
Umfangmaße nach ISO Norm...................................................................................................4
COSMIC Full Function Points (FFP)..........................................................................................5
Praktische Erfahrungen und Synergieeffekte beim Einsatz der Function Point Methode......5
Die Aufwandschätzung als Voraussetzung für solide Projektplanung........................................6
Kritische Erfolgsfaktoren der Aufwandschätzung..................................................................6
Schätzgenauigkeit und Schätzzeitpunkte................................................................................6
Frühe Schätzungen......................................................................................................................7
Akzeptanz......................................................................................................................................7
Aufwandschätzung in speziellen Umgebungen.....................................................................8
Objektorientierte Metriken..........................................................................................................8
Function Point Zählung von Datawarehouse Projekten.......................................................8
Aufwandschätzung von Web-Entwicklungen.........................................................................9
Aufwandschätzung von Wartungsaufträgen/Changes........................................................9
Professionelle Hilfestellung durch Metrikorganisationen..................................................................................................................9
DASMA........................................................................................................................................10
GI FG 2.1.10 Software-Messung und –Bewertung.............................................................10
IFPUG..........................................................................................................................................10
ISBSG..........................................................................................................................................11
MAIN...........................................................................................................................................11
Literaturverzeichnis..................................................................................................................11
Schlagwörter..............................................................................................................................11
Autor............................................................................................................................................12
Anschrift.....................................................................................................................................12

Agile has invented to improve and overcome Draw backs of software development. Now agile model is using in software development very vastly. It is facilitating the developer and client both very resourcefully. It is getting popularity than the other Software Development Life Cycle models because of its characteristics and most owing to allow change request at any level of the project. The client in the software development is the main part and asset of the company. The software house always focuses on its client because client is an asset. Thus developer has major concern with the client's requirement and change request. Agile is getting popularity because of allow change request at any stage of project, on the other hand it is also a drawback in agile model because when project starts the project's completion time and cost is decided. But due to frequent change of requests come from client the cost and completion time both increase eventually which is not good for software house's business and reputation. So there is need a cost and time estimation technique to solve change request issue in agile model.

Los Puntos de Objetos (Object Points), no tienen relación con la programación orientada a objetos sino que se refieren a objetos tales como pantallas, informes y módulos de programas. Se cuenta la cantidad de cada objeto, se los clasifica como de complejidad baja, media o alta y se asigna una ponderación a cada tipo de objeto según su complejidad.

In current world multiple organizations are using the methodology of Global Software Development (GSD) because it is a convenient technique for developing the software projects. Global software advancement serves as a vowed relation among the client and vendor companies. In such relation the client contracts out a part or complete
development of the software to the vendor. As a response the vendor provides the services on which the agreement was made in lieu of the payment is made to the vendor as per agreed. GSD method is used because it offers the completion of project in less cost, quick development and the access to experienced resources. Even though GSD is coming out as a successful method but it has multiple difficulties which needs to be overcome such as bad communication, absence of faith and coordination among the team. Due to these challenges there is a significant risk factor in trouble free execution. Our results show that most of the focus in previous research in this area has been limited to highlighting the problems encountered by the client companies. The vendor side up to this time is not very mature because this aspect of GSD is not payed much attention.

Industrial reports show massive cost overruns associated with software projects. The cost of software reworks constitutes a large portion of the overall cost, reflecting a substantial challenge in cost control. Earned value management (EVM) is the most recognized model for project cost control. However, it shows many limitations in forecasting the software project cost, leading to a considerable challenge in cost control. Nevertheless, the major EVM limitation found is its inability to forecast the cost of software rework. This research investigated the factors affecting this limitation and suggests an enhanced EVM model. The significant contribution of this research is its incorporation of software-related factors into the EVM model. We introduced the software rework index (SRI), which is incorporated into the traditional EVM model to enhance its predictability of the software project cost at completion, including the rework cost. We defined the SRI in terms of two factors: product...

A  simple  UV-Visible  spectrophotometric  method  has  been
developed for the determination of Atorvastatin in its pure form as
well as pharmaceutical dosage form using Methyl Orange reagent.
The  method  is  based  on  the  measurement  of  absorbance  of
Atorvastatin in methanol at 410 nm. The Beer’s law is obeyed
over  the  linear  range  50-300μg  /ml  of  Atorvastatin.  All  the
variables were studied to optimize the reaction conditions. No
interference  was  observed  in  the  presence  of  common
pharmaceutical excipients. The validity of the method was tested
by analyzing the drug in its pharmaceutical preparations. Good
recoveries were also obtained. Assay for the tablet preparation was
performed using UV-Visible spectrophotometric method and the
results were found to be within acceptable limits.

an intense, potentially lethal microwave field. A companion paper (Justesen et al., VA Medical Center, Kansas City) suggests that thresholds for perception of microwave and infrared energy are nevertheless comparable, provided that the larger fraction of reflected power, and the deeper and more diffuse heating by the absorbed microwave energy, are properly taken into account.

Mobile Application are new emerging technology dominating the software engineering platform, this new technology comes with new features, restrictions and new possibilities which does not exist before. Building capable software for this new environment comes with new requirement, constrains and characteristics that are not used in the previous traditional estimation method. In this paper we analyses the identified characteristics that directly interfere with mobile application development. We also analyzed the most common current estimation method that has been used to measure mobile application effort and size and we propose Objective for future estimation method that should be able to measure mobile application development effort and sizing measurement.

The growing application of software and resource constraints in software projects development need a
more accurate estimate of the cost and effort because of the importance in program planning, coordinated
scheduling and resource management including the number of programming's and software design using
tools and modern methods of modeling. Effectively control of investment for software development is
achieved by accurate cost estimation.The accurate Software Cost Estimation (SCE) is very difficult in the
early stages of software development because many of input parameters that are effective in software's
effort are very vague and uncertain in the early stages. SCE that is the basis of software projects
development planning is considered to be of high accuracy, because if the estimate is less than actual
values, confidence factor is reduce and this is means the possibility of failure in project. Conversely, if the
project is estimated at more than the actual value it would be the concept of unhelpful investment and
waste of resources. In the evaluation of software projects is commonly used deterministic method. But
software world is totally different from the linear variables and nowadays for performance and estimation
should be used nonlinear and non-probabilistic methods. In this paper, we have studied the SCE Using
Fuzzy Logic (FL) and we have compared it with COCOMO model. Results of investigations show that FL is
a performance model for SCE.

Software cost estimation is one of the most challenging tasks in software engineering. Over the past years the estimators have used parametric cost estimation models to establish software cost, however the challenges to accurate cost estimation keep evolving with the advancing technology. A detailed review of various cost estimation methods developed so far is presented in this paper. Planned effort and actual effort has been comparison in detail through applying on NASA projects. This paper uses Back-Propagation neural networks for software cost estimation. A model based on Neural Network has been proposed that takes KLOC of the project as input, uses COCOMO model parameters and gives effort as output. Artificial Neural Network represents a complex set of relationship between the effort and the cost drivers and is a potential tool for estimation. The proposed model automates the software cost estimation task and helps project manager to provide fast and realistic estimate for the project effort and development time that in turn gives software cost.

This Article shows a Case Study research that is performed to compare effectiveness of the Planning Poker and Wideband Delphi in two case studies. Although, Wide Band Delphi has been proposed many years ago and has enough supports in both industry and academic area but, Planning Poker is somewhat new. The study showed that applying these techniques in two companies which were using expert’s view for software cost estimation, increased the accuracy of cost estimation. Also, the results showed that Planning Poker led to better accuracy comparing to Wideband Delphi. However, this accuracy is not too much than Wideband Delphi, it was important that Planning Poker led to reducing financial risks of the projects comparing to Wideband Delphi. The study also showed that both Wideband Delphi and Planning Poker reduced the underestimates significantly.

Los jefes de proyecto son los responsables de la estimación y gestión de los presupuestos del proyecto. No realizar una estimación o una mala estimación del esfuerzo de desarrollo son causa de muchos fracasos en proyectos de desarrollo software.

Software project management is the utilization of resources in efficient manner to complete a project within time and budget. It is like a corner stone in every successful project. Sound software project management techniques are very important for the development of software projects both for organizations that develop outsourced projects and in-house projects. The data was collected through interviews and questionnaire. Most of the respondents were project managers having postgraduate degrees. From analysis it is concluded that project management practices play important role in the successful completion of projects. Sixteen percent of projects had no project plan. Many standard software practices are not being applied constantly in the projects. The risk assessment and mitigation are not carried out until the risk occurs. The role of software project management tools and techniques and their practices are also analyzed in detail in this paper. Many believed that decision making is the most important ability of project managers. In this paper we have described different factors that cause the success or failure of projects. Our results present general guidance for project managers to make sure that their projects be successful. This study shows the importance of project management tools and techniques in the industry. It also shows how project management is important for a successful and quality software product. If a project is managed properly, we get a quality product within allocated time and budget.

a transparency is some aspect of the distributed database system that is hidden from the user (programmer, system developer, and application program). A transparency is provided by including some set of mechanisms in the distributed system at a layer interface where the transparency is required. A number of basic transparencies have been defined for a distributed system. This paper present an overview of different types of transparency such as (Access, Concurrency, Location, Replication, Failure, Migration, Performance, Scaling, Execution, Configuration, Network, and Naming), and three type of levels transparency which is (Fragmentation, Location, and Local mapping). It’s important to realize that not all of these are appropriate for every system, or are available at the same level of interface. In this paper, if all transparency have an associated cost with others, matter for much research how the cost of implementing multiple transparencies interact and how to reduce operating system and communication stack. It considered that if using such type of approaches such as: Application Layer Framing and Integrated Layer Processing

Part of each feasibility study of a food production plant is to make an estimation of the “future” investment  costs in such a plant. In this document, the investment cost estimation method of Zevnik-Buchanan is explained, in 10 consecutive steps. The method, requiring a detailed plant flowsheet, can be executed manually by using graphs. However, calculation is more rapid and much simpler by using the Zevnik-Buchanan Excel worksheet, included in this document. For this worksheet, the Z-B graphs had to be converted into mathematical equations. These equations can be found in the corresponding graphs.
2 worked examples explain the basics of Zevnik-Buchanan's estimation method of plant cost investments: one about a 10 000 tons/year mozzarella factory, the other on a 1150 tons/year whole egg powder plant.
Calculations using the Zevnik-Buchanan graphs and equations are valid for the year 1996. To convert these 1996 figures to future estimated plant costs, the Chemical Engineering Plant Cost Indices (CE Plant Cost Index), from 1980 onward, have been included in Annex 2: both as a graph for easy extrapolation, and as a table.
For reference purposes, the 1986 Bartholomai investment costs of some 40 different food factories are listed in Annex 3.

There are several software estimation models such as Line of Code, Function Point and COnstructive COst MOdel (COCOMO). The original COCOMO model is one of the most widely practiced and popular among the software development community because of its flexible usage. It is a suite of models i.e., COnstructive COst MOdel I and COnstructive COst MOdel II. In this paper, we are evaluating the both models, to find out the level of efficiency they present and how they can be tailored to the needs of modern software development projects. We are applying COCOMO models on a case study of an E-Commerce application that is built using Hyper Text Markup Language (HTML) and JavaScript. We will also shed light on the different components of each model, and how their Cost Drivers effect on the accuracy of cost estimations for software development projects.

As the world is progressing towards faster and more efficient computing techniques, cloud computing has emerged as an efficient and cheaper solution to such increasing and demanding requirements. Cloud computing is a computing model which facilitates not only the end-users but also organizational and other enterprise users with high availability of resources on demand basis. This involves the use of scientific workflows that require large amount of data processing, which can be costly and time-consuming if not properly scheduled in cloud environment. Various scheduling strategies have been developed, which include swarm-based optimization approaches as well. Due to the presence of multiple and conflicting requirements of users, multi-objective optimization techniques have become popular for workflow scheduling. This paper deals with Cat-swarm based multi-objective optimization approach to schedule workflows in a cloud computing environment. The objectives considered are minimization of cost, makespan and CPU idle time. Proposed technique gives improved performance, compared to Multi-Objective Particle Swarm Optimization (MOPSO) technique.

Current cost estimation techniques have a number of drawbacks. For example, developing algorithmic models requires extensive past project data. Also, off-the-shelf models have been found to be difficult to calibrate but inaccurate without calibration. Informal approaches based on experienced estimators depend on estimators' availability and are not easily repeatable, as well as not being much more accurate than algorithmic techniques. In this paper we present a method for cost estimation that combines aspects of algorithmic and experiential approaches (referred to as COBRA, COst estimation, Benchmarking, and Risk Assessment). We find through a case study that cost estimates using COBRA show an average ARE of 0.09, and show that the results are easily usable for benchmarking and risk assessment purposes.

""Part of each feasibility study of a food production plant is to make an estimation of the “future” investment costs in such a plant. In this document, three rapid investment cost estimation methods are explained: 1) the indexed 0.6 exponent method, in case your plant is “similar” to one of the 40 food plants, listed by Bartholomai; 2) the adapted Marouli method for complete food plants, if you only know the total production capacity of your plant, and 3) the adapted Marouli method for separate plant production sections, to be used if you have a plant flowsheet and you know the capacity of each of the main production sections of the food plant. Each of these 3 estimation methods can be executed manually, but calculation is rapid and simpler by using the 3 Excel worksheets, included in this document. Several worked examples explain the mathematics behind each of the 3 cost estimation methods; these examples should help to understand the basics of the calculations.
Usually, investment cost estimations are made for plants to be build in the near future. but are based on investment cost figures from the past, often with different production capacities. To convert past plant investment costs to future estimated plant costs, the Chemical Engineering Plant Cost Indices, from 1980 onward, are available, both as a table and as a graph. To convert plant production capacity from past to future capacity, the well-known 0.6 exponent rule is used.
Annex 1 discusses similar investment cost estimation methods for non-food plants, based on Perry’s (1985 and 1999) tables with past plant investment costs of many non-food factories.
""

This paper aims to provide a basis for the improvement of software estimation research through a systematic review of previous work. The review identifies 304 software cost estimation papers in 76 journals and classifies the papers according to research topic, estimation approach, research approach, study context and data set. A Web-based library of these cost estimation papers is provided to ease the identification of relevant estimation research results. The review results combined with other knowledge provide support for recommendations for future software cost estimation research, including 1) increase the breadth of the search for relevant studies, 2) search manually for relevant papers within a carefully selected set of journals when completeness is essential, 3) conduct more studies on estimation methods commonly used by the software industry, and 4) increase the awareness of how properties of the data sets impact the results when evaluating estimation methods.

Current software cost estimation models, such as the 1981 Constructive Cost Model (COCOMO) for software cost estimation and its 1987 Ada COCOMO update, have been experiencing increasing difficulties in estimating the costs of software developed to new life cycle processes and capabilities. These include non-sequential and rapid-development process models; reuse-driven approaches involving commercial off-the-shelf (COTS) packages, re-engineering, applications composition, and applications generation capabilities; object-oriented approaches supported by distributed middleware; and software process maturity initiatives. This paper summarizes research in deriving a baseline COCOMO 2.0 model tailored to these new forms of software development, including rationale for the model decisions. The major new modeling capabilities of COCOMO 2.0 are a tailorable family of software sizing models, involving Object Points, Function Points, and Source Lines of Code; nonlinear models for software reuse and re-engineering; an exponentdriver approach for modeling relative software diseconomies of scale; and several additions, deletions and updates to previous COCOMO effort-multiplier cost drivers. This model is serving as a framework for an extensive current data collection and analysis effort to further refine and calibrate the model's estimation capabilities.

It is very important to conduct software estimation in the early stages of the software life cycle, because it helps managers bid on projects and allocate resources efficiently. This paper presents a novel regression model to estimate the software effort based on the use case point size metric. The use case point model takes use case diagrams as input and gives the software size in use case points as output. The proposed effort equation takes into consideration the non-linear relationship between software size and software effort, as well as the influences of project complexity and productivity. Results show that the software effort estimation accuracy can be improved by 16.5% using PRED(25) and 25% using PRED(35).

Studying the quality requirements (aka Non-Functional Requirements (NFR)) of a system is crucial in Requirements Engineering. Many software projects fail because of neglecting or failing to incorporate the NFR during the software life development cycle. This paper focuses on analyzing the importance of the quality requirements attributes in software effort estimation models based on the Desharnais dataset. The Desharnais dataset is a collection of eighty one software projects of twelve attributes developed by a Canadian software house. The analysis includes studying the influence of each of the quality requirements attributes, as well as the influence of all quality requirements attributes combined when calculating software effort using regression and Artificial Neural Network (ANN) models. The evaluation criteria used in this investigation include the Mean of the Magnitude of Relative Error (MMRE), the Prediction Level (PRED), Root Mean Squared Error (RMSE), Mean Error and the Coefficient of determination (R 2 ). Results show that the quality attribute "Language" is the most statistically significant when calculating software effort. Moreover, if all quality requirements attributes are eliminated in the training stage and software effort is predicted based on software size only, the value of the error (MMRE) is doubled.

Effective Software cost estimation is one of the most challenging and important activities in Software development. The software industry does not estimate projects well. In this paper we have represented size in KLOC as a Fuzzy number. A new model is presented using fuzzy logic to estimate effort required in software development. We use MATLAB for tuning the parameters of famous COCOMO model. The performance of model is evaluated on published software projects data. Comparison of results from our model with existing prevalent models is done.

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: (I) What are the reasons for overruns of budgets and planned durations? 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? software, cost estimation, project control, software cost estimation model

Software cost estimation (SCE) has been an important but difficult task since the beginning of the computer era. It considers list of parameters to estimate accurately the software cost. However, security cost is always excluded in most of the parametric cost estimation models. It is because of the security aspect is normally considered late in the software development. To overcome this problem, we proposed an enhancement to one of the parametric estimation models, which is Function Point Analysis (FPA), to address the security concerns. The enhancement suggests potential software security characteristics during system development life cycle (SDLC). These characteristics are then integrated into FPA calculation to encounter the security cost estimation. This paper also points to the validation of a survey findings and calibration of the FPA calculation.

Software testing is a very broad area, which involves many technical and non-technical areas, such as specification, design, implementation, maintenance, process and management issues in software engineering. UCP is an estimation method that provides ability to estimate size from its use case. Researchers had proposed many size estimation techniques. This research paper shows modification of estimating interactive software project size with enhanced use case point. A critical study shows that how we can improve efficiency of original method of size estimation enhanced use-case point. Estimate interactive software project size with Enhanced use case method and stored procedure with transaction will integrated for find the result with more efficient. Calculation of use case point are not difficult main difficultly is in defining use case and actor. UCP gives the measurement of the testing software development project and also ensures complete system documentation. Firstly use case document of project is prepared on the basis of requirement collected and all the factor are determined and analyzed on the basis of their complexity to estimate size.

En general, los modelos que se utilizan para la estimación del esfuerzo (o del costo, ya que con un costo promedio del personal utilizado se puede derivar fácilmente el costo) son todos modelos empíricos que han surgido del análisis de regresión de los datos obtenidos de proyectos reales.

Software estimation is imperative in software engineering. Many projects fail because of inaccurate size or effort estimation. The most critical and crucial part of software estimation is when estimation is required in the early stages of the software life cycle where the problem to be solved has not yet been completely revealed. Early software size estimation helps projects to be managed efficiently and helps managers estimate the effort, schedule and cost of the project. This in turn, allows the managers to bid effectively on software projects. This paper demonstrates the function points model and presents some methods used to measure the size of software in the early stages. A comparison between these methods highlights the strengths and weaknesses of each method. After displaying software size estimation methods, some methods will be presented to demonstrate how the effort of software can be estimated. Some techniques based on Artificial Intelligence are presented to illustrate how the accuracy of estimation can be improved.